self-improvement

self-improvement is a skill for Claude Code, Codex from Crearize/ai-dev-helm. It costs 43 tokens per session (1,614 once invoked), scanned A, original, MIT.

A self-review process for recording improvements to agent rules, skills, or project documentation after work is done. Proposed changes require user approval before they are applied.

In plain words
What is it for?
Use it after quality checks, before creating the quality-passed marker, or when repeated errors or outdated instructions reveal a process improvement.
Why use it?
It helps turn repeated mistakes, unclear procedures, and missing rules into durable improvements for future sessions.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/crearize/ai-dev-helm/self-improvement
Any agent
npx skills add Crearize/ai-dev-helm --skill self-improvement
Clone the repo
git clone --depth 1 https://github.com/Crearize/ai-dev-helm

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for self-improvement

README.md
[![agentmods](https://agentmods.dev/badge/skills/crearize/ai-dev-helm/self-improvement.svg)](https://agentmods.dev/skills/crearize/ai-dev-helm/self-improvement)
Your own site
<a href="https://agentmods.dev/skills/crearize/ai-dev-helm/self-improvement"><img src="https://agentmods.dev/badge/skills/crearize/ai-dev-helm/self-improvement.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,614 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.01614
Opus 5 $0.00022 $0.00807
Sonnet 5 $0.00009 $0.00323
Haiku 4.5 $0.00004 $0.00161

Measured today against content hash 94c2d7423d72, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

self-improvement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/project/self-improvement/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Self Improvement Skill - 自己改善ハーネス

目的

セッション中に見つかったハーネス・ルール・スキルの改善点を、作業完了前にユーザーへ提示し、承認された内容だけを同じブランチ内で反映する。

改善は次回以降の作業品質を高めるための恒久対応として扱う。一時的な回避策や場当たり的なルール追加は禁止する。


実行タイミング

quality-check の最終段階、.quality-check-passed を作成する前に必ず実行する。

フラグはコミット紐付け型(作成時の HEAD を記録した JSON)であり、hook に消費されない。承認された改善の反映先がハーネスファイルCLAUDE.md / AGENTS.md / .cursorrules / .claude/** / .codex/** / .cursor/** / skills/project/** / skills/superpowers/** / .github/review-*.md / documents/development/coding-rules/**)のみであれば、フラグ作成後・マージ前に追加コミットしてもフラグは無効化されず、再レビューサイクルは不要。例外: 上記グロブに含まれていてもゲート制御面ファイルskills/project/quality-check/**skills/project/_schemas/**skills/project/test-recommendation/**.github/review-*.md・hook 実体/登録ファイル等 — 正確な集合は quality-check SKILL.md「ハーネスのみ変更の免除」のカーブアウトを正とする)への追加コミットはフラグを無効化し、hook が Gate control-plane changed: でブロックする。この場合は quality-check の再実行が必要。

以下のタイミングでも必要に応じて実行する:

  • マージ依頼または PR 作成依頼を受けたとき
  • 同じ種類のエラーや手戻りが繰り返されたとき
  • ユーザーからルール不足や運用改善の指摘を受けたとき
  • 既存の CLAUDE.md / AGENTS.md / スキル手順が実態と合っていないと判明したとき

Step 1: 改善候補の抽出

セッションで発生した事実から、改善候補を抽出する。

候補にしてよいもの:

  • ルールやスキルの欠落により判断がぶれた
  • 同じエラーや確認不足が繰り返された
  • サーバー起動、テスト、レビュー、ブランチ運用などの手順が曖昧だった
  • ユーザーが同じ種類の指示を繰り返す必要があった
  • 実装とドキュメント、設計、運用ルールに乖離があった

候補にしてはいけないもの:

  • 一度限りの偶発的なミス
  • プロジェクト固有ではない一般論
  • 検証されていない推測
  • テスト無効化、エラー握りつぶし、型キャスト逃げなどのその場しのぎ
  • ユーザーの明示指示と矛盾する改善

Step 2: 改善案の分類

各候補を反映先ごとに分類する。

反映先 使う条件
CLAUDE.md / AGENTS.md / .cursorrules AI の基本動作、確認条件、自律性、品質ゲートに関する改善
documents/development/coding-rules/ 実装規約、命名、設計、テスト、セキュリティなどのプロジェクトルール
skills/project/*/SKILL.md 手順化すべき運用、品質チェック、サーバー起動、ブランチ運用
.github/review-*.md レビュー観点やチェックリストの追加
README / docs 利用者向け説明、セットアップ手順、FAQ

Step 3: ユーザー確認

改善候補は必ずユーザーに提示し、適用可否を確認する。

提示形式:

作業完了前の自己改善候補です。適用するものを選んでください。

1. [反映先] 改善タイトル
   根拠: セッション中に発生した事実
   変更案: どのファイルに何を追加・修正するか
   期待効果: 次回以降どう改善されるか

適用 / 見送り / 内容修正

ユーザーが見送った候補は適用しない。判断材料が不足している候補は、調査してから再提示する。

Read the full file on GitHub · 142 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed 94c2d7423d72
  2. 4d ago First seen · 142 lines · 43 tokens per session scan A 40692962ea65

Subscribe to this mod's changes

self-improvement is a skill published in the GitHub repository Crearize/ai-dev-helm (4 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,614 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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