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generate_skill_tests

Generates YAML test cases from a SKILL.md file, covering explicit, implicit, contextual, and negative categories to validate AI agent skills.

Instructions

Auto-generate test cases from a SKILL.md file. Call this when the user asks to create tests for a skill — it reads the skill definition and generates a ready-to-run YAML test suite covering explicit, implicit, contextual, and negative test categories. After generating, call run_skill_test to execute them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of test cases to generate (default: 10)
skill_pathYesPath to the SKILL.md file to generate tests from
output_pathNoWhere to save the generated test YAML (default: same directory as SKILL.md)
Behavior3/5

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

Without annotations, the description partially covers behavior: it reads a file and generates a test suite. However, it omits details on whether it modifies existing files, error handling on invalid paths, or permission requirements. The mention of 'ready-to-run' implies creation but not side effects.

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?

Two sentences efficiently convey purpose, usage context, and follow-up action with no redundancy. Every sentence earns its place, making it highly concise and structured.

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

Completeness4/5

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

Given 3 parameters, no annotations, and no output schema, the description adequately covers the tool's function and workflow. It mentions test categories and suggests next steps, but lacks details on validation, error handling, or prerequisites.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description does not add extra meaning beyond the schema; defaults mentioned in the description are also present in schema parameter descriptions, providing no additional value.

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 the tool auto-generates test cases from a SKILL.md file, specifying categories like explicit, implicit, contextual, and negative. This distinguishes it from siblings like 'create_test' or 'run_skill_test' by linking it directly to skill auto-test generation.

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?

Explicitly advises to call when the user asks to create tests for a skill, and recommends subsequent use of 'run_skill_test'. This provides clear context, though it does not explicitly mention when not to use or compare with alternatives like 'create_test'.

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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