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feat: add skills system for specialized agent workflows (#6)

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Implement a skills framework that enables specialized workflows for
specific tasks (e.g., PDF processing, web page generation). Skills are
discovered from the skills/ directory and automatically mounted in
sandboxes with path mapping support.

- Add SkillsConfig for configuring skills path and container mount point
- Implement dynamic skill loading from SKILL.md files with YAML frontmatter
- Add path mapping in LocalSandbox to translate container paths to local paths
- Mount skills directory in AIO Docker sandbox containers
- Update lead agent prompt to dynamically inject available skills
- Add setup documentation and expand config.example.yaml

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
DanielWalnut
2026-01-16 14:44:51 +08:00
committed by GitHub
parent 5ef3cb57ee
commit cfa97f7a96
30 changed files with 2959 additions and 51 deletions
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44
README.md
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@@ -2,6 +2,50 @@
> Originated from Open Source, give back to Open Source.
A LangGraph-based AI agent backend with sandbox execution capabilities.
## Quick Start
1. **Configure the application**:
```bash
# Copy example configuration
cp config.example.yaml config.yaml
# Set your API keys
export OPENAI_API_KEY="your-key-here"
# or edit config.yaml directly
```
2. **Install dependencies**:
```bash
cd backend
make install
```
3. **Run development server**:
```bash
make dev
```
## Project Structure
```
deer-flow/
├── config.example.yaml # Configuration template (copy to config.yaml)
├── backend/ # Backend application
│ ├── src/ # Source code
│ └── docs/ # Documentation
├── frontend/ # Frontend application
└── skills/ # Agent skills
├── public/ # Public skills
└── custom/ # Custom skills
```
## Documentation
- [Configuration Guide](backend/docs/CONFIGURATION.md) - Setup and configuration instructions
- [Architecture Overview](backend/CLAUDE.md) - Technical architecture details
## License
This project is open source and available under the [MIT License](./LICENSE).

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backend/CLAUDE.md
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@@ -26,11 +26,20 @@ make format
### Configuration System
The app uses a YAML-based configuration system loaded from `config.yaml`. Configuration priority:
The app uses a YAML-based configuration system loaded from `config.yaml`.
**Setup**: Copy `config.example.yaml` to `config.yaml` in the **project root** directory and customize for your environment.
```bash
# From project root (deer-flow/)
cp config.example.yaml config.yaml
```
Configuration priority:
1. Explicit `config_path` argument
2. `DEER_FLOW_CONFIG_PATH` environment variable
3. `config.yaml` in current directory
4. `config.yaml` in parent directory
3. `config.yaml` in current directory (backend/)
4. `config.yaml` in parent directory (project root - **recommended location**)
Config values starting with `$` are resolved as environment variables (e.g., `$OPENAI_API_KEY`).
@@ -61,12 +70,25 @@ Config values starting with `$` are resolved as environment variables (e.g., `$O
- `resolve_variable()` imports module and returns variable (e.g., `module:variable`)
- `resolve_class()` imports and validates class against base class
**Skills System** (`src/skills/`)
- Skills provide specialized workflows for specific tasks (e.g., PDF processing, frontend design)
- Located in `deer-flow/skills/{public,custom}` directory structure
- Each skill has a `SKILL.md` file with YAML front matter (name, description, license)
- Skills are automatically discovered and loaded at runtime
- `load_skills()` scans directories and parses SKILL.md files
- Skills are injected into agent's system prompt with paths
- Path mapping system allows seamless access in both local and Docker sandbox:
- Local sandbox: `/mnt/skills` → `/path/to/deer-flow/skills`
- Docker sandbox: Automatically mounted as volume
### Config Schema
Models, tools, and sandbox providers are configured in `config.yaml`:
Models, tools, sandbox providers, and skills are configured in `config.yaml`:
- `models[]`: LLM configurations with `use` class path
- `tools[]`: Tool configurations with `use` variable path and `group`
- `sandbox.use`: Sandbox provider class path
- `skills.path`: Host path to skills directory (optional, default: `../skills`)
- `skills.container_path`: Container mount path (default: `/mnt/skills`)
## Code Style

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backend/SETUP.md Normal file
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@@ -0,0 +1,76 @@
# Setup Guide
Quick setup instructions for DeerFlow.
## Configuration Setup
DeerFlow uses a YAML configuration file that should be placed in the **project root directory**.
### Steps
1. **Navigate to project root**:
```bash
cd /path/to/deer-flow
```
2. **Copy example configuration**:
```bash
cp config.example.yaml config.yaml
```
3. **Edit configuration**:
```bash
# Option A: Set environment variables (recommended)
export OPENAI_API_KEY="your-key-here"
# Option B: Edit config.yaml directly
vim config.yaml # or your preferred editor
```
4. **Verify configuration**:
```bash
cd backend
python -c "from src.config import get_app_config; print('✓ Config loaded:', get_app_config().models[0].name)"
```
## Important Notes
- **Location**: `config.yaml` should be in `deer-flow/` (project root), not `deer-flow/backend/`
- **Git**: `config.yaml` is automatically ignored by git (contains secrets)
- **Priority**: If both `backend/config.yaml` and `../config.yaml` exist, backend version takes precedence
## Configuration File Locations
The backend searches for `config.yaml` in this order:
1. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
2. `backend/config.yaml` (current directory when running from backend/)
3. `deer-flow/config.yaml` (parent directory - **recommended location**)
**Recommended**: Place `config.yaml` in project root (`deer-flow/config.yaml`).
## Troubleshooting
### Config file not found
```bash
# Check where the backend is looking
cd deer-flow/backend
python -c "from src.config.app_config import AppConfig; print(AppConfig.resolve_config_path())"
```
If it can't find the config:
1. Ensure you've copied `config.example.yaml` to `config.yaml`
2. Verify you're in the correct directory
3. Check the file exists: `ls -la ../config.yaml`
### Permission denied
```bash
chmod 600 ../config.yaml # Protect sensitive configuration
```
## See Also
- [Configuration Guide](docs/CONFIGURATION.md) - Detailed configuration options
- [Architecture Overview](CLAUDE.md) - System architecture

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backend/docs/CONFIGURATION.md Normal file
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@@ -0,0 +1,221 @@
# Configuration Guide
This guide explains how to configure DeerFlow for your environment.
## Quick Start
1. **Copy the example configuration** (from project root):
```bash
# From project root directory (deer-flow/)
cp config.example.yaml config.yaml
```
2. **Set your API keys**:
Option A: Use environment variables (recommended):
```bash
export OPENAI_API_KEY="your-api-key-here"
export ANTHROPIC_API_KEY="your-api-key-here"
# Add other keys as needed
```
Option B: Edit `config.yaml` directly (not recommended for production):
```yaml
models:
- name: gpt-4
api_key: your-actual-api-key-here # Replace placeholder
```
3. **Start the application**:
```bash
make dev
```
## Configuration Sections
### Models
Configure the LLM models available to the agent:
```yaml
models:
- name: gpt-4 # Internal identifier
display_name: GPT-4 # Human-readable name
use: langchain_openai:ChatOpenAI # LangChain class path
model: gpt-4 # Model identifier for API
api_key: $OPENAI_API_KEY # API key (use env var)
max_tokens: 4096 # Max tokens per request
temperature: 0.7 # Sampling temperature
```
**Supported Providers**:
- OpenAI (`langchain_openai:ChatOpenAI`)
- Anthropic (`langchain_anthropic:ChatAnthropic`)
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
- Any LangChain-compatible provider
**Thinking Models**:
Some models support "thinking" mode for complex reasoning:
```yaml
models:
- name: deepseek-v3
supports_thinking: true
when_thinking_enabled:
extra_body:
thinking:
type: enabled
```
### Tool Groups
Organize tools into logical groups:
```yaml
tool_groups:
- name: web # Web browsing and search
- name: file:read # Read-only file operations
- name: file:write # Write file operations
- name: bash # Shell command execution
```
### Tools
Configure specific tools available to the agent:
```yaml
tools:
- name: web_search
group: web
use: src.community.tavily.tools:web_search_tool
max_results: 5
# api_key: $TAVILY_API_KEY # Optional
```
**Built-in Tools**:
- `web_search` - Search the web (Tavily)
- `web_fetch` - Fetch web pages (Jina AI)
- `ls` - List directory contents
- `read_file` - Read file contents
- `write_file` - Write file contents
- `str_replace` - String replacement in files
- `bash` - Execute bash commands
### Sandbox
Choose between local execution or Docker-based isolation:
**Option 1: Local Sandbox** (default, simpler setup):
```yaml
sandbox:
use: src.sandbox.local:LocalSandboxProvider
```
**Option 2: Docker Sandbox** (isolated, more secure):
```yaml
sandbox:
use: src.community.aio_sandbox:AioSandboxProvider
port: 8080
auto_start: true
container_prefix: deer-flow-sandbox
# Optional: Additional mounts
mounts:
- host_path: /path/on/host
container_path: /path/in/container
read_only: false
```
### Skills
Configure the skills directory for specialized workflows:
```yaml
skills:
# Host path (optional, default: ../skills)
path: /custom/path/to/skills
# Container mount path (default: /mnt/skills)
container_path: /mnt/skills
```
**How Skills Work**:
- Skills are stored in `deer-flow/skills/{public,custom}/`
- Each skill has a `SKILL.md` file with metadata
- Skills are automatically discovered and loaded
- Available in both local and Docker sandbox via path mapping
### Title Generation
Automatic conversation title generation:
```yaml
title:
enabled: true
max_words: 6
max_chars: 60
model_name: null # Use first model in list
```
## Environment Variables
DeerFlow supports environment variable substitution using the `$` prefix:
```yaml
models:
- api_key: $OPENAI_API_KEY # Reads from environment
```
**Common Environment Variables**:
- `OPENAI_API_KEY` - OpenAI API key
- `ANTHROPIC_API_KEY` - Anthropic API key
- `DEEPSEEK_API_KEY` - DeepSeek API key
- `TAVILY_API_KEY` - Tavily search API key
- `DEER_FLOW_CONFIG_PATH` - Custom config file path
## Configuration Location
The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
## Configuration Priority
DeerFlow searches for configuration in this order:
1. Path specified in code via `config_path` argument
2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
3. `config.yaml` in current working directory (typically `backend/` when running)
4. `config.yaml` in parent directory (project root: `deer-flow/`)
## Best Practices
1. **Place `config.yaml` in project root** - Not in `backend/` directory
2. **Never commit `config.yaml`** - It's already in `.gitignore`
3. **Use environment variables for secrets** - Don't hardcode API keys
4. **Keep `config.example.yaml` updated** - Document all new options
5. **Test configuration changes locally** - Before deploying
6. **Use Docker sandbox for production** - Better isolation and security
## Troubleshooting
### "Config file not found"
- Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
- The backend searches parent directory by default, so root location is preferred
- Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
### "Invalid API key"
- Verify environment variables are set correctly
- Check that `$` prefix is used for env var references
### "Skills not loading"
- Check that `deer-flow/skills/` directory exists
- Verify skills have valid `SKILL.md` files
- Check `skills.path` configuration if using custom path
### "Docker sandbox fails to start"
- Ensure Docker is running
- Check port 8080 (or configured port) is available
- Verify Docker image is accessible
## Examples
See `config.example.yaml` for complete examples of all configuration options.

