Agent skill

Agent Eval

カスタムタスクでコーディングエージェント（Claude Code、Aider、Codex など）をヘッドツーヘッドで比較し、合格率、コスト、時間、一貫性のメトリクスを測定します

MITAuto-check passedDevelopment

Install Agent Eval

skills CLI

$ npx skills add affaan-m/ECC --skill agent-eval -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install affaan-m/ECC agent-eval --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/ja-JP/skills/agent-eval .claude/skills/agent-eval && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "agent-eval" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval into .claude/skills/agent-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-eval", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add affaan-m/ECC --skill agent-eval -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install affaan-m/ECC agent-eval --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/docs/ja-JP/skills/agent-eval .agents/skills/agent-eval && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "agent-eval" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval into .agents/skills/agent-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-eval", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add affaan-m/ECC --skill agent-eval -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install affaan-m/ECC agent-eval --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/docs/ja-JP/skills/agent-eval .cursor/skills/agent-eval && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "agent-eval" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval into .cursor/skills/agent-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-eval", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/affaan-m/ECC.git --path docs/ja-JP/skills/agent-eval

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add affaan-m/ECC --skill agent-eval -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install affaan-m/ECC agent-eval --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/docs/ja-JP/skills/agent-eval .gemini/skills/agent-eval && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "agent-eval" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval into .gemini/skills/agent-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-eval", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install affaan-m/ECC agent-eval

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add affaan-m/ECC --skill agent-eval -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/docs/ja-JP/skills/agent-eval .github/skills/agent-eval && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "agent-eval" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval into .github/skills/agent-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-eval", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add affaan-m/ECC --skill agent-eval -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install affaan-m/ECC agent-eval --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/docs/ja-JP/skills/agent-eval .opencode/skills/agent-eval && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "agent-eval" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/agent-eval into .opencode/skills/agent-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-eval", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: agent-eval
GitHub stars: 276k
Token cost: ~786 tokens
SKILL.md length: 99 words
Files: 1
Skills in repo: 673
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Works in 3 steps: タスクの定義 → エージェントの実行 → 結果の比較
Development work in your project
SKILL.md covers 起動タイミング, インストール, コアコンセプト and ワークフロー, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Eval is an agent skill from affaan-m/ECC. カスタムタスクでコーディングエージェント（Claude Code、Aider、Codex など）をヘッドツーヘッドで比較し、合格率、コスト、時間、一貫性のメトリクスを測定します

Its SKILL.md is about 790 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. It works with Git. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

Development work in your project

Example prompts

“/agent-eval”

Requirements

Docker

Workflow steps

3 steps, taken from the step headings in SKILL.md.

タスクの定義
エージェントの実行
結果の比較

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml and bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Eval loads about 786 tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 99 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~25
When it runs · the whole SKILL.md, loaded when a task matches: ~786

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 99 words, ~786 tokens.

Download SKILL.mdSave it as .claude/skills/agent-eval/SKILL.md (or your agent's skills folder).

name: agent-eval
description: カスタムタスクでコーディングエージェント（Claude Code、Aider、Codex など）をヘッドツーヘッドで比較し、合格率、コスト、時間、一貫性のメトリクスを測定します
origin: ECC
tools: Read, Write, Edit, Bash, Grep, Glob

エージェント評価スキル

再現可能なタスクでコーディングエージェントをヘッドツーヘッドで比較するための軽量 CLI ツールです。「どのコーディングエージェントが最適か？」という比較はすべて感覚に頼りがちです — このツールはそれを体系化します。

起動タイミング

自分のコードベースでコーディングエージェント（Claude Code、Aider、Codex など）を比較する
新しいツールやモデルを採用する前にエージェントパフォーマンスを測定する
エージェントがモデルやツールを更新した際にリグレッションチェックを実行する
チームにデータに基づいたエージェント選択の判断を提供する

インストール

注意: agent-eval はソースを確認した後、リポジトリからインストールしてください。

コアコンセプト

YAML タスク定義

タスクを宣言的に定義します。各タスクは何をするか、どのファイルを操作するか、成功をどう判定するかを指定します：

yaml

name: add-retry-logic
description: Add exponential backoff retry to the HTTP client
repo: ./my-project
files:
  - src/http_client.py
prompt: |
  Add retry logic with exponential backoff to all HTTP requests.
  Max 3 retries. Initial delay 1s, max delay 30s.
judge:
  - type: pytest
    command: pytest tests/test_http_client.py -v
  - type: grep
    pattern: "exponential_backoff|retry"
    files: src/http_client.py
commit: "abc1234"  # 再現性のために特定コミットに固定

Git ワークツリー分離

各エージェント実行は独自の git ワークツリーを取得します — Docker 不要。これにより再現性の分離が提供され、エージェントが互いに干渉したりベースリポジトリを破壊したりしません。

収集メトリクス

メトリクス	測定内容
合格率	エージェントはジャッジをパスするコードを生成できたか？
コスト	タスクあたりの API 費用（利用可能な場合）
時間	完了までのウォールクロック秒数
一貫性	繰り返し実行での合格率（例：3/3 = 100%）

ワークフロー

1. タスクの定義

タスクごとに 1 つの YAML ファイルを持つ tasks/ ディレクトリを作成します：

bash

mkdir tasks
# タスク定義を作成（上記のテンプレートを参照）

2. エージェントの実行

タスクに対してエージェントを実行します：

bash

agent-eval run --task tasks/add-retry-logic.yaml --agent claude-code --agent aider --runs 3

