agent-evaluation
Design and implement comprehensive evaluation systems for AI agents. Use when building evals for coding agents, conversational agents, research agents, or computer-use agents. Covers grader types, benchmarks, 8-step roadmap, and production integration.
88
88%
Does it follow best practices?
Impact
81%
1.05xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-crafted skill description that excels across all dimensions. It clearly specifies concrete capabilities (evaluation systems, graders, benchmarks), includes natural trigger terms users would actually say, explicitly states both what it does and when to use it, and occupies a distinct niche in AI agent evaluation that won't conflict with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Design and implement comprehensive evaluation systems', covers 'grader types, benchmarks, 8-step roadmap, and production integration' - these are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what ('Design and implement comprehensive evaluation systems... Covers grader types, benchmarks, 8-step roadmap, and production integration') AND when ('Use when building evals for coding agents, conversational agents, research agents, or computer-use agents'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'evals', 'coding agents', 'conversational agents', 'research agents', 'computer-use agents', 'benchmarks', 'grader'. These are terms practitioners naturally use when discussing AI evaluation. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on AI agent evaluation systems with distinct triggers like 'evals', 'grader types', 'benchmarks'. Unlikely to conflict with general coding or documentation skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, actionable skill with excellent code examples and clear workflow guidance for building AI agent evaluation systems. The main weaknesses are moderate verbosity (some explanatory content Claude doesn't need) and a monolithic structure that could benefit from splitting detailed sections into separate files for better progressive disclosure.
Suggestions
Remove or condense the '7 Key Terms' table and 'Eval Evolution' table - Claude understands these concepts and they add token overhead without unique value
Split agent-type-specific strategies (coding, conversational, research, computer-use) into separate reference files linked from the main skill
Consider moving the extensive Examples section to a separate EXAMPLES.md file with a brief link from the main document
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some redundancy (e.g., the 7 Key Terms table explains concepts Claude likely knows, and some sections repeat similar information). The tables and code examples are efficient, but the overall length could be tightened. | 2 / 3 |
Actionability | Excellent actionability with fully executable Python code examples, concrete YAML configurations, specific grading functions, and copy-paste ready implementations for each agent type. The examples are complete and practical. | 3 / 3 |
Workflow Clarity | The 8-step roadmap provides clear sequencing with explicit checkpoints. Steps are numbered, validation is addressed (e.g., 'check_saturation', 'analyze_transcript'), and the graduated complexity pattern provides clear progression paths. | 3 / 3 |
Progressive Disclosure | Content is well-structured with clear sections and headers, but it's a monolithic document that could benefit from splitting detailed agent-type strategies and examples into separate reference files. The References section links to external resources but internal progressive disclosure is limited. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- supercent-io/skills-template
- Commit
fd18296
- Reviewed
Table of Contents
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