llm-evaluation
Provides strategies to evaluate, benchmark, and test LLM performance systematically.
Install
mkdir -p .claude/skills/llm-evaluation-h4d3zs && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13370" && unzip -o skill.zip -d .claude/skills/llm-evaluation-h4d3zs && rm skill.zipInstalls to .claude/skills/llm-evaluation-h4d3zs
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.Key capabilities
- →Measure LLM application performance systematically
- →Compare different models or prompts
- →Detect performance regressions before deployment
- →Validate improvements from prompt changes
- →Establish baselines and track progress over time
- →Debug unexpected model behavior
How it works
This skill provides strategies for evaluating LLM applications using automated metrics, human feedback, and benchmarking to assess performance and quality.
Inputs & outputs
When to use llm-evaluation
- →Benchmarking LLM prompts
- →Monitoring AI model quality
- →Testing for performance regressions
About this skill
LLM Evaluation
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
Do not use this skill when
- The task is unrelated to llm evaluation
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Use this skill when
- Measuring LLM application performance systematically
- Comparing different models or prompts
- Detecting performance regressions before deployment
- Validating improvements from prompt changes
- Building confidence in production systems
- Establishing baselines and tracking progress over time
- Debugging unexpected model behavior
Core Evaluation Types
🧠 Knowledge Modules (Fractal Skills)
1. 1. Automated Metrics
2. 2. Human Evaluation
3. 3. LLM-as-Judge
4. BLEU Score
5. ROUGE Score
6. BERTScore
7. Custom Metrics
8. Single Output Evaluation
9. Pairwise Comparison
10. Annotation Guidelines
11. Inter-Rater Agreement
12. Statistical Testing Framework
13. Regression Detection
14. Running Benchmarks
Limitations
- →The task must be related to LLM evaluation
- →The skill does not cover domains or tools outside its scope
How it compares
This skill offers structured evaluation types and sub-skills for LLM applications, unlike generic performance testing.
Compared to similar skills
llm-evaluation side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| llm-evaluation (this skill) | 0 | 3mo | No flags | Intermediate |
| Plate Evaluation | 0 | 5mo | Review | Intermediate |
| model-change | 0 | 4mo | Review | Intermediate |
| llm-evaluation | 6 | 2mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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