skill-reinforcement_skill

This skill automatically analyzes outcomes after each use and updates learnings to improve future performance and prevent knowledge loss.
  • TypeScript

186

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill different-ai/agent-bank --skill skill-reinforcement

  • SKILL.md7.7 KB

Overview

This skill automatically captures learnings and anti-patterns after any skill run and immediately improves the skill corpus. It prevents knowledge loss between sessions by analyzing outcomes, extracting shortcuts, documenting failures, and updating skill files with actionable edits. The goal is continuous, low-friction improvement after every use.

How this skill works

After a skill completes, the skill inspects the execution context, outcome, token usage, and time taken. It asks focused questions (what failed, what was faster, what assumption broke) and classifies discoveries into categories like shortcuts, failure modes, better patterns, anti-patterns, and environment quirks. Findings are formatted with templates and appended to the relevant skill’s documentation, then validated for clarity and non-redundancy. Periodic reviews surface high-impact areas across many skills.

When to use it

  • Automatically after any skill completes (success, partial, or failure)
  • When a workflow succeeds or fails in a notable or repeatable way
  • Whenever a new shortcut or token-saving pattern is discovered
  • If commands fail and you identify a reliable fix
  • When API behavior differs from docs or assumptions prove wrong

Best practices

  • Capture context immediately: skill name, task, outcome, token cost, and time
  • Use concise, actionable entries with exact commands or code snippets
  • Classify each learning into categories (shortcut, failure, anti-pattern, etc.)
  • Validate each update for formatting, specificity, and non-redundancy
  • Cross-pollinate learnings to related skills when applicable

Example use cases

  • Found OTP visible in email preview: add a token-saving shortcut to the OTP flow
  • Encountered session persistence in staging: add a check-first pattern to login workflows
  • Fixed a failing curl invocation: document the exact command fix under Common Issues
  • Observed repetitive manual steps: convert repeated actions into a new skill/tool
  • Noticed inconsistent API responses: update prerequisites and add an environment quirk note

FAQ

Apply updates immediately after a meaningful discovery; avoid batching notes for much later.

What counts as an actionable learning?

Anything with a clear fix, exact command, or measurable benefit (token/time reduction) that others can follow.

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