Notre avis
Analyse l'historique des conversations de Claude Code pour identifier les schémas de friction et suggérer des améliorations de la configuration.
Points forts
- Exploite les conversations réelles pour des suggestions contextuelles
- Génère des fichiers de propositions exploitables
- Automatise la détection des problèmes récurrents
- Propose des améliorations pour CLAUDE.md et les compétences
Limites
- Nécessite un accès à l'historique des conversations
- Peut être lent sur de grands volumes de données
- Ne modifie pas automatiquement la configuration
Utilisez cette compétence lorsqu'un projet ou votre flux de travail présente des frictions répétées avec l'agent Claude.
Ne l'utilisez pas si vous n'avez pas d'historique de conversations pertinent ou si vous préférez ajuster manuellement la configuration.
Analyse de sécurité
SûrThe skill only reads local conversation history and writes a local markdown file; it uses no network calls, destructive commands, or data exfiltration. The bash commands are limited to listing files, and subagents only analyze content locally.
Aucun point d'attention détecté
Exemples
/self-improvement/self-improvement last 3 days/self-improvement refactoringname: self-improvement description: Analyze conversation history to find friction patterns and suggest CLAUDE.md/skill improvements. Use when user wants to review what went wrong across sessions and systematically improve. (user) allowed-tools: Read, Bash, Grep, Glob, Task
Self-Improvement - Learn from History
Analyze Claude Code conversation history to find friction patterns, check what's already fixed, and suggest improvements.
Instructions
Phase 1: Find Conversations
# Get project directory (encode current path with dashes)
ls ~/.claude/projects/
# List today's conversations (or adjust -mtime for longer range)
ls -lt ~/.claude/projects/<encoded-path>/*.jsonl | head -20
Phase 2: Parallel Analysis
Spawn Task agents (subagent_type: general-purpose) to analyze conversations in parallel. Each agent reads one .jsonl file and extracts:
- What was user trying to accomplish?
- Problems/friction that occurred (include user quotes showing frustration)
- What worked well?
- Repeated patterns or inefficiencies
For large files (>500KB), prioritize those - they contain the meatiest sessions.
Phase 3: Synthesize
After all agents complete, combine findings:
- Top friction patterns ranked by frequency
- What worked well (don't lose these)
- User frustration quotes (raw evidence)
Generalize aggressively. Look for the meta-pattern behind specific issues.
Phase 4: Cross-Reference
Read current documentation:
- Project CLAUDE.md
- User ~/.claude/CLAUDE.md
- Skills in .claude/skills/ and ~/.claude/skills/
For each friction pattern, classify:
- Already fixed - note location
- Still missing - needs addition
Phase 5: Output
Create a markdown file (e.g., CLAUDE_IMPROVEMENTS.md) with:
# Suggested CLAUDE.md Improvements
Based on analysis of N conversations from [date range].
## 1. [Issue Name]
**Problem:** [Description of friction]
**Suggested addition to [location]:**
\```markdown
[Proposed text]
\```
---
## Already Fixed
| Issue | Where |
|-------|-------|
| ... | ... |
---
## Potential Skills
| Skill | Purpose |
|-------|---------|
| ... | ... |
---
## Raw Friction Log
- "user quote 1"
- "user quote 2"
Do not apply changes - create the file for user review.
Usage Examples
/self-improvement
/self-improvement last 3 days
/self-improvement refactoring
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