How do I install the LLM Evaluation skill?

Run npx skillmds@latest add dokhacgiakhoa/llm-evaluation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is LLM Evaluation safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with LLM Evaluation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is LLM Evaluation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published LLM Evaluation?

Dokhacgiakhoa (@dokhacgiakhoa) published this skill. Their other Agent Skills are listed on their SkillMD profile.

LLM Evaluation by dokhacgiakhoa — Agent Skill

Name: LLM Evaluation
Rating: 4.666666666666667 (3 reviews)
Author: Dokhacgiakhoa

LLM Evaluation

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.

Dokhacgiakhoa Updated Sep 9, 2026 505 repo stars

File contents

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

---name: llm-evaluationdescription: 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.--- # 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](./sub-skills/1-automated-metrics.md) ### 2. [2. Human Evaluation](./sub-skills/2-human-evaluation.md) ### 3. [3. LLM-as-Judge](./sub-skills/3-llm-as-judge.md) ### 4. [BLEU Score](./sub-skills/bleu-score.md) ### 5. [ROUGE Score](./sub-skills/rouge-score.md) ### 6. [BERTScore](./sub-skills/bertscore.md) ### 7. [Custom Metrics](./sub-skills/custom-metrics.md) ### 8. [Single Output Evaluation](./sub-skills/single-output-evaluation.md) ### 9. [Pairwise Comparison](./sub-skills/pairwise-comparison.md) ### 10. [Annotation Guidelines](./sub-skills/annotation-guidelines.md) ### 11. [Inter-Rater Agreement](./sub-skills/inter-rater-agreement.md) ### 12. [Statistical Testing Framework](./sub-skills/statistical-testing-framework.md) ### 13. [Regression Detection](./sub-skills/regression-detection.md) ### 14. [Running Benchmarks](./sub-skills/running-benchmarks.md)

Frequently asked questions

LLM Evaluation

File contents

LLM Evaluation

Do not use this skill when

Instructions

Use this skill when

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

Related skills

Frequently asked questions

How do I install the LLM Evaluation skill?

What does the LLM Evaluation skill do?

Is LLM Evaluation safe to use?

Which AI agents work with LLM Evaluation?

Is LLM Evaluation free to use?

Who published LLM Evaluation?