Usage instructions
Self-Improvement Skill is a continuous learning framework specially designed for AI coding assistants. It aims to solve the problem of repeated mistakes caused by the "statelessness" of traditional AI assistants through structured recording of errors, user corrections and functional requirements. This skill is deeply integrated with the OpenClaw platform and is compatible with Claude Code, Codex CLI and GitHub Copilot. It enables cross-session experience accumulation by establishing a local knowledge base.
The core usage revolves around three log files:.learnings/LEARNINGS.mdDocument knowledge gaps and best practices,ERRORS.mdCapture command failures and exceptions,FEATURE_REQUESTS.mdTrack user needs. When the AI makes a mistake and is corrected, encounters an unexpected error, or discovers a better solution, the system generates structured entries (in the formatTYPE-YYYYMMDD-XXX), including priority, domain label, and resolution status. As learning items mature, they can be "promoted" toCLAUDE.md、AGENTS.mdorSOUL.mdand other permanent project memory files that become the contextual basis for all subsequent sessions.
Significant advantages include: first, it establishes a true feedback loop, allowing AI to evolve from a "one-time conversation" to a "continuous learning" mode; second, it supports pattern recognition, throughPattern-KeyandRecurrence-CountTrack repeated issues and automatically trigger systemic repairs; third, provide Hook scripts (activator.sh、error-detector.sh) to achieve automated detection and reduce the burden of manual recording; the fourth is integration with Simplify-and-Harden skills, which can transform code simplification experience into persistence rules; the fifth is cross-Agent compatibility to ensure knowledge sharing between different tool chains.
The potential shortcomings cannot be ignored: firstly, the source is a T3-level individual developer (peterskoett), unofficial or well-known enterprise maintenance, and there is long-term maintenance uncertainty; secondly, developers need to actively configure Hooks and adhere to recording habits, otherwise it will easily become a "write and forget" document cemetery; furthermore, learning files may expand infinitely and require regular review (Review) and archiving, otherwise it will increase I/O overhead and context pollution risks; finally, only Markdown format is currently supported, for those who prefer database storage or structured JSON The team may not be flexible enough.
This skill is most suitable for advanced users who have been using AI assistants for complex project development for a long time, especially those developers who need to maintain multiple related projects, want to establish team-level coding standards, or rely on OpenClaw multi-agent workflows. For short-term projects or occasional users of AI assistants, the setup and maintenance costs may outweigh the benefits.
Risks of use mainly include: failure to clean up in time.learnings/The directory may accumulate a large number of outdated entries, causing the AI to reference expired solutions when retrieving; although the Hook script is an opt-in mechanism, misconfiguration may lead to frequent prompt interference; cross-session communication functions such assessions_send) can leak sensitive context in a multi-user environment if used incorrectly; additionally, because the skill requires write file system permissions, it will not work properly in read-only or highly restricted environments.
Safety review
Core usage
Self-Improvement Skill is a set of self-evolution framework for AI agents (such as Claude), which realizes the capture, classification and inheritance of knowledge through standardized Markdown logs. Users do not need to actively call - when six trigger scenarios are detected (command failure, user error correction, missing function request, API exception, outdated knowledge, discovery of better solutions), the information will be automatically written in a structured manner.learnings/Three core files in the directory:
- ERRORS.md: Record command/API failure details, including error output, environment context, reproduction steps and repair suggestions
- LEARNINGS.md: Includes knowledge correction (correction), knowledge gap (knowledge_gap), best practice (best_practice)
- FEATURE_REQUESTS.md: Track user capability requirements, evaluate complexity and implementation paths
Each record usesTYPE-YYYYMMDD-XXXUnique identifier, supports cross-entry association (**See Also**) with dynamic adjustment of priorities. The key mechanism isUpgrade mechanism: When learning has cross-file applicability, distilled is a concise rule and is promoted toCLAUDE.md(Project facts and agreements) orAGENTS.md(Agent workflow and automation rules) to achieve the transformation from temporary logs to permanent memory.
Significant advantages
1. Zero configuration out of the box: Just create.learnings/Directory, no external dependencies
2. Friendly human-machine collaboration: Markdown format is compatible with version control, and teams can share learning libraries
3. Relapse prevention design: Escalate recurring issues into systemic fixes through periodic review and pattern detection
4. Agent interoperability: The encoding agent can automatically parse structured logs and generate repair patches.
5. Progressive knowledge accumulation: Temporary record → correlation analysis → rule extraction → project memory to form a complete knowledge life cycle
potential limitations
- Relying on manual trigger judgment: The detection rules are based on natural language pattern matching, and complex scenes may be missed.
- Log bloat risk: In high-frequency usage scenarios, regular review and cleaning are required, otherwise information will be overloaded.
- Upgrade threshold is subjective: The lack of quantitative standards for the determination of "wide applicability" may lead to redundant or omitted memory files.
- No native query interface:rely
grepWaiting for external tool retrieval, the efficiency of large-scale projects is limited - One-way knowledge flow: Currently not supported from
CLAUDE.mdReverse synchronization or expiration rules are automatically eliminated
Suitable for the crowd
- Large project teams with long-term iterations need to maintain cross-session context
- Multi-AI agent collaboration environment (human developers + multiple coding agents)
- Engineering organizations pursuing knowledge explicitness and auditability
- Individual developers want to build personal AI interactive memory libraries
General risks
1. Sensitive information leaked: The error log may unintentionally capture environment variables, file paths or business logic details. It is recommended to cooperate with.gitignoreStrategy (Local/Team/Mixed Mode)
2. pollution from outdated learning: When not reviewed regularly, records of fixed issues may mislead subsequent agency decisions
3. Over-normalized costs: Strictly following the template may increase recording friction. It is recommended that the core project adopt the complete template and the rapid experiment project simplify the fields.
4. Escalate conflict: Multiple people editing at the same timeCLAUDE.mdMerge conflicts may occur, and it is recommended to combine it with the version control workflow
productivitydevelopment-engineeringeducation-researchautomationdocsproject-program-management
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