self-improving-agent
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.
Best use case
self-improving-agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using self-improving-agent should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cs-self-improving-agent/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How self-improving-agent Compares
| Feature / Agent | self-improving-agent | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
SKILL.md Source
# 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 ```bash clawhub install self-improving-agent ``` ### Codex CLI ```bash ./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: ```yaml # .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 install` or `clawhub 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](https://code.claude.com/docs/en/memory) - [pskoett/self-improving-agent](https://clawhub.ai/pskoett/self-improving-agent) — inspiration - [playwright-pro](../playwright-pro/) — sister plugin in this repo
Related Skills
self-reflection
Continuous self-improvement through structured reflection and memory
Self-Improving Agent (Proactive Self-Reflection)
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use before starting work and after responding to the user.
youtube-watcher
Fetch and read transcripts from YouTube videos. Use when you need to summarize a video, answer questions about its content, or extract information from it.
youtube-transcript
Fetch and summarize YouTube video transcripts. Use when asked to summarize, transcribe, or extract content from YouTube videos. Handles transcript fetching via residential IP proxy to bypass YouTube's cloud IP blocks.
youtube-auto-captions - YouTube 自动字幕
## 描述
youtube
YouTube Data API integration with managed OAuth. Search videos, manage playlists, access channel data, and interact with comments. Use this skill when users want to interact with YouTube. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway).
yahoo-finance
Get stock prices, quotes, fundamentals, earnings, options, dividends, and analyst ratings using Yahoo Finance. Uses yfinance library - no API key required.
xurl
A Twitter research and content intelligence skill focused on attracting WordPress and Shopify clients. Use to analyze Twitter profiles, threads, and conversations for: (1) Identifying what small agency founders and eCommerce brands are discussing; (2) Understanding pain points around WordPress performance, Shopify CRO, and development bottlenecks; (3) Extracting high-performing content angles; (4) Turning insights into authority-building posts; (5) Converting Twitter intelligence into business leverage for clear content angles, strong positioning, and qualified inbound leads.
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
xiaohongshu-mcp
Automate Xiaohongshu (RedNote) content operations using a Python client for the xiaohongshu-mcp server. Use for: (1) Publishing image, text, and video content, (2) Searching for notes and trends, (3) Analyzing post details and comments, (4) Managing user profiles and content feeds. Triggers: xiaohongshu automation, rednote content, publish to xiaohongshu, xiaohongshu search, social media management.
twitter-openclaw
Interact with Twitter/X — read tweets, search, post, like, retweet, and manage your timeline.
x-twitter-growth
X/Twitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X/Twitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.