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backend/src/agents/lead_agent/prompt.py
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@@ -1,6 +1,8 @@
from datetime import datetime
SYSTEM_PROMPT = f"""
from src.skills import load_skills
SYSTEM_PROMPT_TEMPLATE = """
<role>
You are DeerFlow 2.0, an open-source super agent.
</role>
@@ -14,19 +16,16 @@ You are DeerFlow 2.0, an open-source super agent.
You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.
**Progressive Loading Pattern:**
1. When a user query matches a skill's use case, immediately call `view` on the skill's main file located at `/mnt/skills/{"{skill_name}"}/SKILL.md`
1. When a user query matches a skill's use case, immediately call `view` on the skill's main file using the path attribute provided in the skill tag below
2. Read and understand the skill's workflow and instructions
3. The skill file contains references to external resources under the same folder
4. Load referenced resources only when needed during execution
5. Follow the skill's instructions precisely
**Skills are located at:** {skills_base_path}
<all_available_skills>
<skill name="generate-web-page">
Generate a web page or web application
</skill>
<skill name="pdf-processing">
Extract text, fill forms, merge PDFs (pypdf, pdfplumber)
</skill>
{skills_list}
</all_available_skills>
</skill_system>
@@ -64,4 +63,27 @@ All temporary work happens in `/mnt/user-data/workspace`. Final deliverables mus
def apply_prompt_template() -> str:
return SYSTEM_PROMPT + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"
# Load all available skills
skills = load_skills()
# Get skills container path from config
try:
from src.config import get_app_config
config = get_app_config()
container_base_path = config.skills.container_path
except Exception:
# Fallback to default if config fails
container_base_path = "/mnt/skills"
# Generate skills list XML with paths
skills_list = "\n".join(f'<skill name="{skill.name}" path="{skill.get_container_path(container_base_path)}">\n{skill.description}\n</skill>' for skill in skills)
# If no skills found, provide empty list
if not skills_list:
skills_list = "<!-- No skills available -->"
# Format the prompt with dynamic skills
prompt = SYSTEM_PROMPT_TEMPLATE.format(skills_list=skills_list, skills_base_path=container_base_path)
return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"

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backend/src/community/aio_sandbox/aio_sandbox_provider.py
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@@ -100,6 +100,26 @@ class AioSandboxProvider(SandboxProvider):
(str(thread_dir / "outputs"), f"{CONTAINER_USER_DATA_DIR}/outputs", False),
]
def _get_skills_mount(self) -> tuple[str, str, bool] | None:
"""Get the skills directory mount configuration.
Returns:
Tuple of (host_path, container_path, read_only) if skills directory exists,
None otherwise.
"""
try:
config = get_app_config()
skills_path = config.skills.get_skills_path()
container_path = config.skills.container_path
# Only mount if skills directory exists
if skills_path.exists():
return (str(skills_path), container_path, True) # Read-only mount for security
except Exception as e:
logger.warning(f"Could not setup skills mount: {e}")
return None
def _start_container(self, sandbox_id: str, port: int, extra_mounts: list[tuple[str, str, bool]] | None = None) -> str:
"""Start a new Docker container for the sandbox.
@@ -208,11 +228,17 @@ class AioSandboxProvider(SandboxProvider):
sandbox_id = str(uuid.uuid4())[:8]
# Get thread-specific mounts if thread_id is provided
extra_mounts = None
extra_mounts = []
if thread_id:
extra_mounts = self._get_thread_mounts(thread_id)
extra_mounts.extend(self._get_thread_mounts(thread_id))
logger.info(f"Adding thread mounts for thread {thread_id}: {extra_mounts}")
# Add skills mount if available
skills_mount = self._get_skills_mount()
if skills_mount:
extra_mounts.append(skills_mount)
logger.info(f"Adding skills mount: {skills_mount}")
# If base_url is configured, use existing sandbox
if self._config.get("base_url"):
base_url = self._config["base_url"]
@@ -230,7 +256,7 @@ class AioSandboxProvider(SandboxProvider):
raise RuntimeError("auto_start is disabled and no base_url is configured")
port = self._find_available_port(self._config["port"])
container_id = self._start_container(sandbox_id, port, extra_mounts=extra_mounts)
container_id = self._start_container(sandbox_id, port, extra_mounts=extra_mounts if extra_mounts else None)
self._containers[sandbox_id] = container_id
base_url = f"http://localhost:{port}"

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backend/src/config/__init__.py
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@@ -1,3 +1,4 @@
from .app_config import get_app_config
from .skills_config import SkillsConfig
__all__ = ["get_app_config"]
__all__ = ["get_app_config", "SkillsConfig"]

2
backend/src/config/app_config.py
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@@ -8,6 +8,7 @@ from pydantic import BaseModel, ConfigDict, Field
from src.config.model_config import ModelConfig
from src.config.sandbox_config import SandboxConfig
from src.config.skills_config import SkillsConfig
from src.config.title_config import load_title_config_from_dict
from src.config.tool_config import ToolConfig, ToolGroupConfig
@@ -21,6 +22,7 @@ class AppConfig(BaseModel):
sandbox: SandboxConfig = Field(description="Sandbox configuration")
tools: list[ToolConfig] = Field(default_factory=list, description="Available tools")
tool_groups: list[ToolGroupConfig] = Field(default_factory=list, description="Available tool groups")
skills: SkillsConfig = Field(default_factory=SkillsConfig, description="Skills configuration")
model_config = ConfigDict(extra="allow", frozen=False)
@classmethod

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backend/src/config/skills_config.py Normal file
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@@ -0,0 +1,49 @@
from pathlib import Path
from pydantic import BaseModel, Field
class SkillsConfig(BaseModel):
"""Configuration for skills system"""
path: str | None = Field(
default=None,
description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
)
container_path: str = Field(
default="/mnt/skills",
description="Path where skills are mounted in the sandbox container",
)
def get_skills_path(self) -> Path:
"""
Get the resolved skills directory path.
Returns:
Path to the skills directory
"""
if self.path:
# Use configured path (can be absolute or relative)
path = Path(self.path)
if not path.is_absolute():
# If relative, resolve from current working directory
path = Path.cwd() / path
return path.resolve()
else:
# Default: ../skills relative to backend directory
from src.skills.loader import get_skills_root_path
return get_skills_root_path()
def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
"""
Get the full container path for a specific skill.
Args:
skill_name: Name of the skill (directory name)
category: Category of the skill (public or custom)
Returns:
Full path to the skill in the container
"""
return f"{self.container_path}/{category}/{skill_name}"

46
backend/src/sandbox/local/local_sandbox.py
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@@ -1,13 +1,46 @@
import os
import subprocess
from pathlib import Path
from src.sandbox.local.list_dir import list_dir
from src.sandbox.sandbox import Sandbox
class LocalSandbox(Sandbox):
def __init__(self, id: str):
def __init__(self, id: str, path_mappings: dict[str, str] | None = None):
"""
Initialize local sandbox with optional path mappings.
Args:
id: Sandbox identifier
path_mappings: Dictionary mapping container paths to local paths
Example: {"/mnt/skills": "/absolute/path/to/skills"}
"""
super().__init__(id)
self.path_mappings = path_mappings or {}
def _resolve_path(self, path: str) -> str:
"""
Resolve container path to actual local path using mappings.
Args:
path: Path that might be a container path
Returns:
Resolved local path
"""
path_str = str(path)
# Try each mapping (longest prefix first for more specific matches)
for container_path, local_path in sorted(self.path_mappings.items(), key=lambda x: len(x[0]), reverse=True):
if path_str.startswith(container_path):
# Replace the container path prefix with local path
relative = path_str[len(container_path) :].lstrip("/")
resolved = str(Path(local_path) / relative) if relative else local_path
return resolved
# No mapping found, return original path
return path_str
def execute_command(self, command: str) -> str:
result = subprocess.run(
@@ -26,16 +59,19 @@ class LocalSandbox(Sandbox):
return output if output else "(no output)"
def list_dir(self, path: str, max_depth=2) -> list[str]:
return list_dir(path, max_depth)
resolved_path = self._resolve_path(path)
return list_dir(resolved_path, max_depth)
def read_file(self, path: str) -> str:
with open(path) as f:
resolved_path = self._resolve_path(path)
with open(resolved_path) as f:
return f.read()
def write_file(self, path: str, content: str, append: bool = False) -> None:
dir_path = os.path.dirname(path)
resolved_path = self._resolve_path(path)
dir_path = os.path.dirname(resolved_path)
if dir_path:
os.makedirs(dir_path, exist_ok=True)
mode = "a" if append else "w"
with open(path, mode) as f:
with open(resolved_path, mode) as f:
f.write(content)

34
backend/src/sandbox/local/local_sandbox_provider.py
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@@ -5,10 +5,42 @@ _singleton: LocalSandbox | None = None
class LocalSandboxProvider(SandboxProvider):
def __init__(self):
"""Initialize the local sandbox provider with path mappings."""
self._path_mappings = self._setup_path_mappings()
def _setup_path_mappings(self) -> dict[str, str]:
"""
Setup path mappings for local sandbox.
Maps container paths to actual local paths, including skills directory.
Returns:
Dictionary of path mappings
"""
mappings = {}
# Map skills container path to local skills directory
try:
from src.config import get_app_config
config = get_app_config()
skills_path = config.skills.get_skills_path()
container_path = config.skills.container_path
# Only add mapping if skills directory exists
if skills_path.exists():
mappings[container_path] = str(skills_path)
except Exception as e:
# Log but don't fail if config loading fails
print(f"Warning: Could not setup skills path mapping: {e}")
return mappings
def acquire(self, thread_id: str | None = None) -> str:
global _singleton
if _singleton is None:
_singleton = LocalSandbox("local")
_singleton = LocalSandbox("local", path_mappings=self._path_mappings)
return _singleton.id
def get(self, sandbox_id: str) -> None:

4
backend/src/skills/__init__.py Normal file
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@@ -0,0 +1,4 @@
from .loader import get_skills_root_path, load_skills
from .types import Skill
__all__ = ["load_skills", "get_skills_root_path", "Skill"]

