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https://gitee.com/wanwujie/deer-flow
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* feat: add agent management functionality with creation, editing, and deletion * feat: enhance agent creation and chat experience - Added AgentWelcome component to display agent description on new thread creation. - Improved agent name validation with availability check during agent creation. - Updated NewAgentPage to handle agent creation flow more effectively, including enhanced error handling and user feedback. - Refactored chat components to streamline message handling and improve user experience. - Introduced new bootstrap skill for personalized onboarding conversations, including detailed conversation phases and a structured SOUL.md template. - Updated localization files to reflect new features and error messages. - General code cleanup and optimizations across various components and hooks. * Refactor workspace layout and agent management components - Updated WorkspaceLayout to use useLayoutEffect for sidebar state initialization. - Removed unused AgentFormDialog and related edit functionality from AgentCard. - Introduced ArtifactTrigger component to manage artifact visibility. - Enhanced ChatBox to handle artifact selection and display. - Improved message list rendering logic to avoid loading states. - Updated localization files to remove deprecated keys and add new translations. - Refined hooks for local settings and thread management to improve performance and clarity. - Added temporal awareness guidelines to deep research skill documentation. * feat: refactor chat components and introduce thread management hooks * feat: improve artifact file detail preview logic and clean up console logs * feat: refactor lead agent creation logic and improve logging details * feat: validate agent name format and enhance error handling in agent setup * feat: simplify thread search query by removing unnecessary metadata * feat: update query key in useDeleteThread and useRenameThread for consistency * feat: add isMock parameter to thread and artifact handling for improved testing * fix: reorder import of setup_agent for consistency in builtins module * feat: append mock parameter to thread links in CaseStudySection for testing purposes * fix: update load_agent_soul calls to use cfg.name for improved clarity * fix: update date format in apply_prompt_template for consistency * feat: integrate isMock parameter into artifact content loading for enhanced testing * docs: add license section to SKILL.md for clarity and attribution * feat(agent): enhance model resolution and agent configuration handling * chore: remove unused import of _resolve_model_name from agents * feat(agent): remove unused field * fix(agent): set default value for requested_model_name in _resolve_model_name function * feat(agent): update get_available_tools call to handle optional agent_config and improve middleware function signature --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
199 lines
7.7 KiB
Markdown
199 lines
7.7 KiB
Markdown
---
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name: deep-research
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description: Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.
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---
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# Deep Research Skill
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## Overview
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This skill provides a systematic methodology for conducting thorough web research. **Load this skill BEFORE starting any content generation task** to ensure you gather sufficient information from multiple angles, depths, and sources.
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## When to Use This Skill
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**Always load this skill when:**
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### Research Questions
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- User asks "what is X", "explain X", "research X", "investigate X"
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- User wants to understand a concept, technology, or topic in depth
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- The question requires current, comprehensive information from multiple sources
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- A single web search would be insufficient to answer properly
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### Content Generation (Pre-research)
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- Creating presentations (PPT/slides)
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- Creating frontend designs or UI mockups
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- Writing articles, reports, or documentation
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- Producing videos or multimedia content
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- Any content that requires real-world information, examples, or current data
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## Core Principle
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**Never generate content based solely on general knowledge.** The quality of your output directly depends on the quality and quantity of research conducted beforehand. A single search query is NEVER enough.
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## Research Methodology
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### Phase 1: Broad Exploration
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Start with broad searches to understand the landscape:
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1. **Initial Survey**: Search for the main topic to understand the overall context
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2. **Identify Dimensions**: From initial results, identify key subtopics, themes, angles, or aspects that need deeper exploration
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3. **Map the Territory**: Note different perspectives, stakeholders, or viewpoints that exist
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Example:
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```
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Topic: "AI in healthcare"
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Initial searches:
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- "AI healthcare applications 2024"
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- "artificial intelligence medical diagnosis"
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- "healthcare AI market trends"
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Identified dimensions:
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- Diagnostic AI (radiology, pathology)
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- Treatment recommendation systems
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- Administrative automation
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- Patient monitoring
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- Regulatory landscape
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- Ethical considerations
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```
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### Phase 2: Deep Dive
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For each important dimension identified, conduct targeted research:
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1. **Specific Queries**: Search with precise keywords for each subtopic
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2. **Multiple Phrasings**: Try different keyword combinations and phrasings
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3. **Fetch Full Content**: Use `web_fetch` to read important sources in full, not just snippets
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4. **Follow References**: When sources mention other important resources, search for those too
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Example:
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```
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Dimension: "Diagnostic AI in radiology"
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Targeted searches:
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- "AI radiology FDA approved systems"
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- "chest X-ray AI detection accuracy"
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- "radiology AI clinical trials results"
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Then fetch and read:
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- Key research papers or summaries
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- Industry reports
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- Real-world case studies
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```
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### Phase 3: Diversity & Validation
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Ensure comprehensive coverage by seeking diverse information types:
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| Information Type | Purpose | Example Searches |
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|-----------------|---------|------------------|
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| **Facts & Data** | Concrete evidence | "statistics", "data", "numbers", "market size" |
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| **Examples & Cases** | Real-world applications | "case study", "example", "implementation" |
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| **Expert Opinions** | Authority perspectives | "expert analysis", "interview", "commentary" |
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| **Trends & Predictions** | Future direction | "trends 2024", "forecast", "future of" |
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| **Comparisons** | Context and alternatives | "vs", "comparison", "alternatives" |
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| **Challenges & Criticisms** | Balanced view | "challenges", "limitations", "criticism" |
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### Phase 4: Synthesis Check
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Before proceeding to content generation, verify:
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- [ ] Have I searched from at least 3-5 different angles?
