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experiment-plan

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

71

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Critical

Do not install without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

The body is a well-structured, highly actionable experiment-planning workflow with clear phasing, decision gates, and concrete output templates. Its main weaknesses are mild redundancy between the phases and the 'Key Rules' section, an unused constant, and inline template content that could be progressively disclosed into separate files.

Suggestions

Consolidate the 'Key Rules' into the phases they belong to (or trim them to only non-duplicative rules) to remove the repetition between Phase 2–3 guidance and Key Rules.

Either wire the declared constants (DEFAULT_SEEDS, MAX_BASELINE_FAMILIES) into the relevant workflow steps or remove the unused ones so every constant earns its place.

Move the large EXPERIMENT_PLAN.md / EXPERIMENT_TRACKER.md output templates into referenced template files under a bundle directory and link to them from SKILL.md, deepening the progressive disclosure beyond the inline skeletons.

DimensionReasoningScore

Conciseness

The body is mostly efficient and avoids explaining concepts Claude already knows, but the 'Key Rules' section restates guidance already embedded in the phases (e.g. 'Defend simplicity explicitly', 'Prefer strong baselines over long baseline lists') and constants like DEFAULT_SEEDS and MAX_BASELINE_FAMILIES are declared but not consistently used in the workflow. It is not a 3 because of this repetition and an unused constant; it is not a 1 because there is no padding or concept-explaining fluff and most sections earn their place.

2 / 3

Actionability

Provides concrete per-block specification fields (dataset/split, metrics, success criterion, failure interpretation, table target) and copy-paste-ready output templates with explicit table schemas for EXPERIMENT_PLAN.md and EXPERIMENT_TRACKER.md plus a user-facing summary. It is not a 2 because the guidance is specific and template-driven rather than pseudocode or abstract; not a 1 because it instructs rather than merely describes.

3 / 3

Workflow Clarity

A clearly sequenced Phase 0–5 flow plus a milestone run-order structure with explicit decision gates, stop/go criteria, risk mitigations, and a final checklist. It is not a 2 because validation/checkpoint sequencing is explicit rather than implicit; the destructive/batch cap does not apply since this is a planning skill.

3 / 3

Progressive Disclosure

The body is well-organized into named sections and references shared protocols via clearly signaled one-level links (output-versioning.md, output-manifest.md, output-language.md), but no bundle files exist and the referenced shared-references paths are external to the skill, while large output-template content is kept inline rather than split into separate referenced files. It is not a 1 because organization is good and references are one level deep, not nested; it is not a 3 because content that could be separate files is inline and the referenced protocol files are not present in the bundle to verify.

2 / 3

Total

10

/

12

Passed

Description

100%

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 strong: concrete actions, natural trigger terms, an explicit 'Use when' clause, and a distinct niche anchored to a research-refine pipeline. It uses third-person voice and avoids vague fluff or over-claims.

DimensionReasoningScore

Specificity

Names multiple concrete actions — 'Turn a refined research proposal...into a detailed, claim-driven experiment roadmap' and lists concrete deliverables (ablation matrix, evaluation protocol, run order, compute budget, paper-ready validation). It is not a 2 because the action set is comprehensive rather than naming only a domain and a few actions.

3 / 3

Completeness

Explicitly answers both what ('Turn a refined research proposal...into a detailed...roadmap') and when ('Use after `research-refine`, or when the user asks for...'). It is not a 2 because the 'when' is an explicit trigger clause, not merely implied.

3 / 3

Trigger Term Quality

Covers natural researcher phrasings — 'detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, paper-ready validation' — plus model-family variants (LLM / VLM / Diffusion / RL). It is not a 2 because common variations are well covered rather than missing.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (paper-oriented, claim-driven experiment roadmaps) and is anchored to a pipeline position via 'Use after `research-refine`', making wrong-skill triggering unlikely. It is not a 2 because the triggers are specific to planning rather than the generic overlap a 2 would risk.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

relative_links

Relative link issues: 3 suspicious

Warning

Total

14

/

16

Passed

Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.