77
backend/src/skills/loader.py Normal file
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@@ -0,0 +1,77 @@
from pathlib import Path
from .parser import parse_skill_file
from .types import Skill
def get_skills_root_path() -> Path:
"""
Get the root path of the skills directory.
Returns:
Path to the skills directory (deer-flow/skills)
"""
# backend directory is current file's parent's parent's parent
backend_dir = Path(__file__).resolve().parent.parent.parent
# skills directory is sibling to backend directory
skills_dir = backend_dir.parent / "skills"
return skills_dir
def load_skills(skills_path: Path | None = None, use_config: bool = True) -> list[Skill]:
"""
Load all skills from the skills directory.
Scans both public and custom skill directories, parsing SKILL.md files
to extract metadata.
Args:
skills_path: Optional custom path to skills directory.
If not provided and use_config is True, uses path from config.
Otherwise defaults to deer-flow/skills
use_config: Whether to load skills path from config (default: True)
Returns:
List of Skill objects, sorted by name
"""
if skills_path is None:
if use_config:
try:
from src.config import get_app_config
config = get_app_config()
skills_path = config.skills.get_skills_path()
except Exception:
# Fallback to default if config fails
skills_path = get_skills_root_path()
else:
skills_path = get_skills_root_path()
if not skills_path.exists():
return []
skills = []
# Scan public and custom directories
for category in ["public", "custom"]:
category_path = skills_path / category
if not category_path.exists() or not category_path.is_dir():
continue
# Each subdirectory is a potential skill
for skill_dir in category_path.iterdir():
if not skill_dir.is_dir():
continue
skill_file = skill_dir / "SKILL.md"
if not skill_file.exists():
continue
skill = parse_skill_file(skill_file, category=category)
if skill:
skills.append(skill)
# Sort by name for consistent ordering
skills.sort(key=lambda s: s.name)
return skills

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backend/src/skills/parser.py Normal file
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@@ -0,0 +1,63 @@
import re
from pathlib import Path
from .types import Skill
def parse_skill_file(skill_file: Path, category: str) -> Skill | None:
"""
Parse a SKILL.md file and extract metadata.
Args:
skill_file: Path to the SKILL.md file
category: Category of the skill ('public' or 'custom')
Returns:
Skill object if parsing succeeds, None otherwise
"""
if not skill_file.exists() or skill_file.name != "SKILL.md":
return None
try:
content = skill_file.read_text(encoding="utf-8")
# Extract YAML front matter
# Pattern: ---\nkey: value\n---
front_matter_match = re.match(r"^---\s*\n(.*?)\n---\s*\n", content, re.DOTALL)
if not front_matter_match:
return None
front_matter = front_matter_match.group(1)
# Parse YAML front matter (simple key-value parsing)
metadata = {}
for line in front_matter.split("\n"):
line = line.strip()
if not line:
continue
if ":" in line:
key, value = line.split(":", 1)
metadata[key.strip()] = value.strip()
# Extract required fields
name = metadata.get("name")
description = metadata.get("description")
if not name or not description:
return None
license_text = metadata.get("license")
return Skill(
name=name,
description=description,
license=license_text,
skill_dir=skill_file.parent,
skill_file=skill_file,
category=category,
)
except Exception as e:
print(f"Error parsing skill file {skill_file}: {e}")
return None

34
backend/src/skills/types.py Normal file
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@@ -0,0 +1,34 @@
from dataclasses import dataclass
from pathlib import Path
@dataclass
class Skill:
"""Represents a skill with its metadata and file path"""
name: str
description: str
license: str | None
skill_dir: Path
skill_file: Path
category: str # 'public' or 'custom'
@property
def skill_path(self) -> str:
"""Returns the relative path from skills root to this skill's directory"""
return self.skill_dir.name
def get_container_path(self, container_base_path: str = "/mnt/skills") -> str:
"""
Get the full path to this skill in the container.
Args:
container_base_path: Base path where skills are mounted in the container
Returns:
Full container path to the skill directory
"""
return f"{container_base_path}/{self.category}/{self.skill_dir.name}"
def __repr__(self) -> str:
return f"Skill(name={self.name!r}, description={self.description!r}, category={self.category!r})"

137
config.example.yaml
View File

@@ -1,30 +1,65 @@
# Configuration for the DeerFlow application
#
# Guidelines:
# - The default path of this configuration file is `config.yaml` in the CWD (Current Working Directory) or the parent directory of the CWD.
# How ever you can change it using the `DEER_FLOW_CONFIG_PATH` environment variable.
# - Copy this file to `config.yaml` and customize it for your environment
# - The default path of this configuration file is `config.yaml` in the current working directory.
# However you can change it using the `DEER_FLOW_CONFIG_PATH` environment variable.
# - Environment variables are available for all field values. Example: `api_key: $OPENAI_API_KEY`
# - Provider path is a string that looks like "package_name.sub_package_name.module_name:class_name/variable_name".
# - The `use` path is a string that looks like "package_name.sub_package_name.module_name:class_name/variable_name".
# ============================================================================
# Models Configuration
# ============================================================================
# Configure available LLM models for the agent to use
models:
- name: doubao-seed-1.8
display_name: Doubao 1.8
use: langchain_deepseek:ChatDeepSeek
model: doubao-seed-1-8-251228
api_base: https://ark.cn-beijing.volces.com/api/v3
api_key: $ARK_API_KEY
supports_thinking: true
when_thinking_enabled:
extra_body:
thinking:
type: enabled
- name: gpt-5
display_name: GPT-5
# Example: OpenAI model
- name: gpt-4
display_name: GPT-4
use: langchain_openai:ChatOpenAI
model: gpt-5-251228
api_base: https://api.openai.com/v1
api_key: $OPENAI_API_KEY
supports_thinking: true
model: gpt-4
api_key: $OPENAI_API_KEY # Use environment variable
max_tokens: 4096
temperature: 0.7
# Example: Anthropic Claude model
# - name: claude-3-5-sonnet
# display_name: Claude 3.5 Sonnet
# use: langchain_anthropic:ChatAnthropic
# model: claude-3-5-sonnet-20241022
# api_key: $ANTHROPIC_API_KEY
# max_tokens: 8192
# Example: DeepSeek model (with thinking support)
# - name: deepseek-v3
# display_name: DeepSeek V3 (Thinking)
# use: langchain_deepseek:ChatDeepSeek
# model: deepseek-chat
# api_key: $DEEPSEEK_API_KEY
# max_tokens: 16384
# supports_thinking: true
# when_thinking_enabled:
# extra_body:
# thinking:
# type: enabled
# Example: Volcengine (Doubao) model
# - name: doubao-seed-1.8
# display_name: Doubao 1.8 (Thinking)
# use: langchain_deepseek:ChatDeepSeek
# model: ep-m-20260106111913-xxxxx
# api_base: https://ark.cn-beijing.volces.com/api/v3
# api_key: $VOLCENGINE_API_KEY
# supports_thinking: true
# when_thinking_enabled:
# extra_body:
# thinking:
# type: enabled
# ============================================================================
# Tool Groups Configuration
# ============================================================================
# Define groups of tools for organization and access control
tool_groups:
- name: web
@@ -32,17 +67,26 @@ tool_groups:
- name: file:write
- name: bash
# ============================================================================
# Tools Configuration
# ============================================================================
# Configure available tools for the agent to use
tools:
# Web search tool (requires Tavily API key)
- name: web_search
group: web
use: src.community.tavily.tools:web_search_tool
max_results: 5
# api_key: $TAVILY_API_KEY # Set if needed
# Web fetch tool (uses Jina AI reader)
- name: web_fetch
group: web
use: src.community.jina_ai.tools:web_fetch_tool
timeout: 10
# File operations tools
- name: ls
group: file:read
use: src.sandbox.tools:ls_tool
@@ -59,37 +103,74 @@ tools:
group: file:write
use: src.sandbox.tools:str_replace_tool
# Bash execution tool
- name: bash
group: bash
use: src.sandbox.tools:bash_tool
# ============================================================================
# Sandbox Configuration
# ============================================================================
# Choose between local sandbox (direct execution) or Docker-based AIO sandbox
# Option 1: Local Sandbox (Default)
# Executes commands directly on the host machine
sandbox:
use: src.sandbox.local:LocalSandboxProvider
# To use Docker-based AIO sandbox instead, uncomment the following:
# Option 2: Docker-based AIO Sandbox
# Executes commands in isolated Docker containers
# Uncomment to use:
# sandbox:
# use: src.community.aio_sandbox:AioSandboxProvider
#
# # Optional: Use existing sandbox at this URL (no Docker container will be started)
# # base_url: http://localhost:8080
# # Optional: Docker image to use (default: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest)
#
# # Optional: Docker image to use
# # Default: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
# # image: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
#
# # Optional: Base port for sandbox containers (default: 8080)
# # port: 8080
#
# # Optional: Whether to automatically start Docker container (default: true)
# # auto_start: true
#
# # Optional: Prefix for container names (default: deer-flow-sandbox)
# # container_prefix: deer-flow-sandbox
# # Optional: Mount directories from host to container
#
# # Optional: Additional mount directories from host to container
# # NOTE: Skills directory is automatically mounted from skills.path to skills.container_path
# # mounts:
# # # Other custom mounts
# # - host_path: /path/on/host
# # container_path: /home/user/shared
# # read_only: false
# # - host_path: /another/path
# # container_path: /data
# # read_only: true
# Automatic thread title generation
# ============================================================================
# Skills Configuration
# ============================================================================
# Configure skills directory for specialized agent workflows
skills:
# Path to skills directory on the host (relative to project root or absolute)
# Default: ../skills (relative to backend directory)
# Uncomment to customize:
# path: /absolute/path/to/custom/skills
# Path where skills are mounted in the sandbox container
# This is used by the agent to access skills in both local and Docker sandbox
# Default: /mnt/skills
container_path: /mnt/skills
# ============================================================================
# Title Generation Configuration
# ============================================================================
# Automatic conversation title generation settings
title:
enabled: true
max_words: 6
max_chars: 60
model_name: null # Use default model
model_name: null # Use default model (first model in models list)

177
skills/public/frontend-design/LICENSE.txt Normal file
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@@ -0,0 +1,177 @@
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42
skills/public/frontend-design/SKILL.md Normal file
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@@ -0,0 +1,42 @@
---
name: frontend-design
description: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
license: Complete terms in LICENSE.txt
---
This skill guides creation of distinctive, production-grade frontend interfaces that avoid generic "AI slop" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.
The user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.
## Design Thinking
Before coding, understand the context and commit to a BOLD aesthetic direction:
- **Purpose**: What problem does this interface solve? Who uses it?
- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.
- **Constraints**: Technical requirements (framework, performance, accessibility).
- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?
**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work - the key is intentionality, not intensity.
Then implement working code (HTML/CSS/JS, React, Vue, etc.) that is:
- Production-grade and functional
- Visually striking and memorable
- Cohesive with a clear aesthetic point-of-view
- Meticulously refined in every detail
## Frontend Aesthetics Guidelines
Focus on:
- **Typography**: Choose fonts that are beautiful, unique, and interesting. Avoid generic fonts like Arial and Inter; opt instead for distinctive choices that elevate the frontend's aesthetics; unexpected, characterful font choices. Pair a distinctive display font with a refined body font.
- **Color & Theme**: Commit to a cohesive aesthetic. Use CSS variables for consistency. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.
- **Motion**: Use animations for effects and micro-interactions. Prioritize CSS-only solutions for HTML. Use Motion library for React when available. Focus on high-impact moments: one well-orchestrated page load with staggered reveals (animation-delay) creates more delight than scattered micro-interactions. Use scroll-triggering and hover states that surprise.
- **Spatial Composition**: Unexpected layouts. Asymmetry. Overlap. Diagonal flow. Grid-breaking elements. Generous negative space OR controlled density.
- **Backgrounds & Visual Details**: Create atmosphere and depth rather than defaulting to solid colors. Add contextual effects and textures that match the overall aesthetic. Apply creative forms like gradient meshes, noise textures, geometric patterns, layered transparencies, dramatic shadows, decorative borders, custom cursors, and grain overlays.
NEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white backgrounds), predictable layouts and component patterns, and cookie-cutter design that lacks context-specific character.
Interpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices (Space Grotesk, for example) across generations.
**IMPORTANT**: Match implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details. Elegance comes from executing the vision well.
Remember: Claude is capable of extraordinary creative work. Don't hold back, show what can truly be created when thinking outside the box and committing fully to a distinctive vision.