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- [ ] Have I fetched and read the most important sources in full?
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- [ ] Do I have concrete data, examples, and expert perspectives?
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- [ ] Have I explored both positive aspects and challenges/limitations?
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- [ ] Is my information current and from authoritative sources?
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**If any answer is NO, continue researching before generating content.**
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## Search Strategy Tips
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### Effective Query Patterns
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```
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# Be specific with context
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❌ "AI trends"
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✅ "enterprise AI adoption trends 2024"
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# Include authoritative source hints
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"[topic] research paper"
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"[topic] McKinsey report"
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"[topic] industry analysis"
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# Search for specific content types
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"[topic] case study"
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"[topic] statistics"
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"[topic] expert interview"
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# Use temporal qualifiers — always use the ACTUAL current year from <current_date>
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"[topic] 2026" # ← replace with real current year, never hardcode a past year
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"[topic] latest"
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"[topic] recent developments"
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```
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### Temporal Awareness
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**Always check `<current_date>` in your context before forming ANY search query.**
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`<current_date>` gives you the full date: year, month, day, and weekday (e.g. `2026-02-28, Saturday`). Use the right level of precision depending on what the user is asking:
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| User intent | Temporal precision needed | Example query |
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|---|---|---|
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| "today / this morning / just released" | **Month + Day** | `"tech news February 28 2026"` |
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| "this week" | **Week range** | `"technology releases week of Feb 24 2026"` |
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| "recently / latest / new" | **Month** | `"AI breakthroughs February 2026"` |
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| "this year / trends" | **Year** | `"software trends 2026"` |
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**Rules:**
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- When the user asks about "today" or "just released", use **month + day + year** in your search queries to get same-day results
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- Never drop to year-only when day-level precision is needed — `"tech news 2026"` will NOT surface today's news
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- Try multiple phrasings: numeric form (`2026-02-28`), written form (`February 28 2026`), and relative terms (`today`, `this week`) across different queries
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❌ User asks "what's new in tech today" → searching `"new technology 2026"` → misses today's news
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✅ User asks "what's new in tech today" → searching `"new technology February 28 2026"` + `"tech news today Feb 28"` → gets today's results
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### When to Use web_fetch
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Use `web_fetch` to read full content when:
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- A search result looks highly relevant and authoritative
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- You need detailed information beyond the snippet
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- The source contains data, case studies, or expert analysis
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- You want to understand the full context of a finding
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### Iterative Refinement
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Research is iterative. After initial searches:
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1. Review what you've learned
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2. Identify gaps in your understanding
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3. Formulate new, more targeted queries
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4. Repeat until you have comprehensive coverage
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## Quality Bar
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Your research is sufficient when you can confidently answer:
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- What are the key facts and data points?
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- What are 2-3 concrete real-world examples?
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- What do experts say about this topic?
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- What are the current trends and future directions?
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- What are the challenges or limitations?
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- What makes this topic relevant or important now?
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## Common Mistakes to Avoid
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- ❌ Stopping after 1-2 searches
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- ❌ Relying on search snippets without reading full sources
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- ❌ Searching only one aspect of a multi-faceted topic
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- ❌ Ignoring contradicting viewpoints or challenges
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- ❌ Using outdated information when current data exists
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- ❌ Starting content generation before research is complete
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## Output
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After completing research, you should have:
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1. A comprehensive understanding of the topic from multiple angles
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2. Specific facts, data points, and statistics
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3. Real-world examples and case studies
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4. Expert perspectives and authoritative sources
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5. Current trends and relevant context
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**Only then proceed to content generation**, using the gathered information to create high-quality, well-informed content.
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