zhongwei/gh-dhruvbaldawa-ccconfigs-essentials

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Zhongwei Li 977fbf5872 Initial commit

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description

description
Research blockers or questions using specialized research agents

Research

Research specific blocker or question using specialized research agents and MCP tools.

Usage

/research experimental/.plans/user-auth/implementation/003-jwt.md  # Stuck task
/research "How to implement rate limiting with Redis?"            # General question
/research "Best practices for writing technical blog posts"       # Writing research

Your Task

Research: "${{{ARGS}}}"

Step 1: Analyze & Select Agents

${isTaskFile ? 'Read task file to understand blocker context.' : 'Analyze question to determine approach.'}

Research Need	Agent Combination
New technology/patterns	breadth + technical
Specific error/issue	depth + technical
API/library integration	technical + depth
Best practices comparison	breadth + depth

Agents available:

research-breadth (haiku) - WebSearch → Parallel Search → Perplexity: industry trends, consensus, multiple perspectives
research-depth (haiku) - WebFetch → Parallel Search: specific URLs, implementations, case studies, gotchas
research-technical (haiku) - Context7: official docs, API signatures, types, configs

Step 2: Launch Agents in Parallel

Use Promise.all to launch 2-3 agents:

await Promise.all([
  Task({
    subagent_type: 'research-breadth',  // or 'research-depth' or 'research-technical'
    model: 'haiku',
    description: 'Brief agent description',
    prompt: `Research: "${{{ARGS}}}"

    Focus areas and guidance for this agent.
    Specify which MCP tool to use.
    Expected output format.`
  }),

  Task({
    subagent_type: 'research-technical',
    model: 'haiku',
    description: 'Brief agent description',
    prompt: `Research official docs for: "${{{ARGS}}}"

    Focus areas and guidance for this agent.`
  })
]);

Step 3: Synthesize Findings

Use research-synthesis skill to:

Consolidate findings by theme, identify consensus, note contradictions
Narrativize into story (not bullet dumps): "Industry uses X (breadth), via Y API (technical), as shown by Z (depth)"
Maintain source attribution (note which agent provided insights)
Identify gaps (unanswered questions, disagreements)
Extract actions (implementation path, code/configs, risks)

${isTaskFile ? `

Step 4: Update Task File

Append research findings to task file:

```bash cat >> "$task_file" <<EOF

research findings:

[Agent]: [key insights with sources]
[Agent]: [key insights with sources]

resolution: [Concrete path forward]

next steps: [Specific actions] EOF ```

Update status from STUCK to Pending if blocker resolved. ` : ''}

Output Format

For Stuck Tasks

✅ Research Complete

Task: 003-jwt.md
Blocker: [Description]

Agents Used: breadth (industry patterns), technical (official docs)

Key Findings:
1. **Agent 1**: [Key insight with source]
2. **Agent 2**: [Key insight with source]

Resolution: [Concrete recommendation]

Updated task: Findings in Notes, LLM Prompt updated, Status: STUCK → Pending

Next: Resume implementation with /implement-plan <project>

For General Questions

✅ Research Complete

Question: [Original question]

Agents Used: [List with focus areas]

Synthesis:
[Narrative combining insights from all agents with source attribution]

Recommendation: [What to do with rationale]

Alternative: [If applicable]

Sources: [Links with descriptions]

Key Points

Launch agents in parallel (Promise.all) for speed
Use research-synthesis skill to consolidate (narrative, not lists)
Maintain source attribution (link claims to agents/sources)
For tasks: update file with findings and change status if resolved
See essentials/skills/research-synthesis/reference/multi-agent-invocation.md for detailed patterns

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