30
skills/public/pdf-processing/LICENSE.txt Normal file
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@@ -0,0 +1,30 @@
© 2025 Anthropic, PBC. All rights reserved.
LICENSE: Use of these materials (including all code, prompts, assets, files,
and other components of this Skill) is governed by your agreement with
Anthropic regarding use of Anthropic's services. If no separate agreement
exists, use is governed by Anthropic's Consumer Terms of Service or
Commercial Terms of Service, as applicable:
https://www.anthropic.com/legal/consumer-terms
https://www.anthropic.com/legal/commercial-terms
Your applicable agreement is referred to as the "Agreement." "Services" are
as defined in the Agreement.
ADDITIONAL RESTRICTIONS: Notwithstanding anything in the Agreement to the
contrary, users may not:
- Extract these materials from the Services or retain copies of these
materials outside the Services
- Reproduce or copy these materials, except for temporary copies created
automatically during authorized use of the Services
- Create derivative works based on these materials
- Distribute, sublicense, or transfer these materials to any third party
- Make, offer to sell, sell, or import any inventions embodied in these
materials
- Reverse engineer, decompile, or disassemble these materials
The receipt, viewing, or possession of these materials does not convey or
imply any license or right beyond those expressly granted above.
Anthropic retains all right, title, and interest in these materials,
including all copyrights, patents, and other intellectual property rights.

294
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@@ -0,0 +1,294 @@
---
name: pdf
description: Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.
license: Proprietary. LICENSE.txt has complete terms
---
# PDF Processing Guide
## Overview
This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see reference.md. If you need to fill out a PDF form, read forms.md and follow its instructions.
## Quick Start
```python
from pypdf import PdfReader, PdfWriter
# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")
# Extract text
text = ""
for page in reader.pages:
text += page.extract_text()
```
## Python Libraries
### pypdf - Basic Operations
#### Merge PDFs
```python
from pypdf import PdfWriter, PdfReader
writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
reader = PdfReader(pdf_file)
for page in reader.pages:
writer.add_page(page)
with open("merged.pdf", "wb") as output:
writer.write(output)
```
#### Split PDF
```python
reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
writer = PdfWriter()
writer.add_page(page)
with open(f"page_{i+1}.pdf", "wb") as output:
writer.write(output)
```
#### Extract Metadata
```python
reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Subject: {meta.subject}")
print(f"Creator: {meta.creator}")
```
#### Rotate Pages
```python
reader = PdfReader("input.pdf")
writer = PdfWriter()
page = reader.pages[0]
page.rotate(90) # Rotate 90 degrees clockwise
writer.add_page(page)
with open("rotated.pdf", "wb") as output:
writer.write(output)
```
### pdfplumber - Text and Table Extraction
#### Extract Text with Layout
```python
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
print(text)
```
#### Extract Tables
```python
with pdfplumber.open("document.pdf") as pdf:
for i, page in enumerate(pdf.pages):
tables = page.extract_tables()
for j, table in enumerate(tables):
print(f"Table {j+1} on page {i+1}:")
for row in table:
print(row)
```
#### Advanced Table Extraction
```python
import pandas as pd
with pdfplumber.open("document.pdf") as pdf:
all_tables = []
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if table: # Check if table is not empty
df = pd.DataFrame(table[1:], columns=table[0])
all_tables.append(df)
# Combine all tables
if all_tables:
combined_df = pd.concat(all_tables, ignore_index=True)
combined_df.to_excel("extracted_tables.xlsx", index=False)
```
### reportlab - Create PDFs
#### Basic PDF Creation
```python
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter
# Add text
c.drawString(100, height - 100, "Hello World!")
c.drawString(100, height - 120, "This is a PDF created with reportlab")
# Add a line
c.line(100, height - 140, 400, height - 140)
# Save
c.save()
```
#### Create PDF with Multiple Pages
```python
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet
doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []
# Add content
title = Paragraph("Report Title", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))
body = Paragraph("This is the body of the report. " * 20, styles['Normal'])
story.append(body)
story.append(PageBreak())
# Page 2
story.append(Paragraph("Page 2", styles['Heading1']))
story.append(Paragraph("Content for page 2", styles['Normal']))
# Build PDF
doc.build(story)
```
## Command-Line Tools
### pdftotext (poppler-utils)
```bash
# Extract text
pdftotext input.pdf output.txt
# Extract text preserving layout
pdftotext -layout input.pdf output.txt
# Extract specific pages
pdftotext -f 1 -l 5 input.pdf output.txt # Pages 1-5
```
### qpdf
```bash
# Merge PDFs
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf
# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
qpdf input.pdf --pages . 6-10 -- pages6-10.pdf
# Rotate pages
qpdf input.pdf output.pdf --rotate=+90:1 # Rotate page 1 by 90 degrees
# Remove password
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf
```
### pdftk (if available)
```bash
# Merge
pdftk file1.pdf file2.pdf cat output merged.pdf
# Split
pdftk input.pdf burst
# Rotate
pdftk input.pdf rotate 1east output rotated.pdf
```
## Common Tasks
### Extract Text from Scanned PDFs
```python
# Requires: pip install pytesseract pdf2image
import pytesseract
from pdf2image import convert_from_path
# Convert PDF to images
images = convert_from_path('scanned.pdf')
# OCR each page
text = ""
for i, image in enumerate(images):
text += f"Page {i+1}:\n"
text += pytesseract.image_to_string(image)
text += "\n\n"
print(text)
```
### Add Watermark
```python
from pypdf import PdfReader, PdfWriter
# Create watermark (or load existing)
watermark = PdfReader("watermark.pdf").pages[0]
# Apply to all pages
reader = PdfReader("document.pdf")
writer = PdfWriter()
for page in reader.pages:
page.merge_page(watermark)
writer.add_page(page)
with open("watermarked.pdf", "wb") as output:
writer.write(output)
```
### Extract Images
```bash
# Using pdfimages (poppler-utils)
pdfimages -j input.pdf output_prefix
# This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.
```
### Password Protection
```python
from pypdf import PdfReader, PdfWriter
reader = PdfReader("input.pdf")
writer = PdfWriter()
for page in reader.pages:
writer.add_page(page)
# Add password
writer.encrypt("userpassword", "ownerpassword")
with open("encrypted.pdf", "wb") as output:
writer.write(output)
```
## Quick Reference
| Task | Best Tool | Command/Code |
|------|-----------|--------------|
| Merge PDFs | pypdf | `writer.add_page(page)` |
| Split PDFs | pypdf | One page per file |
| Extract text | pdfplumber | `page.extract_text()` |
| Extract tables | pdfplumber | `page.extract_tables()` |
| Create PDFs | reportlab | Canvas or Platypus |
| Command line merge | qpdf | `qpdf --empty --pages ...` |
| OCR scanned PDFs | pytesseract | Convert to image first |
| Fill PDF forms | pdf-lib or pypdf (see forms.md) | See forms.md |
## Next Steps
- For advanced pypdfium2 usage, see reference.md
- For JavaScript libraries (pdf-lib), see reference.md
- If you need to fill out a PDF form, follow the instructions in forms.md
- For troubleshooting guides, see reference.md

