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

이 스킬 정보

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

기능

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

# 핵심 주제

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

Skill Overview

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

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

이 스킬을 사용하는 이유

추천 설명: 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.

최적의 용도

적합한 상황: Ideal for AI agents that need self-improvement engine.

실행 가능한 사용 사례 for self-improve

사용 사례: Applying Self-Improvement Engine
사용 사례: Applying Errors (ERR) — Log when:
사용 사례: Applying A command returns a non-zero exit code

! 보안 및 제한 사항

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

About The Source

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

Labs 데모

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 및 설치 단계

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

? 자주 묻는 질문

self-improve은 무엇인가요?

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

self-improve은 어떻게 설치하나요?

다음 명령을 실행하세요: npx killer-skills add AndersonsRepo/AI-Harness. Cursor, Windsurf, VS Code, Claude Code와 19개 이상의 다른 IDE에서 동작합니다.

self-improve은 어디에 쓰이나요?

주요 활용 사례는 다음과 같습니다: 사용 사례: Applying Self-Improvement Engine, 사용 사례: Applying Errors (ERR) — Log when:, 사용 사례: Applying A command returns a non-zero exit code.

self-improve 와 호환되는 IDE는 무엇인가요?

이 스킬은 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 와 호환됩니다. 통합 설치에는 Killer-Skills CLI를 사용하세요.

self-improve에 제한 사항이 있나요?

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

이 스킬 설치 방법

  1. 1. 터미널 열기

    프로젝트 디렉터리에서 터미널 또는 명령줄을 여세요.

  2. 2. 설치 명령 실행

    npx killer-skills add AndersonsRepo/AI-Harness 를 실행하세요. CLI가 IDE 또는 에이전트를 자동으로 감지하고 스킬을 설정합니다.

  3. 3. 스킬 사용 시작

    스킬이 이제 활성화되었습니다. 현재 프로젝트에서 self-improve을 바로 사용할 수 있습니다.

! 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 comes 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 comes 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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Looking for an alternative to self-improve or another community skill for your workflow? Explore these related open-source skills.

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