Self-Improving Agent
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Please install @kunlungrowth/self-improving-agent-cn by following https://skillhub.cn/install/skillhub.md.
About this skill
Problem It Addresses
When an AI agent keeps repeating failed commands, forgets user-stated preferences, or stores useful experience only in the current session, manual correction becomes repetitive. This skill addresses the lack of durable learning signals: non-zero exit codes, user corrections such as 'not quite' or 'should be', better practices discovered later, and outdated tool or API knowledge all need to be captured and checked before the next action.
How It Works
It routes different signals into structured memory files: failures to errors.jsonl, corrections to corrections.jsonl, best practices to best_practices.jsonl, and knowledge gaps to knowledge_gaps. These memories can be synchronized across a global directory, project-level .learnings/, and AGENTS.md or MEMORY.md. By checking memory before execution, the agent can reuse corrected workflows for install, build, formatting, and API-related tasks.
Boundaries
It relies on trigger phrases and event signals rather than acting as a general-purpose inference engine. Sensitive data and private credentials should not be written to memory files in plain form. Periodic backups to git, redaction, and cleanup of stale entries are recommended to avoid hard-wiring incorrect experience.
Use Cases
- When terminal dependency installs keep failing, let the agent log the error and check memory before the next run to avoid repeating the same command.
- When a user repeatedly corrects code style, such as requiring single quotes, store the correction and update AGENTS.md so later tasks follow it.
- When a better local Python workflow is found, such as editable installs, record the best practice and use it in later package tasks.
- When an API or dependency is outdated, log the knowledge gap so the agent checks memory and suggests the current approach next time.
Best For
- Backend engineers who repeatedly run shell installs, builds, or scripts with an agent and want to avoid repeated permission or environment pitfalls.
- Frontend engineers who need the agent to keep consistent code-style, comment, and project conventions without restating them.
- Full-stack developers who reuse one agent workflow across multiple repositories and want project-level experience retained.
- DevOps engineers automating operations with LLMs, who need to log failed commands, tool version changes, and better execution patterns.
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