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**CRITICAL: You MUST complete these steps in order. Do not skip ahead to writing code.**
If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory:
`python scripts/check_fillable_fields <file.pdf>`, and depending on the result go to either the "Fillable fields" or "Non-fillable fields" and follow those instructions.
# Fillable fields
If the PDF has fillable form fields:
- Run this script from this file's directory: `python scripts/extract_form_field_info.py <input.pdf> <field_info.json>`. It will create a JSON file with a list of fields in this format:
```
[
{
"field_id": (unique ID for the field),
"page": (page number, 1-based),
"rect": ([left, bottom, right, top] bounding box in PDF coordinates, y=0 is the bottom of the page),
"type": ("text", "checkbox", "radio_group", or "choice"),
},
// Checkboxes have "checked_value" and "unchecked_value" properties:
{
"field_id": (unique ID for the field),
"page": (page number, 1-based),
"type": "checkbox",
"checked_value": (Set the field to this value to check the checkbox),
"unchecked_value": (Set the field to this value to uncheck the checkbox),
},
// Radio groups have a "radio_options" list with the possible choices.
{
"field_id": (unique ID for the field),
"page": (page number, 1-based),
"type": "radio_group",
"radio_options": [
{
"value": (set the field to this value to select this radio option),
"rect": (bounding box for the radio button for this option)
},
// Other radio options
]
},
// Multiple choice fields have a "choice_options" list with the possible choices:
{
"field_id": (unique ID for the field),
"page": (page number, 1-based),
"type": "choice",
"choice_options": [
{
"value": (set the field to this value to select this option),
"text": (display text of the option)
},
// Other choice options
],
}
]
```
- Convert the PDF to PNGs (one image for each page) with this script (run from this file's directory):
`python scripts/convert_pdf_to_images.py <file.pdf> <output_directory>`
Then analyze the images to determine the purpose of each form field (make sure to convert the bounding box PDF coordinates to image coordinates).
- Create a `field_values.json` file in this format with the values to be entered for each field:
```
[
{
"field_id": "last_name", // Must match the field_id from `extract_form_field_info.py`
"description": "The user's last name",
"page": 1, // Must match the "page" value in field_info.json
"value": "Simpson"
},
{
"field_id": "Checkbox12",
"description": "Checkbox to be checked if the user is 18 or over",
"page": 1,
"value": "/On" // If this is a checkbox, use its "checked_value" value to check it. If it's a radio button group, use one of the "value" values in "radio_options".
},
// more fields
]
```
- Run the `fill_fillable_fields.py` script from this file's directory to create a filled-in PDF:
`python scripts/fill_fillable_fields.py <input pdf> <field_values.json> <output pdf>`
This script will verify that the field IDs and values you provide are valid; if it prints error messages, correct the appropriate fields and try again.
# Non-fillable fields
If the PDF doesn't have fillable form fields, you'll need to visually determine where the data should be added and create text annotations. Follow the below steps *exactly*. You MUST perform all of these steps to ensure that the the form is accurately completed. Details for each step are below.
- Convert the PDF to PNG images and determine field bounding boxes.
- Create a JSON file with field information and validation images showing the bounding boxes.
- Validate the the bounding boxes.
- Use the bounding boxes to fill in the form.
## Step 1: Visual Analysis (REQUIRED)
- Convert the PDF to PNG images. Run this script from this file's directory:
`python scripts/convert_pdf_to_images.py <file.pdf> <output_directory>`
The script will create a PNG image for each page in the PDF.
- Carefully examine each PNG image and identify all form fields and areas where the user should enter data. For each form field where the user should enter text, determine bounding boxes for both the form field label, and the area where the user should enter text. The label and entry bounding boxes MUST NOT INTERSECT; the text entry box should only include the area where data should be entered. Usually this area will be immediately to the side, above, or below its label. Entry bounding boxes must be tall and wide enough to contain their text.
These are some examples of form structures that you might see:
*Label inside box*
```
┌────────────────────────┐
│ Name: │
└────────────────────────┘
```
The input area should be to the right of the "Name" label and extend to the edge of the box.
*Label before line*
```
Email: _______________________
```
The input area should be above the line and include its entire width.
*Label under line*
```
_________________________
Name
```
The input area should be above the line and include the entire width of the line. This is common for signature and date fields.
*Label above line*
```
Please enter any special requests:
________________________________________________
```
The input area should extend from the bottom of the label to the line, and should include the entire width of the line.
*Checkboxes*
```
Are you a US citizen? Yes □ No □
```
For checkboxes:
- Look for small square boxes (□) - these are the actual checkboxes to target. They may be to the left or right of their labels.
- Distinguish between label text ("Yes", "No") and the clickable checkbox squares.
- The entry bounding box should cover ONLY the small square, not the text label.
### Step 2: Create fields.json and validation images (REQUIRED)
- Create a file named `fields.json` with information for the form fields and bounding boxes in this format:
```
{
"pages": [
{
"page_number": 1,
"image_width": (first page image width in pixels),
"image_height": (first page image height in pixels),
},
{
"page_number": 2,
"image_width": (second page image width in pixels),
"image_height": (second page image height in pixels),
}
// additional pages
],
"form_fields": [
// Example for a text field.
{
"page_number": 1,
"description": "The user's last name should be entered here",
// Bounding boxes are [left, top, right, bottom]. The bounding boxes for the label and text entry should not overlap.
"field_label": "Last name",
"label_bounding_box": [30, 125, 95, 142],
"entry_bounding_box": [100, 125, 280, 142],
"entry_text": {
"text": "Johnson", // This text will be added as an annotation at the entry_bounding_box location
"font_size": 14, // optional, defaults to 14
"font_color": "000000", // optional, RRGGBB format, defaults to 000000 (black)
}
},
// Example for a checkbox. TARGET THE SQUARE for the entry bounding box, NOT THE TEXT
{
"page_number": 2,
"description": "Checkbox that should be checked if the user is over 18",
"entry_bounding_box": [140, 525, 155, 540], // Small box over checkbox square
"field_label": "Yes",
"label_bounding_box": [100, 525, 132, 540], // Box containing "Yes" text
// Use "X" to check a checkbox.
"entry_text": {
"text": "X",
}
}
// additional form field entries
]
}
```
Create validation images by running this script from this file's directory for each page:
`python scripts/create_validation_image.py <page_number> <path_to_fields.json> <input_image_path> <output_image_path>
The validation images will have red rectangles where text should be entered, and blue rectangles covering label text.
### Step 3: Validate Bounding Boxes (REQUIRED)
#### Automated intersection check
- Verify that none of bounding boxes intersect and that the entry bounding boxes are tall enough by checking the fields.json file with the `check_bounding_boxes.py` script (run from this file's directory):
`python scripts/check_bounding_boxes.py <JSON file>`
If there are errors, reanalyze the relevant fields, adjust the bounding boxes, and iterate until there are no remaining errors. Remember: label (blue) bounding boxes should contain text labels, entry (red) boxes should not.
#### Manual image inspection
**CRITICAL: Do not proceed without visually inspecting validation images**
- Red rectangles must ONLY cover input areas
- Red rectangles MUST NOT contain any text
- Blue rectangles should contain label text
- For checkboxes:
- Red rectangle MUST be centered on the checkbox square
- Blue rectangle should cover the text label for the checkbox
- If any rectangles look wrong, fix fields.json, regenerate the validation images, and verify again. Repeat this process until the bounding boxes are fully accurate.
### Step 4: Add annotations to the PDF
Run this script from this file's directory to create a filled-out PDF using the information in fields.json:
`python scripts/fill_pdf_form_with_annotations.py <input_pdf_path> <path_to_fields.json> <output_pdf_path>

