
deep-research
by 5dlabs
Cognitive Task Orchestrator - GitOps on Bare Metal or Cloud for AI Agents
SKILL.md
name: deep-research description: Deep technical research using Firecrawl Agent for autonomous web investigation, competitive analysis, and implementation pattern discovery. agents: [morgan, cleo, rex, nova, blaze, grizz] triggers: [research, competitive, examples, how do others, best practices, like X, similar to, compare, industry standard] globs:
- "**/prd*.md"
- "**/prd*.txt"
- "/intake/"
- "/docs/"
Deep Research Skill
Perform comprehensive technical research using the Firecrawl Agent API for autonomous web investigation. Use this skill when tasks require understanding external patterns, competitive analysis, or finding implementation examples.
When to Trigger Deep Research
Scan for these patterns in PRDs and task requirements:
| Pattern | Example | Research Action |
|---|---|---|
| "like X" references | "authentication like Auth0" | Research how Auth0 implements it |
| "similar to" comparisons | "similar to Stripe webhooks" | Study Stripe's webhook patterns |
| Competitive mentions | "compete with Notion" | Analyze Notion's architecture |
| Best practices requests | "follow industry standards" | Survey how leaders solve it |
| Unfamiliar tech | "use CRDT for sync" | Find CRDT implementation examples |
| "how do others" questions | "how do others handle this?" | Multi-source investigation |
Research Protocol
Step 1: Identify Research Needs
Before generating tasks, scan the PRD for:
- External references - Named products, services, or standards
- Comparative requirements - "better than", "like", "similar to"
- Technical unknowns - Unfamiliar patterns or technologies
- Best practice requests - "industry standard", "production-ready"
Step 2: Choose the Right Tool
| Research Type | Tool | Why |
|---|---|---|
| Competitive analysis | firecrawl_agent | Multi-site autonomous research |
| Implementation patterns | octocode_githubSearchCode | Searches actual production code across GitHub |
| Library documentation | context7 | Official, structured docs |
| Code examples from GitHub | octocode_githubSearchCode | Real production code with semantic search |
| How major projects solve X | octocode_githubSearchRepositories | Find reference implementations |
| PR discussions/fixes | octocode_githubSearchPullRequests | Learn how issues were resolved |
| Specific page content | firecrawl_scrape | Known URL, faster |
Step 3: Execute Research
Using Firecrawl Agent
firecrawl_agent({
prompt: "YOUR RESEARCH QUESTION - be specific",
schema: {
"type": "object",
"properties": {
"findings": {
"type": "array",
"items": {
"type": "object",
"properties": {
"source": { "type": "string" },
"approach": { "type": "string" },
"details": { "type": "string" },
"tradeoffs": { "type": "string" }
}
}
},
"recommendation": { "type": "string" }
}
}
})
Step 4: Structure Output
Always format research findings as:
## Research: [Topic]
### Summary
[2-3 sentence key takeaway]
### Findings
| Source | Approach | Key Details |
|--------|----------|-------------|
| Auth0 | JWT + refresh rotation | 15min access, 7d refresh |
| Clerk | Session tokens | Server-side validation |
### Recommendation
[How this applies to the current task]
### Sources
- [URL 1] - Description
- [URL 2] - Description
Common Research Patterns
Competitive Analysis
When PRD mentions competitors or "like X":
firecrawl_agent({
prompt: "Compare how [Competitor A], [Competitor B], and [Competitor C] implement [feature]. Focus on [specific aspects from PRD].",
schema: {
"type": "object",
"properties": {
"providers": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"approach": { "type": "string" },
"strengths": { "type": "string" },
"weaknesses": { "type": "string" }
}
}
},
"recommendation": { "type": "string" }
}
}
})
Implementation Patterns
When PRD requires unfamiliar technology:
firecrawl_agent({
prompt: "Find production examples of [technology] being used for [use case]. Include code patterns, gotchas, and performance considerations.",
schema: {
"type": "object",
"properties": {
"examples": {
"type": "array",
"items": {
"type": "object",
"properties": {
"source": { "type": "string" },
"pattern": { "type": "string" },
"code_example": { "type": "string" },
"gotchas": { "type": "string" }
}
}
}
}
}
})
Architecture Research
When designing new systems:
firecrawl_agent({
prompt: "What architectures do major [domain] platforms use for [requirement]? Compare approaches from [Company A], [Company B], etc.",
schema: {
"type": "object",
"properties": {
"architectures": {
"type": "array",
"items": {
"type": "object",
"properties": {
"company": { "type": "string" },
"architecture": { "type": "string" },
"scale": { "type": "string" },
"tradeoffs": { "type": "string" }
}
}
}
}
}
})
Best Practices
When PRD requests "industry standard" approaches:
firecrawl_agent({
prompt: "What are current best practices for [topic] in [year]? Focus on [specific requirements]. Include examples from production systems."
})
Integrating Research into Tasks
Research findings should be embedded in task details fields:
{
"id": "5",
"title": "Nova: Implement Refresh Token Rotation",
"agentHint": "nova",
"details": "## Requirements\nImplement refresh token rotation for session management.\n\n## Research Findings\nBased on competitive analysis:\n- Auth0: 15min access tokens, 7-day refresh tokens with rotation\n- Clerk: Session-based with server validation\n- Supabase: JWT with configurable expiry\n\n## Recommended Approach\nFollow Auth0 pattern with:\n- 15-minute access token lifetime\n- 7-day refresh token with single-use rotation\n- Revocation on suspicious activity\n\n## Code Signatures\n```typescript\nexport const refreshToken = Effect.gen(function* () {\n // Implementation based on research\n})\n```"
}
Cost Management
Firecrawl Agent pricing is dynamic. Optimize costs:
- Be specific - Vague prompts cost more
- Use schemas - Structured output reduces processing
- Provide URLs when known - Narrows search scope
- Batch related questions - One comprehensive query vs multiple small ones
When NOT to Use Deep Research
- Library docs exist in Context7 - Use
context7instead - You know the exact URL - Use
firecrawl_scrape - Simple factual lookup - Use
firecrawl_search - Code examples from GitHub repos - Use
octocode_githubSearchCode(semantic search across repos) - How React/major OSS projects do X - Use OctoCode to search their source
OctoCode Integration
For implementation pattern research, combine Firecrawl (web) with OctoCode (code):
# 1. Research how competitors approach the problem (web)
firecrawl_agent({ prompt: "How does Auth0 implement refresh token rotation?" })
# 2. Find actual implementations in open source (code)
octocode_githubSearchCode({
query: "refresh token rotation",
language: "typescript",
stars: ">500"
})
# 3. Get library docs for the chosen approach
context7_get_library_docs({ libraryId: "/better-auth/better-auth", topic: "refresh tokens" })
Research Checklist
Before finalizing research-informed tasks:
- All "like X" and "similar to" references researched
- Competitive mentions analyzed
- Unfamiliar technologies investigated
- Research findings embedded in relevant task details
- Sources cited for verification
- Recommendations align with PRD requirements
Score
Total Score
Based on repository quality metrics
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
1ヶ月以内に更新
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
Reviews
Reviews coming soon


