kaimingwan/oh-my-kiro/omk-research

Claude Code and agent skill by kaimingwan/oh-my-kiro

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Checking that the SKILL.md file is still in the repository.

Multi-level research: built-in knowledge → web search → Tavily deep research API. Trigger when user says 'research', 'investigate', 'find out', 'compare', 'what is', 'how does X work', 'competitive analysis', 'market research', '@research', or needs information beyond the current codebase and knowledge base.

Install

npx vskill@latest install kaimingwan/oh-my-kiro/omk-research

SKILL.md

Trigger Examples

  • "@research AutoMQ vs Confluent 对比"
  • "帮我调研一下这个库怎么用"
  • "find out how competitors handle this"
  • "what's the best practice for X in 2026?"
  • "compare these three approaches"

Research Skill — Multi-Level Search

Search Level Strategy

Always use the lowest level that can answer the question:

LevelToolUse CaseCost
0Built-in knowledgeCommon concepts, basicsFree
1web_searchQuick verification, simple queriesFree
2Tavily Research APIDeep research, competitive analysisAPI credits

Rule: If Level 0 or 1 can answer it, don't use Level 2.

Don't need research: Common knowledge, already in knowledge/, answerable from built-in knowledge.

Level 2: Tavily Research API

Prerequisites

Get your API key at https://tavily.com (1000 free credits/month)

Set environment variable:

bash
export TAVILY_API_KEY="tvly-your-key-here"

Or add to your agent config:

json
{
"env": {
"TAVILY_API_KEY": "tvly-your-key-here"
}
}
Usage
bash
./scripts/research.sh '{"input": "your research query"}' [output_file]
# Quick research
./scripts/research.sh '{"input": "quantum computing trends"}'
# Deep research
./scripts/research.sh '{"input": "AI agents comparison", "model": "pro"}'
# Save to file
./scripts/research.sh '{"input": "market analysis", "model": "pro"}' ./report.md
Model Selection
ModelUse CaseSpeed
miniSingle topic, targeted~30s
proMulti-angle, comprehensive~60-120s
autoAPI chooses based on complexityVaries

Rule of thumb: "what does X do?" → mini. "X vs Y vs Z" → pro.

Structured Output
bash
./scripts/research.sh '{
"input": "fintech startups 2025",
"model": "pro",
"output_schema": {
"properties": {
"summary": {"type": "string", "description": "Executive summary"},
"companies": {"type": "array", "items": {"type": "string"}}
},
"required": ["summary"]
}
}'
Citation Formats

Supported: numbered (default), mla, apa, chicago

bash
./scripts/research.sh '{"input": "climate impacts", "citation_format": "apa"}'

Post-Research 沉淀 Checkpoint

After completing research, before writing findings or recommendations:

Socratic validation (mandatory for each recommendation/gap/optimization):

  1. Does this problem actually exist in the current codebase? Check existing solutions first.
  2. Is the proposed fix feasible on all target platforms (Kiro + CC)? Check constraints.
  3. Does the benefit outweigh the maintenance cost?

If any answer is "no" → drop that recommendation. Don't include it in findings.

Then persist:

  1. Record validated findings in docs/plans/findings.md (if working on a plan)
  2. If findings reveal reusable patterns → write to knowledge/episodes.md
  3. Cite sources with URLs — no hallucinated references

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
92
Repository forks
14
7-day trend score
23

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 17 May 2026, 5 months ago.

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