Agent Skill to Agent Eval
Agent Skill to Agent Eval is a verified SKILL blueprint on Promptally in Fullstack & Backend Engineering optimized for leading AI models. Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression. Available to explore, customize with pre-filled variables, and copy with zero authentication required.
Technical Specifications
| Specification | Value |
|---|---|
| Artifact type | SKILL (Fullstack & Backend Engineering) |
| Compatible engines | Universal / Model Agnostic |
| Input variables | None (pre-filled) |
| Approximate tokens | 1,072 |
| Source file | SKILL.md |
| Access tier | Free public (MIT) |
Pre-Filled Template
---
name: agent-eval
description: Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.
license: MIT
metadata:
origin: ECC
tools: Read, Write, Edit, Bash, Grep, Glob
---
# 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"
files: src/**/*.py
```
### Model-Based (LLM-as-judge)
```yaml
judge:
- type: llm
prompt: |
Does this implementation correctly handle exponential backoff?
Check for: max retries, increasing delays, jitter.
```
## Best Practices
- **Start with 3-5 tasks** that represent your real workload, not toy examples
- **Run at least 3 trials** per agent to capture variance — agents are non-deterministic
- **Pin the commit** in your task YAML so results are reproducible across days/weeks
- **Include at least one deterministic judge** (tests, build) per task — LLM judges add noise
- **Track cost alongside pass rate** — a 95% agent at 10x the cost may not be the right choice
- **Version your task definitions** — they are test fixtures, treat them as code
## Links
- Repository: [github.com/joaquinhuigomez/agent-eval](https://github.com/joaquinhuigomez/agent-eval)
How to use this skill
- Review the pre-filled example and source files above.
- Copy the template or download
SKILL.md, then use it in your model, IDE, or workflow tool. - Compare your result with the sample output when one is provided.
Frequently Asked Questions
How do I use Agent Skill to Agent Eval?
Open the skill on Promptally, review the pre-filled example, then copy the template or download SKILL.md and paste it into your model, IDE or workflow tool. Agent Skill to Agent Eval is a static blueprint with no dynamic variables. Copy it and use it as-is.
Is Agent Skill to Agent Eval free?
Yes. Agent Skill to Agent Eval is free to view, copy and download on Promptally without creating an account. It is published under the MIT license.
What variables does Agent Skill to Agent Eval need?
Agent Skill to Agent Eval is a static blueprint with no dynamic variables. Copy it and use it as-is.