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Agent Failure Pattern Manager
by Timoranjes
Teaches AI coding agents to stop repeating the same mistakes by maintaining a structured LEARNINGS.md file that captures failure patterns, converts them into guardrails, and feeds them back into CLAUD
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See it in action
You say
You just rewrote the whole auth module when I only asked for a log line. Record this failure and create a prevention rule so you don't overstep again.
Your agent does
I have updated LEARNINGS.md with a new entry:
- [2023-10-27] Expanded scope in auth module.
- Root Cause: Scope Creep on Autonomy (AP-13).
- Prevention: Before modifying code outside the requested scope, I will explicitly ask for confirmation.
What you get
About this skill
The problem
Coding agents lack long-term memory, leading them to repeat the same architectural errors and convention violations every time a new session starts. You waste time re-explaining the same project constraints that the agent should have learned yesterday.
What it does
- Establishes a structured LEARNINGS.md loop to persist failure data across sessions.
- Diagnoses root causes using a specialized 13-pattern agent failure taxonomy.
- Generates concrete, actionable prevention rules instead of vague instructions.
- Automates an end-of-session ritual to capture and consolidate project-specific insights.
- Transitions recurring fixes into permanent project guardrails within CLAUDE.md.
Frameworks & tools
Designed for AI coding assistants including Claude Code, Cursor, and Codex CLI. Compatible with projects using CLAUDE.md, AGENTS.md, or .cursorrules configuration files.
Why this beats prompting it yourself
Manual instructions are often lost or ignored as context windows shift. This skill provides a standardized diagnostic framework that forces the agent to treat its own mistakes as structured data, ensuring errors lead to permanent architectural improvements rather than just temporary fixes.
Use cases
- Stopping agents from using incorrect import patterns or deprecated library versions.
- Preventing scope creep where an agent refactors unrelated files during a simple bug fix.
- Ensuring agents run specific test suites before declaring a task complete.
- Building a project-specific knowledge base during complex legacy code migrations.
Known limitations
Relies on the agent having filesystem access to read and write the LEARNINGS.md file. Requires the developer to verify the quality of recorded prevention rules to avoid noise.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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