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# PDF Processing Advanced Reference
This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.
## pypdfium2 Library (Apache/BSD License)
### Overview
pypdfium2 is a Python binding for PDFium (Chromium's PDF library). It's excellent for fast PDF rendering, image generation, and serves as a PyMuPDF replacement.
### Render PDF to Images
```python
import pypdfium2 as pdfium
from PIL import Image
# Load PDF
pdf = pdfium.PdfDocument("document.pdf")
# Render page to image
page = pdf[0] # First page
bitmap = page.render(
scale=2.0, # Higher resolution
rotation=0 # No rotation
)
# Convert to PIL Image
img = bitmap.to_pil()
img.save("page_1.png", "PNG")
# Process multiple pages
for i, page in enumerate(pdf):
bitmap = page.render(scale=1.5)
img = bitmap.to_pil()
img.save(f"page_{i+1}.jpg", "JPEG", quality=90)
```
### Extract Text with pypdfium2
```python
import pypdfium2 as pdfium
pdf = pdfium.PdfDocument("document.pdf")
for i, page in enumerate(pdf):
text = page.get_text()
print(f"Page {i+1} text length: {len(text)} chars")
```
## JavaScript Libraries
### pdf-lib (MIT License)
pdf-lib is a powerful JavaScript library for creating and modifying PDF documents in any JavaScript environment.
#### Load and Manipulate Existing PDF
```javascript
import { PDFDocument } from 'pdf-lib';
import fs from 'fs';
async function manipulatePDF() {
// Load existing PDF
const existingPdfBytes = fs.readFileSync('input.pdf');
const pdfDoc = await PDFDocument.load(existingPdfBytes);
// Get page count
const pageCount = pdfDoc.getPageCount();
console.log(`Document has ${pageCount} pages`);
// Add new page
const newPage = pdfDoc.addPage([600, 400]);
newPage.drawText('Added by pdf-lib', {
x: 100,
y: 300,
size: 16
});
// Save modified PDF
const pdfBytes = await pdfDoc.save();
fs.writeFileSync('modified.pdf', pdfBytes);
}
```
#### Create Complex PDFs from Scratch
```javascript
import { PDFDocument, rgb, StandardFonts } from 'pdf-lib';
import fs from 'fs';
async function createPDF() {
const pdfDoc = await PDFDocument.create();
// Add fonts
const helveticaFont = await pdfDoc.embedFont(StandardFonts.Helvetica);
const helveticaBold = await pdfDoc.embedFont(StandardFonts.HelveticaBold);
// Add page
const page = pdfDoc.addPage([595, 842]); // A4 size
const { width, height } = page.getSize();
// Add text with styling
page.drawText('Invoice #12345', {
x: 50,
y: height - 50,
size: 18,
font: helveticaBold,
color: rgb(0.2, 0.2, 0.8)
});
// Add rectangle (header background)
page.drawRectangle({
x: 40,
y: height - 100,
width: width - 80,
height: 30,
color: rgb(0.9, 0.9, 0.9)
});
// Add table-like content
const items = [
['Item', 'Qty', 'Price', 'Total'],
['Widget', '2', '$50', '$100'],
['Gadget', '1', '$75', '$75']
];
let yPos = height - 150;
items.forEach(row => {
let xPos = 50;
row.forEach(cell => {
page.drawText(cell, {
x: xPos,
y: yPos,
size: 12,
font: helveticaFont
});
xPos += 120;
});
yPos -= 25;
});
const pdfBytes = await pdfDoc.save();
fs.writeFileSync('created.pdf', pdfBytes);
}
```
#### Advanced Merge and Split Operations
```javascript
import { PDFDocument } from 'pdf-lib';
import fs from 'fs';
async function mergePDFs() {
// Create new document
const mergedPdf = await PDFDocument.create();
// Load source PDFs
const pdf1Bytes = fs.readFileSync('doc1.pdf');
const pdf2Bytes = fs.readFileSync('doc2.pdf');
const pdf1 = await PDFDocument.load(pdf1Bytes);
const pdf2 = await PDFDocument.load(pdf2Bytes);
// Copy pages from first PDF
const pdf1Pages = await mergedPdf.copyPages(pdf1, pdf1.getPageIndices());
pdf1Pages.forEach(page => mergedPdf.addPage(page));
// Copy specific pages from second PDF (pages 0, 2, 4)
const pdf2Pages = await mergedPdf.copyPages(pdf2, [0, 2, 4]);
pdf2Pages.forEach(page => mergedPdf.addPage(page));
const mergedPdfBytes = await mergedPdf.save();
fs.writeFileSync('merged.pdf', mergedPdfBytes);
}
```
### pdfjs-dist (Apache License)
PDF.js is Mozilla's JavaScript library for rendering PDFs in the browser.
#### Basic PDF Loading and Rendering
```javascript
import * as pdfjsLib from 'pdfjs-dist';
// Configure worker (important for performance)
pdfjsLib.GlobalWorkerOptions.workerSrc = './pdf.worker.js';
async function renderPDF() {
// Load PDF
const loadingTask = pdfjsLib.getDocument('document.pdf');
const pdf = await loadingTask.promise;
console.log(`Loaded PDF with ${pdf.numPages} pages`);
// Get first page
const page = await pdf.getPage(1);
const viewport = page.getViewport({ scale: 1.5 });
// Render to canvas
const canvas = document.createElement('canvas');
const context = canvas.getContext('2d');
canvas.height = viewport.height;
canvas.width = viewport.width;
const renderContext = {
canvasContext: context,
viewport: viewport
};
await page.render(renderContext).promise;
document.body.appendChild(canvas);
}
```
#### Extract Text with Coordinates
```javascript
import * as pdfjsLib from 'pdfjs-dist';
async function extractText() {
const loadingTask = pdfjsLib.getDocument('document.pdf');
const pdf = await loadingTask.promise;
let fullText = '';
// Extract text from all pages
for (let i = 1; i <= pdf.numPages; i++) {
const page = await pdf.getPage(i);
const textContent = await page.getTextContent();
const pageText = textContent.items
.map(item => item.str)
.join(' ');
fullText += `\n--- Page ${i} ---\n${pageText}`;
// Get text with coordinates for advanced processing
const textWithCoords = textContent.items.map(item => ({
text: item.str,
x: item.transform[4],
y: item.transform[5],
width: item.width,
height: item.height
}));
}
console.log(fullText);
return fullText;
}
```
#### Extract Annotations and Forms
```javascript
import * as pdfjsLib from 'pdfjs-dist';
async function extractAnnotations() {
const loadingTask = pdfjsLib.getDocument('annotated.pdf');
const pdf = await loadingTask.promise;
for (let i = 1; i <= pdf.numPages; i++) {
const page = await pdf.getPage(i);
const annotations = await page.getAnnotations();
annotations.forEach(annotation => {
console.log(`Annotation type: ${annotation.subtype}`);
console.log(`Content: ${annotation.contents}`);
console.log(`Coordinates: ${JSON.stringify(annotation.rect)}`);
});
}
}
```
## Advanced Command-Line Operations
### poppler-utils Advanced Features
#### Extract Text with Bounding Box Coordinates
```bash
# Extract text with bounding box coordinates (essential for structured data)
pdftotext -bbox-layout document.pdf output.xml
# The XML output contains precise coordinates for each text element
```
#### Advanced Image Conversion
```bash
# Convert to PNG images with specific resolution
pdftoppm -png -r 300 document.pdf output_prefix
# Convert specific page range with high resolution
pdftoppm -png -r 600 -f 1 -l 3 document.pdf high_res_pages
# Convert to JPEG with quality setting
pdftoppm -jpeg -jpegopt quality=85 -r 200 document.pdf jpeg_output
```
#### Extract Embedded Images
```bash
# Extract all embedded images with metadata
pdfimages -j -p document.pdf page_images
# List image info without extracting
pdfimages -list document.pdf
# Extract images in their original format
pdfimages -all document.pdf images/img
```
### qpdf Advanced Features
#### Complex Page Manipulation
```bash
# Split PDF into groups of pages
qpdf --split-pages=3 input.pdf output_group_%02d.pdf
# Extract specific pages with complex ranges
qpdf input.pdf --pages input.pdf 1,3-5,8,10-end -- extracted.pdf
# Merge specific pages from multiple PDFs
qpdf --empty --pages doc1.pdf 1-3 doc2.pdf 5-7 doc3.pdf 2,4 -- combined.pdf
```
#### PDF Optimization and Repair
```bash
# Optimize PDF for web (linearize for streaming)
qpdf --linearize input.pdf optimized.pdf
# Remove unused objects and compress
qpdf --optimize-level=all input.pdf compressed.pdf
# Attempt to repair corrupted PDF structure
qpdf --check input.pdf
qpdf --fix-qdf damaged.pdf repaired.pdf
# Show detailed PDF structure for debugging
qpdf --show-all-pages input.pdf > structure.txt
```
#### Advanced Encryption
```bash
# Add password protection with specific permissions
qpdf --encrypt user_pass owner_pass 256 --print=none --modify=none -- input.pdf encrypted.pdf
# Check encryption status
qpdf --show-encryption encrypted.pdf
# Remove password protection (requires password)
qpdf --password=secret123 --decrypt encrypted.pdf decrypted.pdf
```
## Advanced Python Techniques
### pdfplumber Advanced Features
#### Extract Text with Precise Coordinates
```python
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
page = pdf.pages[0]
# Extract all text with coordinates
chars = page.chars
for char in chars[:10]: # First 10 characters
print(f"Char: '{char['text']}' at x:{char['x0']:.1f} y:{char['y0']:.1f}")
# Extract text by bounding box (left, top, right, bottom)
bbox_text = page.within_bbox((100, 100, 400, 200)).extract_text()
```
#### Advanced Table Extraction with Custom Settings
```python
import pdfplumber
import pandas as pd
with pdfplumber.open("complex_table.pdf") as pdf:
page = pdf.pages[0]
# Extract tables with custom settings for complex layouts
table_settings = {
"vertical_strategy": "lines",
"horizontal_strategy": "lines",
"snap_tolerance": 3,
"intersection_tolerance": 15
}
tables = page.extract_tables(table_settings)
# Visual debugging for table extraction
img = page.to_image(resolution=150)
img.save("debug_layout.png")
```
### reportlab Advanced Features
#### Create Professional Reports with Tables
```python
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
# Sample data
data = [
['Product', 'Q1', 'Q2', 'Q3', 'Q4'],
['Widgets', '120', '135', '142', '158'],
['Gadgets', '85', '92', '98', '105']
]
# Create PDF with table
doc = SimpleDocTemplate("report.pdf")
elements = []
# Add title
styles = getSampleStyleSheet()
title = Paragraph("Quarterly Sales Report", styles['Title'])
elements.append(title)
# Add table with advanced styling
table = Table(data)
table.setStyle(TableStyle([
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 14),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
('BACKGROUND', (0, 1), (-1, -1), colors.beige),
('GRID', (0, 0), (-1, -1), 1, colors.black)
]))
elements.append(table)
doc.build(elements)
```
## Complex Workflows
### Extract Figures/Images from PDF
#### Method 1: Using pdfimages (fastest)
```bash
# Extract all images with original quality
pdfimages -all document.pdf images/img
```
#### Method 2: Using pypdfium2 + Image Processing
```python
import pypdfium2 as pdfium
from PIL import Image
import numpy as np
def extract_figures(pdf_path, output_dir):
pdf = pdfium.PdfDocument(pdf_path)
for page_num, page in enumerate(pdf):
# Render high-resolution page
bitmap = page.render(scale=3.0)
img = bitmap.to_pil()
# Convert to numpy for processing
img_array = np.array(img)
# Simple figure detection (non-white regions)
mask = np.any(img_array != [255, 255, 255], axis=2)
# Find contours and extract bounding boxes
# (This is simplified - real implementation would need more sophisticated detection)
# Save detected figures
# ... implementation depends on specific needs
```
### Batch PDF Processing with Error Handling
```python
import os
import glob
from pypdf import PdfReader, PdfWriter
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def batch_process_pdfs(input_dir, operation='merge'):
pdf_files = glob.glob(os.path.join(input_dir, "*.pdf"))
if operation == 'merge':
writer = PdfWriter()
for pdf_file in pdf_files:
try:
reader = PdfReader(pdf_file)
for page in reader.pages:
writer.add_page(page)
logger.info(f"Processed: {pdf_file}")
except Exception as e:
logger.error(f"Failed to process {pdf_file}: {e}")
continue
with open("batch_merged.pdf", "wb") as output:
writer.write(output)
elif operation == 'extract_text':
for pdf_file in pdf_files:
try:
reader = PdfReader(pdf_file)
text = ""
for page in reader.pages:
text += page.extract_text()
output_file = pdf_file.replace('.pdf', '.txt')
with open(output_file, 'w', encoding='utf-8') as f:
f.write(text)
logger.info(f"Extracted text from: {pdf_file}")
except Exception as e:
logger.error(f"Failed to extract text from {pdf_file}: {e}")
continue
```
### Advanced PDF Cropping
```python
from pypdf import PdfWriter, PdfReader
reader = PdfReader("input.pdf")
writer = PdfWriter()
# Crop page (left, bottom, right, top in points)
page = reader.pages[0]
page.mediabox.left = 50
page.mediabox.bottom = 50
page.mediabox.right = 550
page.mediabox.top = 750
writer.add_page(page)
with open("cropped.pdf", "wb") as output:
writer.write(output)
```
## Performance Optimization Tips
### 1. For Large PDFs
- Use streaming approaches instead of loading entire PDF in memory
- Use `qpdf --split-pages` for splitting large files
- Process pages individually with pypdfium2
### 2. For Text Extraction
- `pdftotext -bbox-layout` is fastest for plain text extraction
- Use pdfplumber for structured data and tables
- Avoid `pypdf.extract_text()` for very large documents
### 3. For Image Extraction
- `pdfimages` is much faster than rendering pages
- Use low resolution for previews, high resolution for final output
### 4. For Form Filling
- pdf-lib maintains form structure better than most alternatives
- Pre-validate form fields before processing
### 5. Memory Management
```python
# Process PDFs in chunks
def process_large_pdf(pdf_path, chunk_size=10):
reader = PdfReader(pdf_path)
total_pages = len(reader.pages)
for start_idx in range(0, total_pages, chunk_size):
end_idx = min(start_idx + chunk_size, total_pages)
writer = PdfWriter()
for i in range(start_idx, end_idx):
writer.add_page(reader.pages[i])
# Process chunk
with open(f"chunk_{start_idx//chunk_size}.pdf", "wb") as output:
writer.write(output)
```
## Troubleshooting Common Issues
### Encrypted PDFs
```python
# Handle password-protected PDFs
from pypdf import PdfReader
try:
reader = PdfReader("encrypted.pdf")
if reader.is_encrypted:
reader.decrypt("password")
except Exception as e:
print(f"Failed to decrypt: {e}")
```
### Corrupted PDFs
```bash
# Use qpdf to repair
qpdf --check corrupted.pdf
qpdf --replace-input corrupted.pdf
```
### Text Extraction Issues
```python
# Fallback to OCR for scanned PDFs
import pytesseract
from pdf2image import convert_from_path
def extract_text_with_ocr(pdf_path):
images = convert_from_path(pdf_path)
text = ""
for i, image in enumerate(images):
text += pytesseract.image_to_string(image)
return text
```
## License Information
- **pypdf**: BSD License
- **pdfplumber**: MIT License
- **pypdfium2**: Apache/BSD License
- **reportlab**: BSD License
- **poppler-utils**: GPL-2 License
- **qpdf**: Apache License
- **pdf-lib**: MIT License
- **pdfjs-dist**: Apache License

