How to install self-improving-agent
npx skills add https://github.com/alirezarezvani/claude-skills --skill self-improving-agentFull instructions (SKILL.md)
Source of truth, from alirezarezvani/claude-skills.
name: "self-improving-agent" description: "Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extract recurring solutions into reusable skills. Use when: (1) reviewing what Claude has learned about your project, (2) graduating a pattern from notes to enforced rules, (3) turning a debugging solution into a skill, (4) checking memory health and capacity."
Self-Improving Agent
Auto-memory captures. This plugin curates.
Claude Code's auto-memory (v2.1.32+) automatically records project patterns, debugging insights, and your preferences in MEMORY.md. This plugin adds the intelligence layer: it analyzes what Claude has learned, promotes proven patterns into project rules, and extracts recurring solutions into reusable skills.
Quick Reference
| Command | What it does |
|---|---|
/si:review | Analyze MEMORY.md — find promotion candidates, stale entries, consolidation opportunities |
/si:promote | Graduate a pattern from MEMORY.md → CLAUDE.md or .claude/rules/ |
/si:extract | Turn a proven pattern into a standalone skill |
/si:status | Memory health dashboard — line counts, topic files, recommendations |
/si:remember | Explicitly save important knowledge to auto-memory |
How It Fits Together
┌─────────────────────────────────────────────────────────┐
│ Claude Code Memory Stack │
├─────────────┬──────────────────┬────────────────────────┤
│ CLAUDE.md │ Auto Memory │ Session Memory │
│ (you write)│ (Claude writes)│ (Claude writes) │
│ Rules & │ MEMORY.md │ Conversation logs │
│ standards │ + topic files │ + continuity │
│ Full load │ First 200 lines│ Contextual load │
├─────────────┴──────────────────┴────────────────────────┤
│ ↑ /si:promote ↑ /si:review │
│ Self-Improving Agent (this plugin) │
│ ↓ /si:extract ↓ /si:remember │
├─────────────────────────────────────────────────────────┤
│ .claude/rules/ │ New Skills │ Error Logs │
│ (scoped rules) │ (extracted) │ (auto-captured)│
└─────────────────────────────────────────────────────────┘
Installation
Claude Code (Plugin)
/plugin marketplace add alirezarezvani/claude-skills
/plugin install self-improving-agent@claude-code-skills
OpenClaw
clawhub install self-improving-agent
Codex CLI
./scripts/codex-install.sh --skill self-improving-agent
Memory Architecture
Where things live
| File | Who writes | Scope | Loaded |
|---|---|---|---|
./CLAUDE.md | You (+ /si:promote) | Project rules | Full file, every session |
~/.claude/CLAUDE.md | You | Global preferences | Full file, every session |
~/.claude/projects/<path>/memory/MEMORY.md | Claude (auto) | Project learnings | First 200 lines |
~/.claude/projects/<path>/memory/*.md | Claude (overflow) | Topic-specific notes | On demand |
.claude/rules/*.md | You (+ /si:promote) | Scoped rules | When matching files open |
The promotion lifecycle
1. Claude discovers pattern → auto-memory (MEMORY.md)
2. Pattern recurs 2-3x → /si:review flags it as promotion candidate
3. You approve → /si:promote graduates it to CLAUDE.md or rules/
4. Pattern becomes an enforced rule, not just a note
5. MEMORY.md entry removed → frees space for new learnings
Core Concepts
Auto-memory is capture, not curation
Auto-memory is excellent at recording what Claude learns. But it has no judgment about:
- Which learnings are temporary vs. permanent
- Which patterns should become enforced rules
- When the 200-line limit is wasting space on stale entries
- Which solutions are good enough to become reusable skills
That's what this plugin does.
Promotion = graduation
When you promote a learning, it moves from Claude's scratchpad (MEMORY.md) to your project's rule system (CLAUDE.md or .claude/rules/). The difference matters:
- MEMORY.md: "I noticed this project uses pnpm" (background context)
- CLAUDE.md: "Use pnpm, not npm" (enforced instruction)
Promoted rules have higher priority and load in full (not truncated at 200 lines).
Rules directory for scoped knowledge
Not everything belongs in CLAUDE.md. Use .claude/rules/ for patterns that only apply to specific file types:
# .claude/rules/api-testing.md
---
paths:
- "src/api/**/*.test.ts"
- "tests/api/**/*"
---
- Use supertest for API endpoint testing
- Mock external services with msw
- Always test error responses, not just happy paths
This loads only when Claude works with API test files — zero overhead otherwise.
Agents
memory-analyst
Analyzes MEMORY.md and topic files to identify:
- Entries that recur across sessions (promotion candidates)
- Stale entries referencing deleted files or old patterns
- Related entries that should be consolidated
- Gaps between what MEMORY.md knows and what CLAUDE.md enforces
skill-extractor
Takes a proven pattern and generates a complete skill:
- SKILL.md with proper frontmatter
- Reference documentation
- Examples and edge cases
- Ready for
/plugin installorclawhub publish
Hooks
error-capture (PostToolUse → Bash)
Monitors command output for errors. When detected, appends a structured entry to auto-memory with:
- The command that failed
- Error output (truncated)
- Timestamp and context
- Suggested category
Token overhead: Zero on success. ~30 tokens only when an error is detected.
Platform Support
| Platform | Memory System | Plugin Works? |
|---|---|---|
| Claude Code | Auto-memory (MEMORY.md) | ✅ Full support |
| OpenClaw | workspace/MEMORY.md | ✅ Adapted (reads workspace memory) |
| Codex CLI | AGENTS.md | ✅ Adapted (reads AGENTS.md patterns) |
| GitHub Copilot | .github/copilot-instructions.md | ⚠️ Manual promotion only |
Related
- Claude Code Memory Docs
- pskoett/self-improving-agent — inspiration
- playwright-pro — sister plugin in this repo
Related skills
More from alirezarezvani/claude-skills and the wider catalog.

senior-architect
This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.

senior-backend
Designs and implements backend systems including REST APIs, microservices, database architectures, authentication flows, and security hardening. Use when the user asks to "design REST APIs", "optimize database queries", "implement authentication", "build microservices", "review backend code", "set up GraphQL", "handle database migrations", or "load test APIs". Covers Node.js/Express/Fastify development, PostgreSQL optimization, API security, and backend architecture patterns.

senior-computer-vision
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

senior-data-engineer
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

senior-data-scientist
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

senior-frontend
Frontend development skill for React, Next.js, TypeScript, and Tailwind CSS applications. Use when building React components, optimizing Next.js performance, analyzing bundle sizes, scaffolding frontend projects, implementing accessibility, or reviewing frontend code quality.