Best Evaluation and Benchmarks Claude Skills & MCP Tools (2026)

evaluating-llms-harness

from davila7★ 15.4k

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

[Evaluation and Benchmarks]

Skill Judge

from softaworks★ 235

A powerful skill for Claude agents.

[Evaluation and Benchmarks]

llm-evaluation

from applied-artificial-intelligence★ 12

LLM evaluation and testing patterns including prompt testing, hallucination detection, benchmark creation, and quality metrics. Use when testing LLM applications, validating prompt quality, implementing systematic evaluation, or measuring LLM performance.

[Evaluation and Benchmarks]

skill-builder

from dparedesi

Create, evaluate, and improve Agent skills to production quality (100/100). Use when the user wants to create a new skill, review an existing skill, score a skill against best practices, or improve a skill's quality. Also use when the user mentions skill development, skill templates, or skill optimization.

[Evaluation and Benchmarks]

evaluating-llms-harness

from zechenzhangAGI★ 62

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

[Evaluation and Benchmarks]

llm-evaluation

from wshobson★ 24.2k

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.

[Evaluation and Benchmarks]

evaluating-code-models

from davila7★ 15.4k

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

[Evaluation and Benchmarks]

evaluating-code-models

from zechenzhangAGI★ 481

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

[Evaluation and Benchmarks]

testing-skills-with-subagents

from LerianStudio★ 25

Skill testing methodology - run scenarios without skill (RED), observe failures,write skill (GREEN), close loopholes (REFACTOR).

[Evaluation and Benchmarks]

grey-haven-evaluation

from greyhaven-ai★ 15

Evaluate LLM outputs with multi-dimensional rubrics, handle non-determinism, and implement LLM-as-judge patterns. Essential for production LLM systems. Use when testing prompts, validating outputs, comparing models, or when user mentions 'evaluation', 'testing LLM', 'rubric', 'LLM-as-judge', 'output quality', 'prompt testing', or 'model comparison'.

[Evaluation and Benchmarks]

skill-validator

from panaversity★ 15

Validates skills against production-level criteria with 9-category scoring.This skill should be used when reviewing, auditing, or improving skills toensure quality standards. Evaluates structure, content, user interaction,documentation, domain standards, technical robustness, maintainability,zero-shot implementation, and reusability. Returns actionable validationreport with scores and improvement recommendations.

[Evaluation and Benchmarks]

skill-evaluator

from bjulius★ 11

Comprehensive evaluation toolkit for analyzing Claude skills across security, quality, utility, and compliance dimensions. This skill should be used when users need to evaluate a skill before installation, review before publishing, or assess overall quality and safety. Performs 5-layer security analysis, validates structure and documentation, checks compliance with skill-creator guidelines, and generates markdown reports with scoring and recommendations.

[Evaluation and Benchmarks]

evaluation

from muratcankoylan★ 10

Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.

[Evaluation and Benchmarks]

honest-reflections

from majiayu000★ 5

Systematic gap analysis for claimed vs actual work completion. Uses 100+ sequential thoughtsto identify assumptions, partial completions, missing components, and rationalization patterns.Validates completion claims against original plans, detects scope deviations, reveals quality gaps.Essential for self-assessment before declaring work complete. Use when: claiming completion,final reviews, quality audits, detecting rationalization patterns in own work.

[Evaluation and Benchmarks]

ai-evaluation-suite

from majiayu000★ 5

Comprehensive AI/LLM evaluation toolkit for production AI systems. Covers LLM output quality, prompt engineering, RAG evaluation, agent performance, hallucination detection, bias assessment, cost/token optimization, latency metrics, model comparison, and fine-tuning evaluation. Includes BLEU/ROUGE metrics, perplexity, F1 scores, LLM-as-judge patterns, and benchmarks like MMLU and HumanEval.

[Evaluation and Benchmarks]

prompt-testing-evaluation

from daishiman★ 3

プロンプトのテスト、評価、反復改善を専門とするスキル。A/Bテスト、評価メトリクス、自動化されたプロンプト品質保証により、本番環境で信頼性の高いプロンプトを実現します。Anchors:• Test-Driven Development: By Example (Kent Beck) / 適用: Red-Green-Refactorサイクル / 目的: 反復的な品質改善• LLM-as-a-Judge pattern / 適用: 自動評価とスコアリング / 目的: スケーラブルな品質評価• A/B Testing for AI Systems / 適用: プロンプト比較実験設計 / 目的: データドリブンな改善Trigger:Use when testing prompts, evaluating prompt quality, running A/B tests on prompts, implementing automated prompt evaluation, or establishing continuous prompt improvement cycles.Keywords: prompt testing, A/B testing, evaluation metrics, LLM-as-a-judge, prompt quality, automated evaluation, regression testing

[Evaluation and Benchmarks]

llm-evaluation

from amurata★ 3

自動メトリクス、人間によるフィードバック、およびベンチマークを使用して、LLMアプリケーションの包括的な評価戦略を実装します。LLMのパフォーマンスをテストし、AIアプリケーションの品質を測定し、または評価フレームワークを確立する場合に使用します。

[Evaluation and Benchmarks]

leverage-point-audit

from melodic-software★ 3

Audit a codebase for the 12 leverage points of agentic coding. Identifies gaps and provides prioritized recommendations. Use when improving agentic coding capability, analyzing why agents fail, or optimizing a codebase for autonomous work.

[Evaluation and Benchmarks]

testing-skills-with-subagents

from spacholski1225★ 2

RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes

[Evaluation and Benchmarks]

tweaktune-synthesizer

from qooba★ 2

Interactive assistant for designing and generating tweaktune pipelines to synthesize training data for LLMs. Use when user wants to create synthetic datasets for fine-tuning, generate conversations, function calling data, or structured JSON datasets.

[Evaluation and Benchmarks]

evaluation-harness

from patricio0312rev★ 2

Builds repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmarking".

[Evaluation and Benchmarks]

llm-evaluation

from phrazzld★ 2

LLM prompt testing, evaluation, and CI/CD quality gates using Promptfoo.Invoke when:- Setting up prompt evaluation or regression testing- Integrating LLM testing into CI/CD pipelines- Configuring security testing (red teaming, jailbreaks)- Comparing prompt or model performance- Building evaluation suites for RAG, factuality, or safetyKeywords: promptfoo, llm evaluation, prompt testing, red team, CI/CD, regression testing

[Evaluation and Benchmarks]

spellbook-auditing

from axiomantic★ 1

Meta-audit skill for spellbook development. Spawns parallel subagents to factcheck docs, optimize instructions, find token savings, and identify MCP candidates. Produces actionable report.

[Evaluation and Benchmarks]

evaluation-metrics

from pluginagentmarketplace★ 1

LLM evaluation frameworks, benchmarks, and quality metrics for production systems.

[Evaluation and Benchmarks]

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