各実行：

指定されたコミットから新しい git ワークツリーを作成
エージェントにプロンプトを渡す
ジャッジ基準を実行
合格・不合格、コスト、時間を記録

3. 結果の比較

比較レポートを生成します：

bash

agent-eval report --format table

Task: add-retry-logic (3 runs each)
┌──────────────┬───────────┬────────┬────────┬─────────────┐
│ Agent        │ Pass Rate │ Cost   │ Time   │ Consistency │
├──────────────┼───────────┼────────┼────────┼─────────────┤
│ claude-code  │ 3/3       │ $0.12  │ 45s    │ 100%        │
│ aider        │ 2/3       │ $0.08  │ 38s    │  67%        │
└──────────────┴───────────┴────────┴────────┴─────────────┘

ジャッジタイプ

コードベース（決定論的）

yaml

judge:
  - type: pytest
    command: pytest tests/ -v
  - type: command
    command: npm run build

パターンベース

yaml

judge:
  - type: grep
    pattern: "class.*Retry"
    files: src/**/*.py

モデルベース（LLM-as-judge）

yaml

judge:
  - type: llm
    prompt: |
      Does this implementation correctly handle exponential backoff?
      Check for: max retries, increasing delays, jitter.

ベストプラクティス

3〜5 タスクから始める — おもちゃの例ではなく、実際のワークロードを代表するタスク
エージェントごとに少なくとも 3 試行実行する — エージェントは非決定論的なので分散を把握する
タスク YAML でコミットを固定する — 日や週をまたいで結果が再現可能になる
タスクごとに少なくとも 1 つの決定論的ジャッジを含める（テスト、ビルド）— LLM ジャッジはノイズを加える
合格率と一緒にコストを追跡する — 10 倍のコストで 95% のエージェントが正しい選択でない場合もある
タスク定義をバージョン管理する — それらはテストフィクスチャであり、コードとして扱う

リンク

リポジトリ: github.com/joaquinhuigomez/agent-eval

Files

Just SKILL.md in docs/ja-JP/skills/agent-eval of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Compare with similar skills

Agent Eval next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Eval compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Agent Eval this skillaffaan-m/ECC	276k	—	~786	Automated safety check: Pass	MIT	4 days ago
Finishing a Development Branchobra/superpowers	297k	5 repos	~1.9k	Automated safety check: Pass	MIT	yesterday
Code Review ChecklistshareAI-lab/learn-claude-code	78k	5 repos	~1.1k	Automated safety check: Pass	MIT	11 days ago
Code Design Rationale Investigatorcursor/plugins	10k	9 repos	~2.6k	Automated safety check: Pass	None	2 days ago
Contributor-First PR MergeHKUDS/OpenHarness	16k	1 repos	~847	Automated safety check: Pass	MIT	4 mo ago
Finishing A Development Branchfarm-fe/farm	5.6k	34 repos	~1.8k	Automated safety check: Pass	MIT	17 days ago

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Finishing a Development Branch
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297k GitHub starsUsed in 5 repos~1.9k tokens
DevelopmentAuto-check passed
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Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
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Code Design Rationale Investigator
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Contributor-First PR Merge
HKUDS/OpenHarness
Merges external GitHub pull requests while keeping the original author credited, and fixes conflicts after the merge instead of rewriting the contribution.
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Finishing A Development Branch
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Questions about Agent Eval

What does Agent Eval do?

カスタムタスクでコーディングエージェント（Claude Code、Aider、Codex など）をヘッドツーヘッドで比較し、合格率、コスト、時間、一貫性のメトリクスを測定します. Agent Eval is an agent skill from affaan-m/ECC.

When should I use Agent Eval?

Agent Eval fits situations like: development work in your project.

How do I install Agent Eval in Claude Code?

Run `npx skills add affaan-m/ECC --skill agent-eval -a claude-code`. Or copy the skill folder (docs/ja-JP/skills/agent-eval in affaan-m/ECC) into .claude/skills/agent-eval in your project. Claude Code loads it when a task matches its description.

How do I install Agent Eval in Codex?

Run `npx skills add affaan-m/ECC --skill agent-eval -a codex`. Or copy the skill folder (docs/ja-JP/skills/agent-eval in affaan-m/ECC) into .agents/skills/agent-eval in your project. Codex loads it when a task matches its description.

Can I use Agent Eval in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill agent-eval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-eval, .gemini/skills/agent-eval, .github/skills/agent-eval and .opencode/skills/agent-eval in your project.

What does Agent Eval need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Eval is instructions for the agent only. Our summary lists: Docker.

Does Agent Eval access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Agent Eval safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Eval use?

Agent Eval is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Eval use?

About 786 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Eval?

Skills that share tags, products or a category with Agent Eval: Finishing a Development Branch (obra/superpowers, 297k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Code Design Rationale Investigator (cursor/plugins, 10k stars) and Contributor-First PR Merge (HKUDS/OpenHarness, 16k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Eval?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,546 GitHub stars. The repository holds 673 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Agent Eval

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

エージェント評価スキル

起動タイミング

インストール

コアコンセプト

YAML タスク定義

Git ワークツリー分離

収集メトリクス

ワークフロー

1. タスクの定義

2. エージェントの実行

3. 結果の比較

ジャッジタイプ

コードベース（決定論的）

パターンベース

モデルベース（LLM-as-judge）

ベストプラクティス

リンク

Files

Compare with similar skills

Similar skills

More from affaan-m/ECC

Related

Questions about Agent Eval