Guide · Agent skill
How to use the LLM Evaluation Strategies agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The llm-evaluation skill is a set of instructions that teaches an AI agent how to evaluate LLM-based applications systematically. Once it is installed, the agent can recommend and write evaluation setups that combine automated metrics, human rating criteria and LLM-as-judge scoring. This guide explains what the skill does, how to install it, how to trigger it, and what you need before relying on it.
LLM Evaluation Strategieswshobson/agents · MIT Download the skill Quick start
- 1Download the ZIP. Download the llm-evaluation skill ZIP from this page.
- 2Install the skill. Unzip it into ~/.claude/skills/ for personal use or into your project's .claude/skills/ folder, or upload the ZIP under Settings → Capabilities → Skills in the Claude apps.
- 3Prepare your test cases. Collect inputs, expected answers and any context for the outputs you want to evaluate.
- 4Ask for an evaluation setup. Describe your application and what you want to measure in plain language.
- 5Supply the metric functions. Implement or import the metrics the agent recommends, such as calculate_bleu or check_groundedness, since the skill does not provide them.
- 6Run the suite and review scores. Run the EvaluationSuite over your test cases and compare the averaged metrics and raw scores.
What is an agent skill and what does this one make the agent do?
An agent skill is a folder with a SKILL.md file that gives an AI agent specialised instructions for a task. When a request matches the skill's description, the agent loads those instructions and follows them.
This skill covers three kinds of evaluation. Automated metrics compute scores such as BLEU, ROUGE, METEOR, BERTScore, perplexity, accuracy, precision/recall/F1 and retrieval measures like MRR and NDCG. Human evaluation covers dimensions such as factual accuracy, coherence, relevance, fluency, safety and helpfulness. LLM-as-judge uses a stronger model to grade a weaker model's outputs, either pointwise, pairwise, reference-based or reference-free.
How do you install the llm-evaluation skill?
Download the ZIP for this skill from this page. Then install it using one of these options.
For a personal install that works across your projects, unzip the download into ~/.claude/skills/. For a single project, unzip it into that project's .claude/skills/ folder. In the Claude apps, you can instead upload the ZIP under Settings → Capabilities → Skills.
- Personal: unzip into ~/.claude/skills/
- Single project: unzip into your project's .claude/skills/
- Claude apps: Settings → Capabilities → Skills, then upload the ZIP
How do you trigger the skill with a natural request?
You do not need a special command. Describe the evaluation task in plain language and the agent will load the skill when the request matches its description. The description covers testing LLM performance, measuring AI application quality and establishing evaluation frameworks.
Name the thing you are evaluating and what you care about. Mentioning a chatbot, summariser or RAG search feature, plus the outputs you have, gives the agent enough context to pick a suitable setup.
- Set up an evaluation suite for my RAG search feature that checks groundedness and retrieval quality.
- Compare my two prompt versions for the support chatbot and give me a repeatable scorecard.
- Help me add a regression test so a prompt change does not break the summarizer's behaviour.
What does the example code look like?
The skill includes a basic Python pattern. A Metric class wraps a named scoring function, and an EvaluationSuite runs a model over a list of test cases, applies each metric and returns the averaged results along with the raw scores.
The pattern expects you to supply the metric functions yourself, such as calculate_accuracy, calculate_bleu, calculate_bertscore and check_groundedness. The skill does not provide these. Test cases are dictionaries with an input, an expected answer and optional context. The example uses numpy for averaging.
- Each test case has input, expected and context keys
- Custom metrics can be added with Metric.custom(name, fn)
- The result contains a metrics dictionary of averages and raw_scores per metric
suite = EvaluationSuite([Metric.accuracy(), Metric.bleu(), Metric.bertscore(), Metric.custom("groundedness", check_groundedness)])
results = await suite.evaluate(model=your_model, test_cases=test_cases)What do you need before you start?
You need Python with numpy, because the example code uses it. You also need the metric implementations, which you must write or import yourself. The reference file shows example implementations of BLEU using nltk and ROUGE using rouge_score, but those are examples in the reference notes rather than packages the skill installs.
You also need an LLM or model object to evaluate. For LLM-as-judge, you need a judge model with its own API key or account.
- Python with numpy
- Metric functions you supply, such as calculate_bleu, calculate_bertscore and check_groundedness
- A model object to evaluate
- For LLM-as-judge: a judge model with its own API key or account
What about safety, scripts and network use?
This skill does not run scripts and does not use the network. It consists of instructions and example code that you run in your own environment.
Be careful with sensitive data. LLM-as-judge setups send test inputs and outputs to a model provider, which may create privacy or compliance issues. Check this before you use real customer data.
- Runs scripts: no
- Uses network: no
- LLM-as-judge sends test inputs and outputs to a model provider
Tips for getting useful results
Do not rely on BLEU and ROUGE alone for creative or subjective output. They measure word overlap and can mislead. Pair them with human criteria or LLM-as-judge scoring.
Keep a fixed set of test cases so you can rerun the suite after each prompt or model change. This is what makes a regression test or scorecard repeatable.
- Pair word-overlap metrics with human or judge-based criteria for subjective output
- Reuse the same test cases so scores are comparable across runs
- Read the reference notes for more implementation patterns when the main file is not enough
What people use it for
- Checking a RAG search feature for groundedness and retrieval quality before release
- Comparing two prompt versions of a support chatbot with a repeatable scorecard
- Adding regression tests so prompt edits do not silently change behaviour
- Scoring summarizer outputs with ROUGE alongside human criteria such as coherence and relevance
Frequently asked questions
Is the llm-evaluation skill free to use?
The skill is published under the MIT licence, as noted on this page. Check the licence file in the source repository for the full terms.
What do I need to install the llm-evaluation skill?
Download the ZIP from this page and unzip it into ~/.claude/skills/ or a project's .claude/skills/ folder, or upload it under Settings → Capabilities → Skills in the Claude apps. No scripts run and no network access is used by the skill itself.
Does the skill include the BLEU, ROUGE and BERTScore functions?
No. The skill's example code calls metric functions such as calculate_bleu, calculate_bertscore and check_groundedness, which you must supply. The reference notes show example BLEU and ROUGE implementations using nltk and rouge_score.
Do I need an API key to use it?
Not for the basic metric pattern. LLM-as-judge scoring requires a judge model with its own API key or account, and it sends test inputs and outputs to a model provider.
How is this different from just asking an AI to rate one answer?
The skill is aimed at building repeatable evaluation pipelines over many test cases, not casual review of a single answer. It is designed for regression tests and comparing models or prompt versions.
Why are my BLEU or ROUGE scores misleading?
These metrics measure word overlap, so a correct answer phrased differently can score low. For creative or subjective output, combine them with human criteria or LLM-as-judge scoring.
New agent skills, repos and AI tools — every week
The best new skills and open-source projects, the fastest-growing repos, and one free prompt. Five minutes to read. Unsubscribe anytime.
Our verdict
Use this skill if you are a developer building a chatbot, summariser or RAG feature and need a repeatable way to check quality before release. Pick something else if you only want a one-off opinion on a single answer, or if your evaluation data is too sensitive to send to a model provider for LLM-as-judge scoring.
Download the skill