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from dataclasses import dataclass
import json
import sys
# Script to check that the `fields.json` file that Claude creates when analyzing PDFs
# does not have overlapping bounding boxes. See forms.md.
@dataclass
class RectAndField:
rect: list[float]
rect_type: str
field: dict
# Returns a list of messages that are printed to stdout for Claude to read.
def get_bounding_box_messages(fields_json_stream) -> list[str]:
messages = []
fields = json.load(fields_json_stream)
messages.append(f"Read {len(fields['form_fields'])} fields")
def rects_intersect(r1, r2):
disjoint_horizontal = r1[0] >= r2[2] or r1[2] <= r2[0]
disjoint_vertical = r1[1] >= r2[3] or r1[3] <= r2[1]
return not (disjoint_horizontal or disjoint_vertical)
rects_and_fields = []
for f in fields["form_fields"]:
rects_and_fields.append(RectAndField(f["label_bounding_box"], "label", f))
rects_and_fields.append(RectAndField(f["entry_bounding_box"], "entry", f))
has_error = False
for i, ri in enumerate(rects_and_fields):
# This is O(N^2); we can optimize if it becomes a problem.
for j in range(i + 1, len(rects_and_fields)):
rj = rects_and_fields[j]
if ri.field["page_number"] == rj.field["page_number"] and rects_intersect(ri.rect, rj.rect):
has_error = True
if ri.field is rj.field:
messages.append(f"FAILURE: intersection between label and entry bounding boxes for `{ri.field['description']}` ({ri.rect}, {rj.rect})")
else:
messages.append(f"FAILURE: intersection between {ri.rect_type} bounding box for `{ri.field['description']}` ({ri.rect}) and {rj.rect_type} bounding box for `{rj.field['description']}` ({rj.rect})")
if len(messages) >= 20:
messages.append("Aborting further checks; fix bounding boxes and try again")
return messages
if ri.rect_type == "entry":
if "entry_text" in ri.field:
font_size = ri.field["entry_text"].get("font_size", 14)
entry_height = ri.rect[3] - ri.rect[1]
if entry_height < font_size:
has_error = True
messages.append(f"FAILURE: entry bounding box height ({entry_height}) for `{ri.field['description']}` is too short for the text content (font size: {font_size}). Increase the box height or decrease the font size.")
if len(messages) >= 20:
messages.append("Aborting further checks; fix bounding boxes and try again")
return messages
if not has_error:
messages.append("SUCCESS: All bounding boxes are valid")
return messages
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: check_bounding_boxes.py [fields.json]")
sys.exit(1)
# Input file should be in the `fields.json` format described in forms.md.
with open(sys.argv[1]) as f:
messages = get_bounding_box_messages(f)
for msg in messages:
print(msg)

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import unittest
import json
import io
from check_bounding_boxes import get_bounding_box_messages
# Currently this is not run automatically in CI; it's just for documentation and manual checking.
class TestGetBoundingBoxMessages(unittest.TestCase):
def create_json_stream(self, data):
"""Helper to create a JSON stream from data"""
return io.StringIO(json.dumps(data))
def test_no_intersections(self):
"""Test case with no bounding box intersections"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 30]
},
{
"description": "Email",
"page_number": 1,
"label_bounding_box": [10, 40, 50, 60],
"entry_bounding_box": [60, 40, 150, 60]
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("SUCCESS" in msg for msg in messages))
self.assertFalse(any("FAILURE" in msg for msg in messages))
def test_label_entry_intersection_same_field(self):
"""Test intersection between label and entry of the same field"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 60, 30],
"entry_bounding_box": [50, 10, 150, 30] # Overlaps with label
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("FAILURE" in msg and "intersection" in msg for msg in messages))
self.assertFalse(any("SUCCESS" in msg for msg in messages))
def test_intersection_between_different_fields(self):
"""Test intersection between bounding boxes of different fields"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 30]
},
{
"description": "Email",
"page_number": 1,
"label_bounding_box": [40, 20, 80, 40], # Overlaps with Name's boxes
"entry_bounding_box": [160, 10, 250, 30]
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("FAILURE" in msg and "intersection" in msg for msg in messages))
self.assertFalse(any("SUCCESS" in msg for msg in messages))
def test_different_pages_no_intersection(self):
"""Test that boxes on different pages don't count as intersecting"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 30]
},
{
"description": "Email",
"page_number": 2,
"label_bounding_box": [10, 10, 50, 30], # Same coordinates but different page
"entry_bounding_box": [60, 10, 150, 30]
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("SUCCESS" in msg for msg in messages))
self.assertFalse(any("FAILURE" in msg for msg in messages))
def test_entry_height_too_small(self):
"""Test that entry box height is checked against font size"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 20], # Height is 10
"entry_text": {
"font_size": 14 # Font size larger than height
}
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("FAILURE" in msg and "height" in msg for msg in messages))
self.assertFalse(any("SUCCESS" in msg for msg in messages))
def test_entry_height_adequate(self):
"""Test that adequate entry box height passes"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 30], # Height is 20
"entry_text": {
"font_size": 14 # Font size smaller than height
}
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("SUCCESS" in msg for msg in messages))
self.assertFalse(any("FAILURE" in msg for msg in messages))
def test_default_font_size(self):
"""Test that default font size is used when not specified"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 20], # Height is 10
"entry_text": {} # No font_size specified, should use default 14
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("FAILURE" in msg and "height" in msg for msg in messages))
self.assertFalse(any("SUCCESS" in msg for msg in messages))
def test_no_entry_text(self):
"""Test that missing entry_text doesn't cause height check"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [60, 10, 150, 20] # Small height but no entry_text
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("SUCCESS" in msg for msg in messages))
self.assertFalse(any("FAILURE" in msg for msg in messages))
def test_multiple_errors_limit(self):
"""Test that error messages are limited to prevent excessive output"""
fields = []
# Create many overlapping fields
for i in range(25):
fields.append({
"description": f"Field{i}",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30], # All overlap
"entry_bounding_box": [20, 15, 60, 35] # All overlap
})
data = {"form_fields": fields}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
# Should abort after ~20 messages
self.assertTrue(any("Aborting" in msg for msg in messages))
# Should have some FAILURE messages but not hundreds
failure_count = sum(1 for msg in messages if "FAILURE" in msg)
self.assertGreater(failure_count, 0)
self.assertLess(len(messages), 30) # Should be limited
def test_edge_touching_boxes(self):
"""Test that boxes touching at edges don't count as intersecting"""
data = {
"form_fields": [
{
"description": "Name",
"page_number": 1,
"label_bounding_box": [10, 10, 50, 30],
"entry_bounding_box": [50, 10, 150, 30] # Touches at x=50
}
]
}
stream = self.create_json_stream(data)
messages = get_bounding_box_messages(stream)
self.assertTrue(any("SUCCESS" in msg for msg in messages))
self.assertFalse(any("FAILURE" in msg for msg in messages))
if __name__ == '__main__':
unittest.main()

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import sys
from pypdf import PdfReader
# Script for Claude to run to determine whether a PDF has fillable form fields. See forms.md.
reader = PdfReader(sys.argv[1])
if (reader.get_fields()):
print("This PDF has fillable form fields")
else:
print("This PDF does not have fillable form fields; you will need to visually determine where to enter data")

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import os
import sys
from pdf2image import convert_from_path
# Converts each page of a PDF to a PNG image.
def convert(pdf_path, output_dir, max_dim=1000):
images = convert_from_path(pdf_path, dpi=200)
for i, image in enumerate(images):
# Scale image if needed to keep width/height under `max_dim`
width, height = image.size
if width > max_dim or height > max_dim:
scale_factor = min(max_dim / width, max_dim / height)
new_width = int(width * scale_factor)
new_height = int(height * scale_factor)
image = image.resize((new_width, new_height))
image_path = os.path.join(output_dir, f"page_{i+1}.png")
image.save(image_path)
print(f"Saved page {i+1} as {image_path} (size: {image.size})")
print(f"Converted {len(images)} pages to PNG images")
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: convert_pdf_to_images.py [input pdf] [output directory]")
sys.exit(1)
pdf_path = sys.argv[1]
output_directory = sys.argv[2]
convert(pdf_path, output_directory)

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import json
import sys
from PIL import Image, ImageDraw
# Creates "validation" images with rectangles for the bounding box information that
# Claude creates when determining where to add text annotations in PDFs. See forms.md.
def create_validation_image(page_number, fields_json_path, input_path, output_path):
# Input file should be in the `fields.json` format described in forms.md.
with open(fields_json_path, 'r') as f:
data = json.load(f)
img = Image.open(input_path)
draw = ImageDraw.Draw(img)
num_boxes = 0
for field in data["form_fields"]:
if field["page_number"] == page_number:
entry_box = field['entry_bounding_box']
label_box = field['label_bounding_box']
# Draw red rectangle over entry bounding box and blue rectangle over the label.
draw.rectangle(entry_box, outline='red', width=2)
draw.rectangle(label_box, outline='blue', width=2)
num_boxes += 2
img.save(output_path)
print(f"Created validation image at {output_path} with {num_boxes} bounding boxes")
if __name__ == "__main__":
if len(sys.argv) != 5:
print("Usage: create_validation_image.py [page number] [fields.json file] [input image path] [output image path]")
sys.exit(1)
page_number = int(sys.argv[1])
fields_json_path = sys.argv[2]
input_image_path = sys.argv[3]
output_image_path = sys.argv[4]
create_validation_image(page_number, fields_json_path, input_image_path, output_image_path)

