commit 1916d21d59fc90b2923c23764da8ff1e735ac200
Author: Zhongwei Li <lizhongwei.nkcs@gmail.com>
Date:   Sun Nov 30 08:41:27 2025 +0800

    Initial commit

diff --git a/.claude-plugin/plugin.json b/.claude-plugin/plugin.json
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+{
+  "name": "research-task",
+  "description": "Deep Dive Research",
+  "version": "1.0.0",
+  "author": {
+    "name": "Matthew Pazaryna",
+    "email": "[email protected]"
+  },
+  "commands": [
+    "./commands"
+  ]
+}
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diff --git a/README.md b/README.md
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+# research-task
+
+Deep Dive Research
diff --git a/commands/task.md b/commands/task.md
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+---
+description: Perform research for a specific task and return structured findings (called by issue agent)
+category: dev
+difficulty: beginner
+estimated_time: instant
+allowed-tools: WebFetch, WebSearch, Read, Bash
+version: 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):
+
+```markdown
+## 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}]({URL})
+- [{Title}]({URL})
+```
+
+---
+
+## 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)
diff --git a/plugin.lock.json b/plugin.lock.json
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+++ b/plugin.lock.json
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