7.5 KiB
name, description
| name | description |
|---|---|
| research-orchestration | Use when brainstorming completes and user selects research-first option - manages parallel research subagents (up to 4) across codebase, library docs, web, and GitHub sources, synthesizing findings and auto-saving to memory before planning |
Research Orchestration
Use this skill to manage parallel research subagents and synthesize findings from multiple sources.
When to Use
After brainstorming completes and user selects "B) research first" option.
Selection Algorithm
Default Selection
Based on brainstorm context, intelligently select researchers:
serena-explorer [✓ ALWAYS]
- Always need codebase understanding
- No keywords required - default ON
context7-researcher [✓ if library mentioned]
- Select if: new library, framework, official docs needed
- Keywords: "using [library]", "integrate [framework]", "best practices for [tool]"
- Example: "using React hooks" → ON
web-researcher [✓ if patterns mentioned]
- Select if: best practices, tutorials, modern approaches, expert opinions
- Keywords: "industry standard", "common pattern", "how to", "best approach"
- Example: "authentication best practices" → ON
github-researcher [☐ usually OFF]
- Select if: known issues, community solutions, similar features, troubleshooting
- Keywords: "GitHub issue", "others solved", "similar to [project]", "known problems"
- Example: "known issues with SSR" → ON
User Presentation
Present recommendations with context:
Based on the brainstorm, I recommend these researchers:
[✓] Codebase (serena-explorer)
→ Understand current architecture and integration points
[✓] Library docs (context7-researcher)
→ React hooks patterns and official recommendations
[✓] Web (web-researcher)
→ Authentication best practices and security patterns
[ ] GitHub (github-researcher)
→ Not needed unless we hit specific issues
Adjust selection? (Y/n)
If Y: Interactive toggle
Toggle researchers: (C)odebase (L)ibrary (W)eb (G)itHub (D)one
User input: L G D
Result: Toggled OFF context7-researcher, ON github-researcher, Done
If n: Use defaults and proceed
Spawning Subagents
Run up to 4 in parallel using Task tool:
// Spawn all selected researchers in parallel
const results = await Promise.all([
// Always spawn serena-explorer
Task({
subagent_type: "serena-explorer",
description: "Explore codebase architecture",
prompt: `
Analyze the current codebase for ${feature} implementation.
Find:
- Current architecture relevant to ${feature}
- Similar existing implementations we can learn from
- Integration points where ${feature} should hook in
- Patterns used in similar features
Provide all findings with file:line references.
`
}),
// Conditionally spawn context7-researcher
...(useContext7 ? [Task({
subagent_type: "context7-researcher",
description: "Research library documentation",
prompt: `
Research official documentation for ${libraries}.
Find:
- Recommended patterns for ${useCase}
- API best practices and examples
- Security considerations
- Performance recommendations
Include Context7 IDs, benchmark scores, and code examples.
`
})] : []),
// Conditionally spawn web-researcher
...(useWeb ? [Task({
subagent_type: "web-researcher",
description: "Research best practices",
prompt: `
Search for ${topic} best practices and expert opinions.
Find:
- Industry standard approaches for ${useCase}
- Recent articles (2024-2025) on ${topic}
- Expert recommendations with rationale
- Common gotchas and solutions
Cite sources with authority assessment and publication dates.
`
})] : []),
// Conditionally spawn github-researcher
...(useGithub ? [Task({
subagent_type: "github-researcher",
description: "Research GitHub issues/PRs",
prompt: `
Search GitHub for ${topic} issues and solutions.
Find:
- Closed issues related to ${problem}
- Merged PRs implementing ${feature}
- Community discussions on ${topic}
- Known gotchas and workarounds
Focus on ${relevantRepos} repositories.
Provide issue links, status, and consensus solutions.
`
})] : [])
])
Key points:
- All spawned in single Task call block (parallel execution)
- Each has specific prompt tailored to feature context
- Prompts reference brainstorm decisions
- Results returned when all complete
Synthesis
After all subagents complete, synthesize findings:
Structure
# Research: ${feature-name}
## Brainstorm Summary
${brief-summary-of-brainstorm-decisions}
## Codebase Findings (serena-explorer)
### Current Architecture
- **${component}:** `${file}:${line}`
- ${description}
### Similar Implementations
- **${existing-feature}:** `${file}:${line}`
- ${pattern-used}
- ${why-relevant}
### Integration Points
- **${location}:** `${file}:${line}`
- ${how-to-hook-in}
## Library Documentation (context7-researcher)
### ${Library-Name}
**Context7 ID:** ${id}
**Benchmark Score:** ${score}
**Relevant APIs:**
- **${api-name}:** ${description}
```${lang}
${code-example}
Best Practices:
- ${practice-1}
- ${practice-2}
Web Research (web-researcher)
${Topic}
Source: ${author} - "${title}" (${date}) Authority: ${stars} (${justification}) URL: ${url}
Key Recommendations:
-
${recommendation}
"${quote}"
- ${implementation-detail}
Trade-offs:
- ${trade-off-1}
- ${trade-off-2}
GitHub Research (github-researcher)
${Issue-Topic}
Source: ${repo}#${number} (${status}) URL: ${url}
Problem: ${description}
Solution:
${code-example}
Caveats:
- ${caveat-1}
- ${caveat-2}
Synthesis
Recommended Approach
Based on all research, recommend ${approach} because:
- Codebase fit: ${how-it-fits-existing-patterns}
- Library support: ${official-patterns-available}
- Industry proven: ${expert-consensus}
- Community validated: ${github-evidence}
Key Decisions
- ${decision-1}: ${rationale}
- ${decision-2}: ${rationale}
Risks & Mitigations
- Risk: ${risk}
- Mitigation: ${mitigation}
Next Steps
Ready to write implementation plan with this research context.
## Auto-Save
After synthesis completes, automatically save to memory:
```typescript
// Use state-persistence skill
await saveResearchMemory({
feature: extractFeatureName(brainstorm),
content: synthesizedResearch,
type: "research"
})
Filename: YYYY-MM-DD-${feature-name}-research.md
Location: Serena MCP memory (via write_memory tool)
Handoff
After save completes, report to user:
Research complete and saved to memory: ${filename}
I've synthesized findings from ${count} sources:
- Codebase: ${summary-of-serena-findings}
- Library docs: ${summary-of-context7-findings}
- Web: ${summary-of-web-findings}
- GitHub: ${summary-of-github-findings}
Key recommendation: ${one-sentence-approach}
Ready to write the implementation plan with this research context.
Then invoke writing-plans skill automatically.
Error Handling
If subagent fails:
- Continue with other subagents
- Note missing research in synthesis
- Offer to re-run failed researcher
If no results found:
- Note in synthesis
- Don't block workflow
- Proceed with available research
If all subagents fail:
- Report failure
- Offer to proceed without research
- User can retry or continue to planning