gh-mpazaryna-claude-toolkit-plugins-research-task/commands/task.md

description, category, difficulty, estimated_time, allowed-tools, version

description	category	difficulty	estimated_time	allowed-tools	version
Perform research for a specific task and return structured findings (called by issue agent)	dev	beginner	instant	WebFetch, WebSearch, Read, Bash	1.0.0

Research Task Agent

Specialized command for performing research on technical topics. Returns structured findings to the calling agent.

Variables

RESEARCH_QUESTIONS: (required - list of questions to answer) TASK_CONTEXT: (required - why this research matters) SUGGESTED_APPROACH: (optional - where to look)

Workflow

Step 1: Understand Research Scope

Parse the research questions:

• Primary questions (must answer)
• Secondary questions (nice to answer)
• Context (why it matters)

Step 2: Identify Information Sources

Based on research questions, determine sources:

• Official documentation (e.g., Apple developer docs, API references)
• Technical articles (e.g., developer blogs, Medium)
• Code examples (e.g., GitHub, Stack Overflow)
• Community discussions (e.g., forums, Reddit)
• Academic papers (if deep technical topic)

Step 3: Gather Information

For each source type:

Documentation:

• Use WebFetch for official docs
• Extract key concepts, APIs, limitations
• Note version/compatibility requirements

Code Examples:

• Search GitHub for relevant implementations
• Look for patterns and best practices
• Identify common pitfalls

Community Knowledge:

• WebSearch for recent discussions
• Find real-world experiences
• Identify gotchas and workarounds

Step 4: Synthesize Findings

Organize findings by research question:

For each question:

• Answer: Direct answer if found
• Details: Supporting information
• Sources: Where information came from
• Confidence: How certain (high/medium/low)
• Caveats: Limitations or conditions

Step 5: Create Recommendations

Based on findings:

• Recommended approach: What to do
• Rationale: Why this approach
• Alternatives: Backup options
• Risks: What to watch out for
• Next steps: How to proceed

Step 6: Return Structured Findings

Output format (returned to calling agent):

## Research Findings for: {TASK_TITLE}

### Question 1: {QUESTION}
**Answer**: {DIRECT_ANSWER}

**Details**:
{SUPPORTING_INFORMATION}

**Sources**:
- {SOURCE_1}
- {SOURCE_2}

**Confidence**: High | Medium | Low
**Caveats**: {LIMITATIONS}

---

### Question 2: {QUESTION}
[Same structure]

---

## Recommendations

### Approach
{WHAT_TO_DO}

### Rationale
{WHY}

### Risks
- {RISK_1}: {mitigation}
- {RISK_2}: {mitigation}

### Alternatives
1. {ALTERNATIVE_1}: {when to use}
2. {ALTERNATIVE_2}: {when to use}

## Code Examples

```{language}
{EXAMPLE_CODE}

Open Questions

Unanswered questions:

• {OPEN_Q1}
• {OPEN_Q2}

References

• {Title}
• {Title}

---

## Example Invocation

Called by `/research-task` when task type = research:

Input:

• RESEARCH_QUESTIONS:

  • "What NaturalLanguage framework APIs are available?"
  • "Can NER extract job titles and companies?"
  • "What's the accuracy for career narratives?"

• TASK_CONTEXT: "Stage 1 TELL requires extracting career events from CV text"

• SUGGESTED_APPROACH: "Check Apple docs, test with sample CV text"

Output: Structured findings with answers, code examples, recommendations

---

## Design Principles

1. **Single Responsibility**: Only does research, doesn't write files
2. **Returns Data**: Outputs findings as structured text to calling agent
3. **Evidence-Based**: All claims backed by sources
4. **Actionable**: Provides clear recommendations
5. **Honest**: Admits when information not found or uncertain

---

## Notes

- This agent is typically called by `/paz:plan:issue`, not directly by user
- If called directly, will still work and output findings to console
- Uses WebFetch for documentation, WebSearch for discussions
- May read local files if researching internal codebase
- Research is cached naturally by WebFetch (15-minute cache)