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run_skill_test

Executes a two-phase evaluation of a SKILL.md: deterministic checks on tool calls, file ops, and token budgets, then LLM-as-judge rubric scoring for output quality.

Instructions

Run a skill test suite against a SKILL.md. Executes two evaluation phases: Phase 1 (deterministic) checks tool calls, file operations, commands run, output content, and token budgets. Phase 2 (rubric) uses LLM-as-judge to score output quality against a defined rubric. Call this after writing skill tests or after any change to the skill or agent. Use --no-rubric for fast Phase 1-only checks with no LLM cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_fileYesPath to the skill test YAML file (e.g. 'tests/my-skill-tests.yaml')
agentNoAgent type to test against: 'claude-code', 'system-prompt', 'codex', 'langgraph', 'crewai', 'openai-assistants', 'custom'. Defaults to value in YAML.
no_rubricNoSkip Phase 2 rubric evaluation — run deterministic checks only (faster, no LLM cost). Default: false.
modelNoModel to use for evaluation (default: claude-sonnet-4-20250514)
verboseNoShow detailed output for all tests, not just failures. Default: false.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It explains both phases, the cost implication of Phase 2, and the effect of --no-rubric. It does not detail output format or error handling, but covers key behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences with front-loaded purpose. No redundant information; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should clarify what the tool returns (e.g., test results). It mentions 'detailed output' for verbose but not default output. Also lacks prerequisities like SKILL.md existence. Could be more thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. The description adds value by explaining --no-rubric (fast, no LLM cost) and --verbose (show all tests). This goes beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Run a skill test suite against a SKILL.md' and breaks down the two evaluation phases. It distinguishes from sibling tools like create_test or list_tests by focusing on execution.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when to call ('after writing skill tests or after any change to the skill or agent') and mentions --no-rubric for fast checks. However, it does not compare to sibling tools like run_check or run_snapshot, missing a clear when-not-to-use scenario.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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