Self-Improvement Learning Log
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Please install @user_5d1eaca6/123-plus-pro into your AI assistant following https://skillhub.cn/install/skillhub.md.
About this skill
Problem
Coding agents often repeat avoidable mistakes across sessions: using the wrong package manager, forgetting generated clients, or tripping over the same API quirk. Conversational context disappears, so the same failure can reappear weeks later. This skill turns errors, corrections, and feature requests into durable text artifacts instead of transient chat history.
How It Works
It maintains .learnings/ at the project or workspace root:
- ERRORS.md: command failures, exceptions, and timeouts
- LEARNINGS.md: corrections, knowledge gaps, and best practices
- FEATURE_REQUESTS.md: requested capabilities
Entries use IDs like LRN-20250115-001, with metadata such as Status, Priority, Area, and See Also. When a lesson is broad or recurring, it can be distilled into concise rules and promoted to CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md; in OpenClaw workspaces, guidance may also land in SOUL.md and TOOLS.md.
Boundaries
This is useful for iterated codebases and shared agent conventions. Logs can capture sensitive output, so prefer summaries and redactions over raw command transcripts. Hook reminders are optional and should not be mistaken for automatic repair. If local notes should stay private, keep entries out of version control.
Use Cases
- After repeated CI build failures, log an error summary to ERRORS.md and mark it pending.
- When a user corrects the package manager, record the fix in LEARNINGS.md and promote it to CLAUDE.md.
- When multiple tasks hit the same API auth issue, aggregate entries by Pattern-Key and refine an AGENTS.md rule.
- Before an OpenClaw session, review TOOLS.md tool limits to avoid repeated configuration failures.
Best For
- Agent pipeline engineers who need to turn repeated failures into project conventions.
- Platform teams maintaining multi-agent workspaces who need tool limits documented in TOOLS.md.
- Developers using Claude Code or Copilot who want corrections captured and injected into later context.
- AI agent operations teams who need errors, corrections, and feature requests tracked.
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