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shinka-run

Run existing ShinkaEvolve tasks with the `shinka_run` CLI from a task directory (`evaluate.py` + `initial.<ext>`). Use when an agent needs to launch async evolution runs quickly with required `--results_dir`, generation count, and strict namespaced keyword overrides.

72

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Low

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SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with executable commands and a clear, well-gated workflow including validation and confirmation checkpoints for batch operations. It is slightly verbose and monolithic, with some background and repeated policy detail that could be extracted into a reference file.

Suggestions

Trim or remove the 'What is ShinkaEvolve?' background section and the repo/paper links — this is conceptual context Claude already knows and does not advance execution.

Consolidate the model-validation rules so they appear once instead of being restated across 'Important runtime rules', 'Confirm first-batch configuration', and 'Batch Control Policy'.

Move the detailed `shinka_models` membership-validation rules into a one-level-deep reference file (e.g. MODEL_VALIDATION.md) and link to it, keeping the main workflow lean.

DimensionReasoningScore

Conciseness

Mostly efficient with concrete commands, but the 'What is ShinkaEvolve?' background and the repeated model-validation/batch-confirmation rules across three sections add unnecessary tokens and could be tightened.

2 / 3

Actionability

Provides fully executable, copy-paste-ready `shinka_run` invocations with specific real flags and values, plus concrete helper commands (`shinka_models`, `shinka_run --help`).

3 / 3

Workflow Clarity

A clear 7-step sequence with explicit validation checkpoints — file existence checks, `shinka_models` membership validation, a 'stop and ask' recovery loop, and mandatory user confirmation before batch execution.

3 / 3

Progressive Disclosure

Well-organized into sections with no nested references, but it is a single monolithic ~110-line file with detailed inline model-validation rules and framework background that could be split into a one-level-deep reference.

2 / 3

Total

10

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12

Passed

Description

100%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, complete, and distinctive, clearly stating both what the skill does and when to use it with concrete CLI and flag references. It is concise and free of fluff or over-claims.

DimensionReasoningScore

Specificity

Names multiple concrete actions — running ShinkaEvolve tasks via the `shinka_run` CLI from a task directory, launching async evolution runs with required `--results_dir`, generation count, and namespaced keyword overrides.

3 / 3

Completeness

Explicitly answers both what ('Run existing ShinkaEvolve tasks...') and when via an explicit 'Use when an agent needs to launch async evolution runs quickly...' trigger clause.

3 / 3

Trigger Term Quality

Strong coverage of natural domain terms a user would say — 'ShinkaEvolve tasks', 'shinka_run CLI', 'task directory', 'evolution runs', '--results_dir'.

3 / 3

Distinctiveness Conflict Risk

Highly specific niche tied to a named framework and CLI with distinctive triggers (`evaluate.py` + `initial.<ext>`), unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
SakanaAI/ShinkaEvolve
Reviewed

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