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comorbidity-common-immune-biomarker-research-planner

Generates complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarker selection, immune/regulatory interpretation, multi-layer 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.

69

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

77%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 excellent workflow sequencing, validation checkpoints, and clean progressive disclosure via verified reference files. Its main weakness is conciseness: the Hard Rules section duplicates guidance already present in the stepwise execution body.

Suggestions

Collapse the 'Hard Rules' section into a short non-negotiables list, removing rules that merely restate instructions already given inline in Steps 4.5, 5, and 7 (e.g., rules 11–13 on reference verification, 16/18/20 on dependency mapping, 22 on the dataset disclaimer) to cut duplication.

Move the per-config detail tables and the endpoint-formula examples into the existing reference files, keeping SKILL.md as a lean overview that points to them, which would tighten conciseness without losing actionability.

Consolidate the repeated 'mandatory section' assertions (rules 15, 20, 21, 23 and the Step 7 section list) into a single canonical checklist to reduce token cost.

DimensionReasoningScore

Conciseness

The body is mostly efficient but the 23-item 'Hard Rules' section substantially re-states constraints already embedded in the execution steps (dependency consistency, reference verification, disclaimer, minimal-subset rules), adding noticeable padding rather than earning every token.

3 / 5

Actionability

Provides concrete, executable guidance — config comparison tables, exact reference categories I1–I4, explicit dependency formulas, mandatory section lists, and the DESeq2-vs-limma data-type rule — with only minor gaps because bulk detail is delegated to reference files.

4 / 5

Workflow Clarity

A clearly sequenced 7-step process ('always run in order') with explicit validation checkpoints — Step 5 dependency consistency check 'mandatory before output', Step 4.5 literature verification, redirect-and-stop out-of-scope handling, and the mandatory self-critical risk review — giving strong feedback-loop structure.

5 / 5

Progressive Disclosure

Clear overview with well-signaled, one-level-deep references to references/*.md, all of which exist as real bundle files; content is appropriately split across SKILL.md and the reference modules for easy navigation.

5 / 5

Total

17

/

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.

A strong, specific description that clearly states both what the skill produces and when to invoke it, using third person and concrete domain triggers. The only minor gap is slightly fuller synonym coverage in trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and outputs — 'Generates complete ... research designs', 'outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' ('Generates complete ... research designs', 'Covers five study patterns', 'always outputs Lite / Standard / Advanced / Publication+') and 'when' ('Use when a study links two clinically related diseases through ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes strong domain-natural terms a bioinformatics researcher would actually say ('disease pair', 'shared DEGs', 'enrichment', 'PPI hub genes', 'machine-learning feature selection', 'immune infiltration'), but could be richer in synonyms/common variations; sits noticeably above the midpoint but not comprehensive.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear, narrow niche (comorbidity-oriented shared-biomarker bioinformatics design) with distinct triggers and minimal realistic overlap with other skills.

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.

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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