self-learning-skills

Overview

The self-learning skill acts as a meta-framework for AI development assistants, monitoring sessions for hard-won operational knowledge, deployment steps, or architectural facts. When a task succeeds after multiple tries, the agent records the verified procedure, explicit failure patterns, and eliminated dead-ends into a durable format like a local skill file, Cursor rule, or AGENTS.md instruction. Strict safety guidelines prevent leaking secrets, and a rigorous promotion rule ensures only fully validated, repeatable workflows graduate to authoritative instructions.

Capabilities

• ▸Recognizes successful trial-and-error moments and operational cues during coding sessions.
• ▸Persists reusable procedures into tool-native formats like Claude Code skills or Cursor rules.
• ▸Applies strict promotion criteria requiring passing checks, named failure patterns, and ruled-out dead-ends.
• ▸Maintains security by restricting stored configuration details to references rather than literal secret values.

Best for

Preserving complex deployment, migration, or testing workflows across development sessions., Capturing non-obvious project environment facts, configuration locations, and operational gotchas., Recording ruled-out troubleshooting dead-ends to prevent future agent sessions from repeating mistakes.

Works with

Security considerations

Ensures safety by explicitly forbidding the recording of raw secret values, API keys, passwords, or connection strings, restricting documentation strictly to environment variable names or secret manager references.

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