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import json
import sys
from pypdf import PdfReader
# Extracts data for the fillable form fields in a PDF and outputs JSON that
# Claude uses to fill the fields. See forms.md.
# This matches the format used by PdfReader `get_fields` and `update_page_form_field_values` methods.
def get_full_annotation_field_id(annotation):
components = []
while annotation:
field_name = annotation.get('/T')
if field_name:
components.append(field_name)
annotation = annotation.get('/Parent')
return ".".join(reversed(components)) if components else None
def make_field_dict(field, field_id):
field_dict = {"field_id": field_id}
ft = field.get('/FT')
if ft == "/Tx":
field_dict["type"] = "text"
elif ft == "/Btn":
field_dict["type"] = "checkbox" # radio groups handled separately
states = field.get("/_States_", [])
if len(states) == 2:
# "/Off" seems to always be the unchecked value, as suggested by
# https://opensource.adobe.com/dc-acrobat-sdk-docs/standards/pdfstandards/pdf/PDF32000_2008.pdf#page=448
# It can be either first or second in the "/_States_" list.
if "/Off" in states:
field_dict["checked_value"] = states[0] if states[0] != "/Off" else states[1]
field_dict["unchecked_value"] = "/Off"
else:
print(f"Unexpected state values for checkbox `${field_id}`. Its checked and unchecked values may not be correct; if you're trying to check it, visually verify the results.")
field_dict["checked_value"] = states[0]
field_dict["unchecked_value"] = states[1]
elif ft == "/Ch":
field_dict["type"] = "choice"
states = field.get("/_States_", [])
field_dict["choice_options"] = [{
"value": state[0],
"text": state[1],
} for state in states]
else:
field_dict["type"] = f"unknown ({ft})"
return field_dict
# Returns a list of fillable PDF fields:
# [
# {
# "field_id": "name",
# "page": 1,
# "type": ("text", "checkbox", "radio_group", or "choice")
# // Per-type additional fields described in forms.md
# },
# ]
def get_field_info(reader: PdfReader):
fields = reader.get_fields()
field_info_by_id = {}
possible_radio_names = set()
for field_id, field in fields.items():
# Skip if this is a container field with children, except that it might be
# a parent group for radio button options.
if field.get("/Kids"):
if field.get("/FT") == "/Btn":
possible_radio_names.add(field_id)
continue
field_info_by_id[field_id] = make_field_dict(field, field_id)
# Bounding rects are stored in annotations in page objects.
# Radio button options have a separate annotation for each choice;
# all choices have the same field name.
# See https://westhealth.github.io/exploring-fillable-forms-with-pdfrw.html
radio_fields_by_id = {}
for page_index, page in enumerate(reader.pages):
annotations = page.get('/Annots', [])
for ann in annotations:
field_id = get_full_annotation_field_id(ann)
if field_id in field_info_by_id:
field_info_by_id[field_id]["page"] = page_index + 1
field_info_by_id[field_id]["rect"] = ann.get('/Rect')
elif field_id in possible_radio_names:
try:
# ann['/AP']['/N'] should have two items. One of them is '/Off',
# the other is the active value.
on_values = [v for v in ann["/AP"]["/N"] if v != "/Off"]
except KeyError:
continue
if len(on_values) == 1:
rect = ann.get("/Rect")
if field_id not in radio_fields_by_id:
radio_fields_by_id[field_id] = {
"field_id": field_id,
"type": "radio_group",
"page": page_index + 1,
"radio_options": [],
}
# Note: at least on macOS 15.7, Preview.app doesn't show selected
# radio buttons correctly. (It does if you remove the leading slash
# from the value, but that causes them not to appear correctly in
# Chrome/Firefox/Acrobat/etc).
radio_fields_by_id[field_id]["radio_options"].append({
"value": on_values[0],
"rect": rect,
})
# Some PDFs have form field definitions without corresponding annotations,
# so we can't tell where they are. Ignore these fields for now.
fields_with_location = []
for field_info in field_info_by_id.values():
if "page" in field_info:
fields_with_location.append(field_info)
else:
print(f"Unable to determine location for field id: {field_info.get('field_id')}, ignoring")
# Sort by page number, then Y position (flipped in PDF coordinate system), then X.
def sort_key(f):
if "radio_options" in f:
rect = f["radio_options"][0]["rect"] or [0, 0, 0, 0]
else:
rect = f.get("rect") or [0, 0, 0, 0]
adjusted_position = [-rect[1], rect[0]]
return [f.get("page"), adjusted_position]
sorted_fields = fields_with_location + list(radio_fields_by_id.values())
sorted_fields.sort(key=sort_key)
return sorted_fields
def write_field_info(pdf_path: str, json_output_path: str):
reader = PdfReader(pdf_path)
field_info = get_field_info(reader)
with open(json_output_path, "w") as f:
json.dump(field_info, f, indent=2)
print(f"Wrote {len(field_info)} fields to {json_output_path}")
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: extract_form_field_info.py [input pdf] [output json]")
sys.exit(1)
write_field_info(sys.argv[1], sys.argv[2])

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import json
import sys
from pypdf import PdfReader, PdfWriter
from extract_form_field_info import get_field_info
# Fills fillable form fields in a PDF. See forms.md.
def fill_pdf_fields(input_pdf_path: str, fields_json_path: str, output_pdf_path: str):
with open(fields_json_path) as f:
fields = json.load(f)
# Group by page number.
fields_by_page = {}
for field in fields:
if "value" in field:
field_id = field["field_id"]
page = field["page"]
if page not in fields_by_page:
fields_by_page[page] = {}
fields_by_page[page][field_id] = field["value"]
reader = PdfReader(input_pdf_path)
has_error = False
field_info = get_field_info(reader)
fields_by_ids = {f["field_id"]: f for f in field_info}
for field in fields:
existing_field = fields_by_ids.get(field["field_id"])
if not existing_field:
has_error = True
print(f"ERROR: `{field['field_id']}` is not a valid field ID")
elif field["page"] != existing_field["page"]:
has_error = True
print(f"ERROR: Incorrect page number for `{field['field_id']}` (got {field['page']}, expected {existing_field['page']})")
else:
if "value" in field:
err = validation_error_for_field_value(existing_field, field["value"])
if err:
print(err)
has_error = True
if has_error:
sys.exit(1)
writer = PdfWriter(clone_from=reader)
for page, field_values in fields_by_page.items():
writer.update_page_form_field_values(writer.pages[page - 1], field_values, auto_regenerate=False)
# This seems to be necessary for many PDF viewers to format the form values correctly.
# It may cause the viewer to show a "save changes" dialog even if the user doesn't make any changes.
writer.set_need_appearances_writer(True)
with open(output_pdf_path, "wb") as f:
writer.write(f)
def validation_error_for_field_value(field_info, field_value):
field_type = field_info["type"]
field_id = field_info["field_id"]
if field_type == "checkbox":
checked_val = field_info["checked_value"]
unchecked_val = field_info["unchecked_value"]
if field_value != checked_val and field_value != unchecked_val:
return f'ERROR: Invalid value "{field_value}" for checkbox field "{field_id}". The checked value is "{checked_val}" and the unchecked value is "{unchecked_val}"'
elif field_type == "radio_group":
option_values = [opt["value"] for opt in field_info["radio_options"]]
if field_value not in option_values:
return f'ERROR: Invalid value "{field_value}" for radio group field "{field_id}". Valid values are: {option_values}'
elif field_type == "choice":
choice_values = [opt["value"] for opt in field_info["choice_options"]]
if field_value not in choice_values:
return f'ERROR: Invalid value "{field_value}" for choice field "{field_id}". Valid values are: {choice_values}'
return None
# pypdf (at least version 5.7.0) has a bug when setting the value for a selection list field.
# In _writer.py around line 966:
#
# if field.get(FA.FT, "/Tx") == "/Ch" and field_flags & FA.FfBits.Combo == 0:
# txt = "\n".join(annotation.get_inherited(FA.Opt, []))
#
# The problem is that for selection lists, `get_inherited` returns a list of two-element lists like
# [["value1", "Text 1"], ["value2", "Text 2"], ...]
# This causes `join` to throw a TypeError because it expects an iterable of strings.
# The horrible workaround is to patch `get_inherited` to return a list of the value strings.
# We call the original method and adjust the return value only if the argument to `get_inherited`
# is `FA.Opt` and if the return value is a list of two-element lists.
def monkeypatch_pydpf_method():
from pypdf.generic import DictionaryObject
from pypdf.constants import FieldDictionaryAttributes
original_get_inherited = DictionaryObject.get_inherited
def patched_get_inherited(self, key: str, default = None):
result = original_get_inherited(self, key, default)
if key == FieldDictionaryAttributes.Opt:
if isinstance(result, list) and all(isinstance(v, list) and len(v) == 2 for v in result):
result = [r[0] for r in result]
return result
DictionaryObject.get_inherited = patched_get_inherited
if __name__ == "__main__":
if len(sys.argv) != 4:
print("Usage: fill_fillable_fields.py [input pdf] [field_values.json] [output pdf]")
sys.exit(1)
monkeypatch_pydpf_method()
input_pdf = sys.argv[1]
fields_json = sys.argv[2]
output_pdf = sys.argv[3]
fill_pdf_fields(input_pdf, fields_json, output_pdf)

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import json
import sys
from pypdf import PdfReader, PdfWriter
from pypdf.annotations import FreeText
# Fills a PDF by adding text annotations defined in `fields.json`. See forms.md.
def transform_coordinates(bbox, image_width, image_height, pdf_width, pdf_height):
"""Transform bounding box from image coordinates to PDF coordinates"""
# Image coordinates: origin at top-left, y increases downward
# PDF coordinates: origin at bottom-left, y increases upward
x_scale = pdf_width / image_width
y_scale = pdf_height / image_height
left = bbox[0] * x_scale
right = bbox[2] * x_scale
# Flip Y coordinates for PDF
top = pdf_height - (bbox[1] * y_scale)
bottom = pdf_height - (bbox[3] * y_scale)
return left, bottom, right, top
def fill_pdf_form(input_pdf_path, fields_json_path, output_pdf_path):
"""Fill the PDF form with data from fields.json"""
# `fields.json` format described in forms.md.
with open(fields_json_path, "r") as f:
fields_data = json.load(f)
# Open the PDF
reader = PdfReader(input_pdf_path)
writer = PdfWriter()
# Copy all pages to writer
writer.append(reader)
# Get PDF dimensions for each page
pdf_dimensions = {}
for i, page in enumerate(reader.pages):
mediabox = page.mediabox
pdf_dimensions[i + 1] = [mediabox.width, mediabox.height]
# Process each form field
annotations = []
for field in fields_data["form_fields"]:
page_num = field["page_number"]
# Get page dimensions and transform coordinates.
page_info = next(p for p in fields_data["pages"] if p["page_number"] == page_num)
image_width = page_info["image_width"]
image_height = page_info["image_height"]
pdf_width, pdf_height = pdf_dimensions[page_num]
transformed_entry_box = transform_coordinates(
field["entry_bounding_box"],
image_width, image_height,
pdf_width, pdf_height
)
# Skip empty fields
if "entry_text" not in field or "text" not in field["entry_text"]:
continue
entry_text = field["entry_text"]
text = entry_text["text"]
if not text:
continue
font_name = entry_text.get("font", "Arial")
font_size = str(entry_text.get("font_size", 14)) + "pt"
font_color = entry_text.get("font_color", "000000")
# Font size/color seems to not work reliably across viewers:
# https://github.com/py-pdf/pypdf/issues/2084
annotation = FreeText(
text=text,
rect=transformed_entry_box,
font=font_name,
font_size=font_size,
font_color=font_color,
border_color=None,
background_color=None,
)
annotations.append(annotation)
# page_number is 0-based for pypdf
writer.add_annotation(page_number=page_num - 1, annotation=annotation)
# Save the filled PDF
with open(output_pdf_path, "wb") as output:
writer.write(output)
print(f"Successfully filled PDF form and saved to {output_pdf_path}")
print(f"Added {len(annotations)} text annotations")
if __name__ == "__main__":
if len(sys.argv) != 4:
print("Usage: fill_pdf_form_with_annotations.py [input pdf] [fields.json] [output pdf]")
sys.exit(1)
input_pdf = sys.argv[1]
fields_json = sys.argv[2]
output_pdf = sys.argv[3]
fill_pdf_form(input_pdf, fields_json, output_pdf)