agent-eval
by affaan-magent-eval is a skill for benchmarking coding agents head-to-head on reproducible tasks, comparing pass rate, cost, time, and consistency. Use the agent-eval skill to evaluate Claude Code, Aider, Codex, or another agent in your own repo with clearer evidence than ad hoc prompting.
This skill scores 78/100, which means it is a solid listing candidate for directory users who want a reproducible way to compare coding agents. The repository gives enough operational detail to understand when to use it and how it works, though users should still review the source before installing because there are no supporting scripts or reference files.
- Clear activation use cases for comparing agents, regression checks, and model/tool adoption decisions.
- Concrete workflow elements: YAML task definitions, judge checks, and git worktree isolation for reproducible comparisons.
- Strong install-decision value for teams wanting data-backed agent selection instead of ad hoc comparisons.
- No install command, scripts, or support files are provided, so adoption still depends on reading the main skill file.
- The repository appears focused on one lightweight CLI workflow; users needing broader evaluation infrastructure may want more tooling.
Overview of agent-eval skill
agent-eval is a skill for benchmarking coding agents head-to-head on the same task, then comparing results by pass rate, cost, time, and consistency. If you are deciding whether to adopt Claude Code, Aider, Codex, or another agent in a real repo, the agent-eval skill helps you move from opinion to reproducible evidence.
It is best for teams and power users who need a fair comparison, not a generic “prompt it and see” test. The real job-to-be-done is to define a task once, run multiple agents against the same baseline, and judge which one performs best under your constraints.
What makes agent-eval useful
The key value of agent-eval is controlled comparison: same repo, same task, same success checks, separate worktrees. That makes the results easier to trust than ad hoc trials or one-off prompts.
When the skill fits
Use agent-eval skill when you want to:
- compare agents before standardizing a workflow
- check whether a model update changed outcomes
- test performance on your own codebase and rules
- gather decision evidence for a team or procurement choice
When it may not fit
If you only need a single coding answer, a normal prompt is simpler. agent-eval is most valuable when you care about repeatability, evaluation criteria, and tradeoffs between speed, quality, and cost.
How to Use agent-eval skill
Install and inspect the skill
For agent-eval install, add the skill from the repo and read the core skill file first:
npx skills add affaan-m/everything-claude-code --skill agent-eval
Then open SKILL.md and any linked context you use in your workflow. In this repository, the main source is the skill file itself, so the install decision depends heavily on whether its task model matches your evaluation process.
Turn a vague goal into a usable task
agent-eval usage works best when you define a concrete task, a target repo, and objective checks. A weak prompt is “test which agent is better at refactoring.” A stronger prompt is:
- add retry logic to
src/http_client.py - pin the repo to a commit for reproducibility
- specify files that may change
- define judge commands such as
pytestorgrep - state the maximum acceptable time or cost if that matters
The more the task can be verified automatically, the more useful the comparison.
Suggested workflow
A practical agent-eval guide is:
- Pick one task that reflects a real decision you need to make.
- Write the task in YAML with repo path, files, prompt, and judges.
- Run multiple agents on the same task.
- Compare output quality, execution time, and cost.
- Repeat with another task before making a final choice.
The skill uses git worktree isolation, which helps avoid agents stepping on each other’s changes and makes side-by-side evaluation cleaner.
Read these files first
Start with:
SKILL.mdfor the task format and workflow- any repo-local files that define your testing or judging rules
- the files named in your YAML task definition
If you are evaluating agent-eval for Model Evaluation specifically, confirm that your tasks and judges are stable enough to produce comparable runs before you invest in larger benchmarks.
agent-eval skill FAQ
Is agent-eval only for coding-agent benchmarks?
Yes, primarily. The skill is designed for head-to-head coding agent comparison, not general prompt testing or broad LLM benchmarking.
Do I need Docker to use it?
No. The skill uses git worktree isolation, so you can keep runs separated without container overhead.
Is it beginner friendly?
It is approachable if you can define a task clearly and run a command-line workflow. It is less suited to users who want a one-click evaluator with no setup.
How is this different from a normal prompt?
A normal prompt asks one agent to solve one task. agent-eval skill asks multiple agents to solve the same task with fixed judges so you can compare outcomes with less bias.
How to Improve agent-eval skill
Use stronger task definitions
The best agent-eval results come from tasks with clear inputs, clear edit boundaries, and objective judges. If your prompt is too open-ended, the comparison will mostly measure interpretation differences instead of agent quality.
Add judges that reflect real success
Prefer checks that mirror how your team actually validates changes: tests, lint, file diffs, or pattern checks. If the judge is too loose, weak solutions can look good; if it is too strict, you may reward brittle hacks.
Iterate on the benchmark, not the answer
If one agent wins for the wrong reason, revise the task before drawing conclusions. Tighten the files list, clarify acceptance criteria, and pin the commit so the agent-eval skill measures the same target every time.
Watch for common failure modes
The most common mistakes are vague prompts, mismatched judges, and tasks that are too large for a fair comparison. For better agent-eval usage, keep the first benchmark small, reproducible, and representative of the work you actually want agents to do.