tonitangpotato/rustclaw/research

Claude Code and agent skill by tonitangpotato/rustclaw

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Competitive analysis, technical research, and market validation for new features

Install

npx vskill@latest install tonitangpotato/rustclaw/research

SKILL.md

SKILL: Research Phase

Conduct competitive analysis, technical research, and market validation before drafting requirements.

Trigger Conditions

This skill activates when:

  • Starting a new feature that needs research
  • User asks for "research", "竞品", "调研", "market research", or "competitive analysis"
  • GID ritual reaches the research phase

Input

  • Feature name/description from capture-idea phase
  • Any existing context from .gid/features/{feature}/idea.md

Output

Create docs/RESEARCH-{feature-name}.md with the following structure:

markdown
# Research: {feature-name}
## Executive Summary
2-3 sentence overview of findings and recommendation.
## Competitive Landscape
### {Competitor 1}
-**URL**:
-**What it does**:
-**Key features**:
-**Strengths**:
-**Weaknesses**:
-**Pricing**: (if applicable)
-**User sentiment**: (from reviews, forums, etc.)
### {Competitor 2}
...
### Competitive Matrix
| Feature | Us (planned) | Competitor 1 | Competitor 2 |
|---------|--------------|--------------|--------------|
| ... | ... | ... | ... |
## Technical Approaches
### Option A: {approach name}
-**Description**:
-**Pros**:
-**Cons**:
-**Effort estimate**:
-**Key libraries/dependencies**:
### Option B: {approach name}
...
### Technical Recommendation
Which approach to use and why.
## Market Signals
### User Demand Evidence
-Forum discussions, Reddit threads, HN comments
-GitHub issues/stars on related projects
-Search volume trends (if available)
### Pain Points Found
-What problems users are reporting
-What's missing in existing solutions
### Market Size Indicators
-Target audience size estimates
-Growth trends in the space
## Prior Art
### Open Source Projects
-Relevant repos with brief assessment
### Academic Papers / Blog Posts
-Key technical resources that inform the design
### Lessons Learned from Others
-What worked/failed for similar projects
## Key Insights
1.**Insight 1**: [actionable takeaway]
2.**Insight 2**: [actionable takeaway]
3.**Insight 3**: [actionable takeaway]
4.**Insight 4**: [actionable takeaway]
5.**Insight 5**: [actionable takeaway]
## Recommendation
### Go / No-Go Assessment
**Recommendation**: [GO / NO-GO / PIVOT]
**Reasoning**:
-[Key factor 1]
-[Key factor 2]
-[Key factor 3]
### If GO, Key Success Factors
-What must be true for this to succeed
-What differentiates us from competition
### If NO-GO, Alternatives
-What should we do instead

Research Process

Step 0: Codebase Discovery (MANDATORY before any research)
→ find . -type f \( -name "*.rs" -o -name "*.ts" -o -name "*.py" \) | xargs grep -li "{feature_keywords}" 2>/dev/null
→ find . -path "*/packages/*" -o -path "*/crates/*" -o -path "*/src/*" | grep -i "{feature_name}"
→ Check if implementation already exists under a different name/path
→ If files found:
- Read them to understand what's already built
- Document in research output: "## Existing Implementation" section
- Assess: is this task already done? partially done? needs different approach?
- If fully implemented: STOP research, report "already exists at {path}"
→ If nothing found: proceed to Step 1

Why this step exists: We once reimplemented a TypeScript MCP server in Rust because we didn't check packages/mcp/ first. This step is free (no LLM, just grep) and prevents wasted work.

Step 1: Competitive Analysis
→ web_search("{feature} alternatives")
→ web_search("{feature} competitors")
→ web_search("{feature} open source")
→ For each competitor found:
→ web_fetch(competitor_url) to understand features
→ web_search("{competitor} reviews") for user sentiment
Step 2: Technical Research
→ web_search("{feature} implementation")
→ web_search("{feature} architecture")
→ web_search("{feature} rust library" or "{feature} python library")
→ Check GitHub trending/search for relevant projects
→ Evaluate feasibility based on existing tools/libraries
Step 3: Market Validation
→ web_search("{feature} demand")
→ web_search("{feature} reddit" or "{feature} hacker news")
→ web_search("{feature} problems" or "{feature} pain points")
→ Look for patterns in user complaints/requests
Step 4: Prior Art
→ web_search("{feature} paper" or "{feature} research")
→ web_search("{feature} blog post" or "{feature} tutorial")
→ GitHub search for related projects, check their issues/discussions
Step 5: Synthesize & Recommend
→ Compile findings into structured document
→ Identify patterns across sources
→ Make go/no-go recommendation with clear reasoning
→ If GO, identify key differentiators and success factors

Notes

  • Speed over perfection — This is research to inform decisions, not an exhaustive academic survey
  • Focus on actionable insights — Every section should help make better product decisions
  • Be honest about unknowns — If data is scarce, say so rather than speculating
  • Quantify when possible — GitHub stars, forum upvotes, search trends > vague assertions
  • Document sources — Include URLs so findings can be verified
  • Time-box — Aim for 20-30 minutes of research, not hours
  • Use 中英混用 naturally if researching Chinese market/competitors

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

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