eval-harness
Evaluation harness for testing agent and skill quality through structured benchmarks, regression tests, and quality scoring.
What this skill does
# Eval Harness ## Overview Evaluation harness methodology adapted from the Everything Claude Code project. Provides structured frameworks for benchmarking agent performance, testing skill quality, and running regression suites. ## Evaluation Types ### 1. Agent Performance Benchmark - Define test cases with known-correct outputs - Run agent against each test case - Score: accuracy, completeness, relevance - Compare against baseline performance - Track performance over time ### 2. Skill Quality Testing - Verify skill instructions produce expected outcomes - Test edge cases and boundary conditions - Measure consistency across multiple runs - Check for harmful or incorrect outputs - Validate against ground truth ### 3. Regression Suite - Collection of previously-passing test cases - Run after any agent/skill modification - Flag regressions with before/after comparison - Maintain pass rate threshold (>= 95%) ### 4. Process Verification - End-to-end process execution with known inputs - Verify each phase produces expected outputs - Check task ordering and dependency satisfaction - Measure total execution time ## Quality Scoring ### Accuracy Score (0-100) - Correctness of output vs expected - Partial credit for partially correct outputs - Penalty for hallucinated or fabricated content ### Completeness Score (0-100) - Coverage of required output elements - Missing sections flagged and scored - Bonus for useful additional context ### Consistency Score (0-100) - Run same input 3 times - Compare outputs for semantic similarity - Flag inconsistencies ### Composite Score - (accuracy * 0.4 + completeness * 0.3 + consistency * 0.3) - Threshold: 80 to pass ## When to Use - After creating new agents or skills - After modifying existing agents or skills - Periodic quality audits - Before promoting skills to production ## Agents Used - Used by process-level evaluation orchestrators - No specific agent dependency (evaluates other agents)
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
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agent-skill-creator
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llm-wiki
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skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.