aggregate-logs
CommandGenerate LEARNINGS.md from skill execution logs over a configurable time window.
Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading.
> /plugin marketplace add athola/claude-night-market
name: analyze-skill description: Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading. usage: /analyze-skill [skill-path]
Analyzes a skill file for complexity metrics and provides modularization recommendations based on the modular-skills framework.
Use this command when you need to:
Avoid this command if:
# Analyze specific skill file /analyze-skill skills/my-skill/SKILL.md # Analyze skill directory /analyze-skill skills/my-skill # Analyze current directory (if in a skill folder) /analyze-skill .
The analyzer evaluates several complexity dimensions:
/analyze-skill skills/modular-skills/SKILL.md # Output: # === Analysis for: skills/modular-skills/SKILL.md === # Line count: 180 (threshold: 150) # Estimated tokens: 1,847 # === Recommendations === # MODULARIZE: File exceeds threshold (180 > 150) # GOOD: Token usage in optimal range /analyze-skill skills/skills-eval --verbose # Includes detailed section breakdown
This command is the first step in the modular-skills workflow:
1. **Analyze** → Use this command to assess complexity 2. **Design** → Plan module structure based on recommendations 3. **Estimate** → Use `/context-report` for token budgeting 4. **Validate** → Use `/skills-eval` for quality checks
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/skill_analyzer.py --file "${1:-.}"A plugin marketplace for Claude Code. Install only the plugins you need to run git workflows, code review, spec-driven development, and autonomous agents from inside your Claude Code session.
Generate LEARNINGS.md from skill execution logs over a configurable time window.
Generate context optimization report for skill directories
Create slash commands with brainstorming and best practices
Scaffold new Claude Code skills with brainstorming, TDD methodology, and proper frontmatter and module structure.
Manually evaluate a recent skill execution to record qualitative feedback.
Implement tasks incrementally — build, test, verify, commit. Add "auto" to run the whole plan in one approved pass.