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evaluate_skill

Runs the Anthropic skill-creator eval loop to assess and optimize a skill using an evaluation dataset.

Instructions

Runs Anthropic skill-creator eval loop for a skill (requires Python, Claude CLI auth, and an eval set JSON; legacy layouts may also require ANTHROPIC_API_KEY + anthropic package).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skill_nameYesThe skill directory name to evaluate
eval_set_pathNoOptional path to eval set JSON. If omitted, common default locations are checked.
max_iterationsNoOptional max optimization iterations
num_workersNoOptional evaluator parallel workers (defaults to 1 for stable trigger measurements)
runs_per_queryNoOptional repeats per query (defaults to 1; increase for variance analysis)
timeout_secondsNoOptional timeout per query in seconds (defaults to 120)
holdoutNoOptional holdout fraction for run_loop test split (defaults to 0.4, use 0 to disable holdout)
trigger_thresholdNoOptional trigger-rate threshold for pass/fail decisions (defaults to 0.5)
description_overrideNoOptional starting description override for what-if optimization without editing SKILL.md first
modelNoOptional model passed to Claude CLI (defaults to "sonnet")
Behavior3/5

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

No annotations provided, so description must cover behavioral traits. It mentions prerequisites and legacy requirements, but does not disclose side effects, performance impact, or whether the tool modifies any files. This is a moderate disclosure.

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

Conciseness4/5

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

Description is a single sentence with a parenthetical, efficiently conveying the primary action and key requirements. Slightly dense, but no superfluous content; could be restructured for better readability.

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?

With 10 parameters (all optional except one), no output schema, and no annotations, the description provides essential prerequisites but lacks details on output behavior and eval loop outcomes. Adequate but not comprehensive.

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 coverage is 100%, so each parameter already has a description. The tool description adds prerequisite context but does not enhance understanding of individual parameters beyond the schema. Baseline 3 is appropriate.

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?

Description clearly states 'Runs Anthropic skill-creator eval loop for a skill', specifying the exact action and target resource. This distinguishes it from sibling tools get_skill and validate_skill, which have different purposes.

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?

Description lists prerequisites (Python, Claude CLI auth, eval set JSON) which guide usage. It implies usage for evaluation, though lacks explicit when-to-use vs siblings or when-not-to-use scenarios.

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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