AI Eval Skills
Help coding agents build product specific evaluations while avoiding common eval mistakes.
- Source repo
- ai-evals-course/evals-skills
- Stars
- ★ 1.5k
- Last updated
- 13d ago
- License
- Apache-2.0
- FA score
- 52/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- An engineering team with an existing product eval pipeline that wants to find setup problems and prioritize fixes.
- Not a fit if
- Teams looking for a hosted evaluation service
- Teams focused on foundation model benchmarks
- Source review
- 52/100 · Major gaps
What does this agent do, and when should you use it?
Eval Skills is a set of skills for AI coding agents focused on product specific AI evaluations, rather than foundation model benchmarks. Its `evals-start` entry point routes users according to their current evaluation work. Teams with an existing eval pipeline can use `eval-audit` to inspect the setup and get prioritized recommendations; teams with unanalyzed traces can use `error-discovery` for error analysis. Other skills cover synthetic test data, code checks, LLM-as-Judge, evaluator calibration, RAG quality, and human review interfaces. The error discovery workflow reads JSONL, CSV, or JSON data and builds a single file HTML review app served by a Python standard library server; deployment and model provider support are not specified.
evals-start routes users to a skill based on their evaluation situation. eval-audit inspects an existing evaluation pipeline and surfaces prioritized recommendations. error-discovery reads JSONL, CSV, or JSON files containing LLM outputs or traces, identifies the content type, designs visual encoding, clusters records and selects diverse samples, then builds a single file HTML review app served by a Python standard library server. Users leave free text annotations in the app; the agent organizes failure modes, tracks coverage, and proposes additional samples. The remaining skills generate synthetic inputs, write code checks for objective failures, design LLM-as-Judge prompts, calibrate evaluators using data splits, TPR/TNR, and bias correction, evaluate RAG retrieval and generation, and build human trace annotation interfaces.
- An engineering team with an existing product eval pipeline that wants to find setup problems and prioritize fixes.
- A product team with LLM outputs or agent traces that needs to discover failure modes before writing evals.
- An eval engineer who needs diverse synthetic test inputs generated across multiple dimensions.
- A developer who wants to turn objective failure conditions into code checks.
- An evaluation team designing an LLM-as-Judge for subjective criteria and calibrating it against human labels.
- A team assessing RAG retrieval and generation separately, or building an interface for human trace review.
How do you install or deploy this agent?
The README uses npx skills to install the skill set and documents commands for installing all skills or only error-discovery. This requires an environment that can run npx; the README does not specify a Node.js version, model credentials, or supported coding agent products.
How do you use this agent?
After installation, give a dataset to an AI coding agent that supports these skills and ask it to analyze errors. The README's first task example is:
What are this agent's strengths and limitations?
- The
evals-startentry point routes users by their current situation, separating pipeline audits from unanalyzed trace work. error-discoverycovers sampling, clustering, human annotation, failure mode organization, and proposing further samples in an analysis loop.- The skill set includes distinct workflows for code checks, LLM-as-Judge calibration, and RAG evaluation.
- For error discovery, the README specifies a single file HTML review app served with the Python standard library, with no extra dependencies.
- This is a skill set for AI coding agents, not a standalone hosted evaluation service; compatibility with specific agent products is unspecified.
- The README does not identify model providers, credentials, costs, or network data handling requirements.
- The Python standard library server is documented only for error discovery; runtimes and deployment for other skills are not detailed.
- Beyond install and update commands, the README does not provide complete parameters, configuration, or output formats for each skill.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AI Eval Skills This agent | 52 · Major gaps | Agent plugin / skill | ★ 1.5k | 13d ago | — | — |
| Giskard | 47 · Major gaps | Library / SDKFree + model costs | ★ 5.9k | 1d ago | Python | OpenAI API · Claude API |
| Oumi | 58 · Major gaps | CLIFree + model costs | ★ 9.4k | today | Python | Claude Code · OpenAI API |
| Voice Lab | 27 · Major gaps | CLIFree + model costs | ★ 175 | 1y ago | Python | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
The README says error-discovery reads JSONL/CSV/JSON data, builds a local HTML review app, and serves it with Python's standard library, but it does not explain permission boundaries or data storage and transfer details. Least privilege and sensitive-data handling are therefore only partially addressed. There is no explicit confirmation mechanism, external-effects account, or rollback guidance. The README mentions a course and related materials, but publisher identity is unverified and no maintenance owner is named, so source attribution is limited.
The README's descriptions of the skill entry point, common paths, and stated workflow are broadly consistent. It says the local review app has no extra runtime dependencies, though installation still relies on npx. Error handling and failure messages are not described, so that criterion receives no credit.
The material covers scenarios including existing eval pipelines, trace analysis, synthetic data, code evaluation, judge prompts, RAG, and human review, and distinguishes product evaluations from foundation-model benchmarks. evals-start is described as a routing entry point, but its detailed triggers are not present in the supplied material. Environment guidance is mainly limited to npx and Python's standard library, with little compatibility detail.
The README has a clear skill directory, purpose table, and install/update commands, plus an error-discovery example. Names are recognizable, but there is no FAQ, full limitations section, or change log. The LICENSE file supplies Apache 2.0 terms. Update commands are provided, but no maintenance owner or process is identified.
The documented workflow from sample selection and clustering through annotation to failure-mode synthesis appears usable, and the claimed local review app has no extra dependencies. Scores are limited because concrete outputs, boundaries, and evidence of cost savings are sparse; the benefits are mainly README claims.
The README makes specific claims about skill purposes and workflow and links to skill files, but those files are not included in the supplied evidence and there is no independent corroboration. Explicitly documented statements can be distinguished from implementation that was not shown; the limited evidence base constrains cross-checking.
- The supplied material contains only the README and LICENSE; the skill instructions themselves are absent, so data handling, confirmation steps, and error handling cannot be verified.
- The README describes analyzing user-provided traces and generating a review interface. Review the actual skill instructions and data flow before using sensitive or regulated data.
FAQ
Can I use it for foundation model benchmarks?
Which skill should I start with?
evals-start, which routes based on your situation. Use eval-audit for an existing evaluation pipeline or error-discovery for traces you have not analyzed.