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conventional-non-oncology-hub-gene-research-planner

Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

66

Quality

80%

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SecuritybySnyk

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tessl review fix ./awesome-med-research-skills/Protocol Design/conventional-non-oncology-hub-gene-research-planner/SKILL.md
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, actionable research-planning skill with a clear ordered workflow, a mandatory validation gate, and excellent one-level-deep progressive disclosure into eight real reference files. The main weakness is verbosity from rules and mandatory-section emphasis repeated across the steps, hard-rules list, and output sections.

Suggestions

Consolidate the citation/DOI verification rules into one canonical location (e.g. references/literature-retrieval-and-citation.md) and have Step 4.5, Section I, and Hard Rules 11-13 reference it instead of restating the same constraints three times.

De-duplicate the dataset-disclaimer, minimal-executable-subset, and dependency-formula rules: state each once in the relevant step and have the Hard Rules cross-reference rather than re-spell them.

Trim the repeated 'mandatory in every output / do not omit' emphasis by collecting all mandatory sections into a single checklist near the end, reducing redundant admonitions scattered through Steps 6-7.

DimensionReasoningScore

Conciseness

Content is substantive procedural guidance that does not explain concepts Claude already knows, but it carries notable redundancy: citation/DOI rules appear three times (Step 4.5, Hard Rules 11-13, Section I), the dataset disclaimer twice, and the minimal-subset and dependency-formula rules twice each. Not a 4 because the repetition is clearly tighten-able padding; not a 2 because the core content earns its place rather than being padded fluff.

3 / 5

Actionability

Concrete guidance throughout: five named study patterns, four configs with explicit modules, dependency-selection formulas, and output sections A-J with required contents. Not a 5 because the granular executable method/tool detail is deferred to reference files rather than covered inline for the common cases; not a 3 because the structural guidance is specific and actionable, not pseudocode-level.

4 / 5

Workflow Clarity

Seven steps are explicitly ordered, and Step 5 is a mandatory dependency-consistency gate with a revise-before-output feedback loop, plus a Hard Rules checklist. Not a 5 because the validation is conceptual self-checking rather than a concrete verification step (the top anchor expects an executable validation command); not a 3 because checkpoints and a feedback loop are clearly present.

4 / 5

Progressive Disclosure

The body is an orchestration overview that signals eight one-level-deep reference files (study-patterns, workload-configurations, literature-retrieval-and-citation, workflow-step-template, analysis-modules, method-library, figure-deliverable-plan, validation-evidence-hierarchy), all verified to exist and linked with clear markdown navigation. Content is appropriately split between overview and detail, matching the top anchor.

5 / 5

Total

16

/

20

Passed

Description

88%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, specific description that clearly states both what the skill produces and when to use it, with concrete trigger phrases and a well-scoped non-oncology niche. The main weakness is trigger phrasing that leans on long jargon composites rather than crisp natural variations.

DimensionReasoningScore

Specificity

Lists multiple concrete actions (DEG analysis, process-gene intersection, enrichment, GSEA, PPI hub-gene prioritization, TF/miRNA networks, ROC, immune infiltration) plus four workload configs and named output components, giving comprehensive coverage. Not a 4 because coverage is broad and specific rather than having only minor gaps.

5 / 5

Completeness

Explicitly answers 'what' ('Generates complete... research designs... always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan...') and 'when' with a concrete trigger clause ('Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis...'). Both halves are explicit with concrete triggers, matching the top anchor.

5 / 5

Trigger Term Quality

Good domain keyword coverage ('DEG analysis', 'GSEA', 'PPI hub-gene prioritization', 'ROC-based biomarker evaluation', 'immune infiltration') that target users would say, but the trigger phrases are long composite jargon clauses with few natural shorthand variations. Not a 5 because it lacks the synonym/variation breadth of the top anchor; not a 3 because the terms are genuinely natural for this niche.

4 / 5

Distinctiveness Conflict Risk

'conventional non-oncology' and the hub-gene pipeline carve a clear niche distinct from oncology skills, with specific triggers. Not a 5 because there is residual overlap risk with general bioinformatics research-planning skills; not a 3 because the non-oncology framing is genuinely distinguishing.

4 / 5

Total

18

/

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.

Validation15 / 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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