self-improve — for Claude Code self-improve, AI-Harness, community, for Claude Code, ide skills, pattern-key, related, recurrence-count, last-seen, first-seen

v1.0.0

Sobre este Skill

Cenario recomendado: Ideal for AI agents that need self-improvement engine. Resumo localizado: # Self-Improvement Engine You are a continuously learning agent. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Recursos

Self-Improvement Engine
Errors (ERR) — Log when:
A command returns a non-zero exit code
An exception or stack trace appears
Unexpected output or behavior occurs

# Tópicos principais

AndersonsRepo AndersonsRepo
[1]
[0]
Atualizado: 3/23/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Cenario recomendado: Ideal for AI agents that need self-improvement engine. Resumo localizado: # Self-Improvement Engine You are a continuously learning agent. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Por que usar essa habilidade

Recomendacao: self-improve helps agents self-improvement engine. Self-Improvement Engine You are a continuously learning agent. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Melhor para

Cenario recomendado: Ideal for AI agents that need self-improvement engine.

Casos de Uso Práticos for self-improve

Caso de uso: Self-Improvement Engine
Caso de uso: Errors (ERR) — Log when:
Caso de uso: A command returns a non-zero exit code

! Segurança e Limitações

  • Limitacao: Do NOT create a duplicate entry — only update the original
  • Limitacao: Requires repository-specific context from the skill documentation
  • Limitacao: Works best when the underlying tools and dependencies are already configured

About The Source

The section below is adapted from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Demo Labs

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ e etapas de instalação

These questions and steps mirror the structured data on this page for better search understanding.

? Perguntas frequentes

O que é self-improve?

Cenario recomendado: Ideal for AI agents that need self-improvement engine. Resumo localizado: # Self-Improvement Engine You are a continuously learning agent. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Como instalar self-improve?

Execute o comando: npx killer-skills add AndersonsRepo/AI-Harness/self-improve. Ele funciona com Cursor, Windsurf, VS Code, Claude Code e mais de 19 outros IDEs.

Quais são os casos de uso de self-improve?

Os principais casos de uso incluem: Caso de uso: Self-Improvement Engine, Caso de uso: Errors (ERR) — Log when:, Caso de uso: A command returns a non-zero exit code.

Quais IDEs são compatíveis com self-improve?

Esta skill é compatível com Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use a CLI do Killer-Skills para uma instalação unificada.

self-improve tem limitações?

Limitacao: Do NOT create a duplicate entry — only update the original. Limitacao: Requires repository-specific context from the skill documentation. Limitacao: Works best when the underlying tools and dependencies are already configured.

Como instalar este skill

  1. 1. Abra o terminal

    Abra o terminal ou linha de comando no diretório do projeto.

  2. 2. Execute o comando de instalação

    Execute: npx killer-skills add AndersonsRepo/AI-Harness/self-improve. A CLI detectará sua IDE ou agente automaticamente e configurará a skill.

  3. 3. Comece a usar o skill

    O skill já está ativo. Seu agente de IA pode usar self-improve imediatamente no projeto atual.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below is adapted from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

self-improve

# Self-Improvement Engine You are a continuously learning agent. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows. Self-Improvement

SKILL.md
Readonly
Upstream Repository Material
The section below is adapted from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

Self-Improvement Engine

You are a continuously learning agent. After every meaningful interaction, evaluate whether something was learned and log it.

When to Log

Errors (ERR) — Log when:

  • A command returns a non-zero exit code
  • An exception or stack trace appears
  • Unexpected output or behavior occurs
  • A timeout or connection failure happens
  • A tool call is denied or fails

Learnings (LRN) — Log when:

  • The user corrects you ("No, that's wrong...", "Actually...", "Not like that...")
  • You discover your knowledge is outdated or incorrect
  • Documentation you referenced is wrong or has changed
  • An API behaves differently than expected
  • A better approach is discovered for something you've done before
  • The user provides information you didn't know

Feature Requests (FEAT) — Log when:

  • The user asks for a capability that doesn't exist
  • You realize a skill would make a recurring task easier
  • The user says "I wish you could..." or "Can you..."

How to Log

Each entry is an individual markdown file in vault/learnings/ with YAML frontmatter.

File Naming

  • vault/learnings/LRN-YYYYMMDD-XXX.md for learnings
  • vault/learnings/ERR-YYYYMMDD-XXX.md for errors
  • vault/learnings/FEAT-YYYYMMDD-XXX.md for feature requests

To determine the next sequence number (XXX), list existing files in vault/learnings/ matching today's date and the entry type prefix, then increment.

Templates

See template files for the full frontmatter and body format:

Recurring Pattern Detection

Before creating a new entry:

  1. List files in vault/learnings/ and scan their frontmatter for matching pattern-key or overlapping tags
  2. If a match is found:
    • Add a [[wikilink]] to the related list in both the existing and new file's frontmatter
    • Increment recurrence-count on the original file
    • Update last-seen date on the original file
    • Do NOT create a duplicate entry — only update the original
  3. If no match, create a new entry file

Promotion Rules

When a learning meets ALL of these criteria, flag it for promotion:

  • recurrence-count >= 3
  • Occurred across 2+ distinct tasks
  • Within a 30-day window (last-seen - first-seen <= 30 days)

Promotion process:

  1. Change status to promoted in the file's frontmatter
  2. Append the learning to the ## Promoted Learnings section of CLAUDE.md
  3. Format: - **[Area]**: Learning description (promoted YYYY-MM-DD, from LRN-XXXXXXXX-XXX)

Important: Always ask the user for approval before promoting. Say:

"I've noticed a recurring pattern: [description]. This has come up [N] times. Should I promote this to CLAUDE.md so I always remember it?"

Skill Extraction

When a learning is valuable enough to become a reusable skill, it qualifies if:

  • It has 2+ [[wikilinks]] in its related list
  • Status is resolved with a verified working fix
  • It required non-obvious debugging to discover
  • It's broadly applicable across projects

To extract, create a new SKILL.md in .claude/skills/<skill-name>/ with:

  • disable-model-invocation: true (user must opt-in to new auto-generated skills)
  • Clear description of what the skill does
  • The learned workflow as step-by-step instructions

Always ask the user before creating a new skill.

Daily Digest

When invoked with /self-improve digest or at the end of a long session, summarize:

  • New entries added today (count by type) — check vault/learnings/ for files with today's date
  • Any patterns approaching promotion threshold (recurrence-count >= 2)
  • Any feature requests that could be built quickly

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