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single-cell-research-planner

Designs complete single-cell research plans from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a single-cell study — including disease-focused, mechanism-focused, biomarker-focused, translational, perturbation-inspired, or validation-aware projects. It should define the research question, choose the best-fit study pattern, recommend sample grouping logic, suggest reference datasets as examples only, specify the core analysis modules, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, sample metadata, accession numbers, cohort availability, cell-type labels, external validation resources, or literature references. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

67

Quality

84%

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

A well-structured planning skill with excellent progressive disclosure — a full one-level-deep reference bundle, every file real and mapped to a specific output section — and a clear, ordered workflow with a mandatory pre-output consistency gate. Weaknesses are moderate redundancy (hard rules restated in three places, a trigger section duplicating input-validation examples) and the absence of any worked output example or explicit failure-handling for the Step 5 gate.

Suggestions

Consolidate the never-fabricate/never-overstate rules into the Hard Rules section only, and delete the redundant 'Core Function should not' bullets and 'What This Skill Should Not Do' items that restate them — keeping one canonical rule list would remove ~40 lines of repetition.

Drop the 'Sample Triggers' section (or fold it into the frontmatter description); it duplicates the Input Validation examples and trigger guidance belongs in the description, not the body.

Add explicit failure handling for the Step 5 dependency check (e.g., 'If any check fails, revise the affected sections and re-run this checklist before generating output') and a brief worked example of one output section (e.g., a filled-in Section C workload table) to make the output format fully concrete.

DimensionReasoningScore

Conciseness

The body is mostly efficient instruction (no biology pedagogy Claude already knows), but it is noticeably repetitive: the never-fabricate rules appear in "Core Function," "Hard Rules 1–3," and "What This Skill Should Not Do"; the Dataset Disclaimer placement is stated in Step 6, Section H, and Hard Rule 2; and "Sample Triggers" largely reiterates the Input Validation examples and content that belongs in the description. This fits the level-3 anchor (mostly efficient but could be tightened); it does not reach level 4's 'minor instances of over-explanation,' since whole duplicated sections could be consolidated into Hard Rules alone.

3 / 5

Actionability

For an instruction-only skill the guidance is largely executable: a fixed 7-step execution order, a mandated output structure (Sections A–L) with per-section content specs, a copy-paste redirect template for out-of-scope input, and concrete method constraints ("count matrices should map to DESeq2 by default; non-count normalized expression matrices should map to limma by default"). It falls short of level 5 because there is no worked example of any output section (e.g., a filled-in Section C comparison table), leaving the exact output format partly to inference; it is clearly above level 3, which expects pseudocode-level vagueness.

4 / 5

Workflow Clarity

Steps are clearly sequenced ("7 Steps (always run in order)") with an explicit mandatory gate — "Step 5 — Dependency Consistency Check (mandatory before output)" with a six-item checklist — plus out-of-scope redirect-and-stop handling and a final self-critical risk review with a fallback plan. It sits at level 4 rather than 5 because the recovery loop is implicit: Step 5 says the check is mandatory before output but never instructs what to do when a check fails (revise which sections, re-run the check), and no checkpoint exists between generating the workflow (Step 6) and the risk review (Step 7).

4 / 5

Progressive Disclosure

The body is a genuine orchestrating overview: the "Reference Module Integration" section maps each of the nine reference files to the specific output section where it must be used (e.g., "references/study-patterns.md → use when selecting the dominant single-cell study pattern in Section B"), references are one level deep, clearly signaled, and all nine referenced files exist in references/. This matches the level-5 anchor (clear overview with well-signaled one-level-deep references, easy navigation); there is no nesting or buried reference content.

5 / 5

Total

16

/

20

Passed

Description

92%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.

An excellent description: third-person voice, explicit 'use when' triggers, and a comprehensive enumeration of concrete capabilities tied to a well-defined niche. The only weakness is verbosity — the final two sentences ("Never fabricate datasets..." and "Always include the mandatory Dataset Disclaimer...") are policy statements that pad the description without adding trigger value — and the absence of the very common 'scRNA-seq'/'scRNA' synonyms.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete actions — "define the research question, choose the best-fit study pattern, recommend sample grouping logic, suggest reference datasets as examples only, specify the core analysis modules, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+)" — covering the skill's full output surface comprehensively. It matches the level-5 anchor (multiple specific concrete actions, comprehensive coverage); level 4 requires 'minor gaps in coverage,' and none are evident.

5 / 5

Completeness

Both halves are explicit: 'what' is the enumerated capability list (research question, study pattern, sample grouping, datasets, analysis modules, validation ladder, workload configurations), and 'when' is the clause "Always use this skill whenever a user wants to design, scope, or structure a single-cell study." This matches the level-5 anchor (clearly and explicitly answers both what AND when with concrete trigger phrases); level 4 would require the 'when' to be less explicit, which it is not.

5 / 5

Trigger Term Quality

Natural trigger phrases are present — "design, scope, or structure a single-cell study" and "disease-focused, mechanism-focused, biomarker-focused, translational, perturbation-inspired, or validation-aware projects" — giving good keyword coverage. However, common synonyms users actually say — "scRNA-seq", "scRNA", "single-cell RNA-seq", "single-cell sequencing" — are absent, so it falls short of the level-5 anchor (comprehensive coverage of natural terms including synonyms). It is clearly above level 3, which expects missing common variations.

4 / 5

Distinctiveness Conflict Risk

"single-cell research plans" plus design/scoping/structuring triggers define a clear niche (study planning) distinct from single-cell analysis-tooling or generic biomedical literature skills, with minimal conflict risk. This fits the level-5 anchor (clear niche with distinct triggers); it is not level 4, since the planner-vs-analysis boundary is explicit rather than merely 'mostly distinct.'

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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