ml-training-recipe

Find implementable ML training recipes from papers, datasets, docs, and code. Use when the user wants to fine-tune, train, reproduce, or choose a practical ML method, dataset, hyperparameter setup, or benchmark recipe.

Quality

88%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

92%Weight 40%Scale 1-5

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

An efficient, well-structured delegating skill that points to a single slash-command workflow with clear agents and output. Its only weakness is that actionability rests entirely on the delegated workflow, with no inline examples of common recipe-finding cases.

Suggestions

Add one or two concrete examples of the output (e.g., a sample ranked recipe brief) so the expected result is unambiguous without inspecting the /recipe workflow.

Briefly note the criteria the /recipe workflow uses to rank recipes, so the skill communicates value even before the slash command expands.

Clarify when the optional `verifier` agent should be invoked, turning the implicit validation step into an explicit checkpoint.

Dimension	Reasoning	Score
Conciseness	Six lean lines that assume Claude's competence; every line earns its place, including the useful guard against reading a relative prompt-template path.	5 / 5
Actionability	Gives a concrete executable command ('Run the `/recipe` workflow') and names the output artifact and location, but does not illustrate common cases or the workflow's internal steps.	4 / 5
Workflow Clarity	A simple single-purpose skill whose single action is unambiguous, with agents and output destination stated; the simple-skill exception applies since no destructive/batch operation is exposed here.	5 / 5
Progressive Disclosure	Under 50 lines with no bundle files and no need for external references; the body is cleanly organized into action, agents, and output sections.	5 / 5
	Total	19 / 20 Passed

Description

83%Weight 40%Scale 1-5

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

A strong description that clearly states both capability and trigger conditions with concrete, natural-language terms. It is specific and well-scoped, with only minor room to broaden trigger synonyms and reduce overlap ambiguity.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete actions/sources ('Find implementable ML training recipes from papers, datasets, docs, and code') plus 'fine-tune, train, reproduce, or choose', giving good concrete coverage with minor gaps.	4 / 5
Completeness	Explicitly answers both what ('Find implementable ML training recipes from papers, datasets, docs, and code') and when ('Use when the user wants to fine-tune, train, reproduce, or choose...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Includes natural user terms ('fine-tune, train, reproduce, choose ... method, dataset, hyperparameter setup, benchmark recipe') with synonyms, though a few common phrasings like 'model' or 'training run' are missing.	4 / 5
Distinctiveness Conflict Risk	The 'implementable ML training recipes' niche is mostly distinct with clear triggers, but carries minor overlap risk with general ML research or coding skills.	4 / 5
	Total	17 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: companion-inc/feynman
Path: skills/ml-training-recipe/SKILL.md
Commit: 95c51d9

Reviewed: about 8 hours ago

Table of Contents

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