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agent-eval

★ 220K repomlN/AIntermediateClaude
🤖 AI Summary

**agent-eval** is a CLI tool that runs head-to-head comparisons of coding agents (e.g., Claude Code, Aider) on reproducible YAML-defined tasks, scoring each agent's output against success criteria to produce data-backed performance metrics for tool selection and regression testing.

How to Install

Claude Code:
git clone --depth 1 https://github.com/affaan-m/ECC.git && cp ECC/skills/agent-eval ~/.claude/skills/agent-eval -r
# Agent Eval Skill A lightweight CLI tool for comparing coding agents head-to-head on reproducible tasks. Every "which coding agent is best?" comparison runs on vibes — this tool systematizes it. ## When to Activate - Comparing coding agents (Claude Code, Aider, Codex, etc.) on your own codebase - Measuring agent performance before adopting a new tool or model - Running regression checks when an agent updates its model or tooling - Producing data-backed agent selection decisions for a team ## Installation > **Note:** Install agent-eval from its repository after reviewing the source. ## Core Concepts ### YAML Task Definitions Define tasks declaratively. Each task specifies what to do, which files to touch, and how to judge success: ```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" # pin to specific commit for reproducibility ``` ### Git Worktree Isolation Each agent run gets its own git worktree — no Docker required. This provides reproducibility isolation so agents cannot interfere with each other or corrupt the base repo. ### Metrics Collected | Metric | What It Measures | |--------|-----------------| | Pass rate | Did the agent produce code that passes the judge? | | Cost | API spend per task (when available) | | Time | Wall-clock seconds to completion | | Consistency | Pass rate across repeated runs (e.g., 3/3 = 100%) | ## Workflow ### 1. Define Tasks Create a `tasks/` directory with YAML files, one per task: ```bash mkdir tasks # Write task definitions (see template above) ``` ### 2. Run Agents Execute agents against your tasks: ```bash agent-eval run --task tasks/add-retry-logic.yaml --agent claude-code --agent aider --runs 3 ``` Each run: 1. Creates a fresh git worktree from the specified commit 2. Hands the prompt to the agent 3. Runs the judge criteria 4. Records pass/fail, cost, and time ### 3. Compare Results Generate a comparison report: ```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% │ └──────────────┴───────────┴────────┴────────┴─────────────┘ ``` ## Judge Types ### Code-Based (deterministic) ```yaml judge: - type: pytest command: pytest tests/ -v - type: command command: npm run build ``` ### Pattern-Based ```yaml judge: - type: grep pattern: "class.*Retry" fil

Details

Category AI/ML → ml
Sourceaffaan-m/ECC
SKILL.mdView on GitHub →
Repo Stars★ 220.5K
Est. per SkillN/A (shared across 121 skills from this repo)
DifficultyIntermediate
Risk LevelN/A

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Works Well With

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