Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Static code analysis and complexity metrics
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
No separate download: the skill runs the in-repo tool .claude/tools/analysis/project-analyzer/analyzer.mjs.
winget install OpenJS.NodeJS.LTS (Windows), brew install node (macOS).Metrics: Focus on cyclomatic complexity (decision paths), LOC, maintainability index, and duplicate blocks. Use ESLint complexity rule (e.g. "complexity": ["error", 15]) for JS/TS; optional chaining and default params add branches.
Process: Analyze before refactoring; run project-wide then drill into hotspots. Track trends over time (not one-off). Use max-depth, max-lines, max-nested-callbacks, max-params, max-statements alongside complexity.
Hacks: Start with project-analyzer output; filter by file type and threshold. Prioritize files with high complexity and high churn. Disable complexity rule only if you cannot set a sensible limit; prefer lowering the threshold over disabling.
No single cert; aligns with static analysis and ESLint complexity. ESLint: complexity rule, max-depth, max-lines, max-params. Skill data: Cyclomatic complexity, LOC, maintainability, duplicates; analyze before refactor; track hotspots and trends.
Suggested hooks: Pre-commit or CI: run project-analyzer/doctor for health; optional complexity gate. Use with developer (secondary), qa (secondary), code-reviewer (primary).
Workflows: Use with code-reviewer (primary), developer/ qa (secondary), c4-code (primary). Flow: run analyzer → filter hotspots → refactor or add tests. See code-review-workflow.md.
Static code analysis and metrics. 90%+ context savings.
| Tool | Description |
|---|---|
| analyze-file | Analyze single file |
| analyze-project | Analyze entire project |
| complexity | Calculate complexity metrics |
| Tool | Description |
|---|---|
| loc | Lines of code |
| cyclomatic | Cyclomatic complexity |
| maintainability | Maintainability index |
| duplicates | Find duplicate code |
| Tool | Description |
|---|---|
| summary | Get analysis summary |
| hotspots | Find complexity hotspots |
| trends | Analyze metric trends |
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Analyzing only changed files | Misses cross-file complexity accumulation | Run project-wide then filter to changed hot spots |
| Ignoring high-complexity files over time | Gradual degradation invisible in point-in-time analysis | Track weekly trends; alert on any increase |
| Complexity threshold >20 | Research shows defect rate spikes sharply above 20 | Set ESLint complexity rule to ≤15 for enforcement |
| Reporting metrics without action items | Metrics without remediation don't reduce complexity | Attach specific refactoring suggestion per hotspot |
| Running analysis once and ignoring results | Technical debt silently accumulates | Schedule automated weekly analysis with trend reports |
Before starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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