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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.

Quality

87%

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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, highly actionable instruction skill with excellent workflow sequencing, validation checkpoints, and clean one-level progressive disclosure into eight real reference files. The main weakness is redundancy: the Hard Rules section substantially duplicates the step sections, inflating the token budget without adding new guidance.

Suggestions

Consolidate the 23 Hard Rules: keep only rules not already stated in Steps 1–7 (e.g., DESeq2 vs limma choice, redirect scope) and convert the rest into one-line cross-references to the step sections, cutting the body by roughly a quarter.

Add one compact worked example (a filled-in Standard-plan outline for a single trigger like the diabetic-nephropathy example) so the A–J output contract is unambiguous on first use.

Merge the duplicated dependency-consistency text (Step 5 checklist vs. Hard Rules 16/18/19 and the C.5 section description) into a single canonical checklist with pointers.

DimensionReasoningScore

Conciseness

The body mostly avoids explaining concepts Claude already knows and stays instructional, but it is noticeably repetitive and could be tightened: the 23 Hard Rules restate step-section content almost verbatim (rules 15/17/20 restate Section G and C.5 mandates; rules 11–13 restate Step 4.5's citation rules; rule 22 restates Section D's disclaimer instruction; rules 16/18/19 restate Step 5's dependency checks). This matches 'mostly efficient but includes some unnecessary explanation or could be tightened'. Not a 2: there is no domain-knowledge padding (no library tutorials, no PDF-style concept explanations) — the excess is duplicated rules, not filler. Not a 4: the duplication is substantial and systematic, not a minor instance.

3 / 5

Actionability

For an instruction-only skill the guidance is highly concrete: an exact input format ('[disease / condition] + [process-related gene family / pathway / biological theme] + [validation direction]'), worked example inputs, a copy-paste redirect message template, the exact Dataset Disclaimer line to emit, explicit dependency formulas, and a fully specified A–J output section structure. It is not a 5 because there is no worked example of a complete (even skeletal) output for one concrete case, and a few directives remain abstract ('Explain the logic of each decision', 'Explain why this is the best match') with the actionable 8-field template deferred to a reference file. It is clearly above 3: the guidance goes far beyond pseudocode-level hints.

4 / 5

Workflow Clarity

The 'Execution — 7 Steps (always run in order)' gives a clear mandatory sequence with an explicit validation checkpoint (Step 5 'Dependency Consistency Check (mandatory before output)') and a feedback loop ('If any dependency inconsistency is found, revise the plan before outputting'). Additional checkpoints and recovery paths are present: STOP-and-redirect on out-of-scope, state-assumptions fallback for insufficient input, and the literature fallback ('If browsing/search is unavailable... output a search strategy + target evidence map instead of fake references'). This matches the top anchor: clear sequence, explicit validation, error-recovery loops.

5 / 5

Progressive Disclosure

SKILL.md functions as an overview with eight clearly signaled, one-level-deep references ('→ Detailed pattern logic: [references/study-patterns.md]', etc.), all of which exist in references/ and contain real, distinct content (verified against the file listing and workload-configurations.md). Detail is appropriately split out (config tables, method library, step template, figure plan, evidence hierarchy) with a concise decision skeleton left inline. Not below 4: every reference is explicit, shallow, and purpose-labeled, and no reference chain nests further.

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 description: third-person voice, explicit 'Use when' trigger clause, comprehensive and concrete capability list, and a clearly bounded niche. The only weakness is trigger phrasing that stays inside dense domain jargon ('PPI hub-gene prioritization', 'ROC-based biomarker evaluation') rather than also covering the plainer synonyms users might type.

Suggestions

Add a few plain-language trigger synonyms (e.g., 'differentially expressed genes', 'gene expression datasets', 'research plan' / 'study design') alongside the jargon terms so users typing less formal phrasing still match.

Consider naming the input ingredients earlier and more briefly (disease + gene family + validation direction) since this triple is the actual user-facing entry point.

DimensionReasoningScore

Specificity

The description enumerates many concrete capabilities — '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' — plus a fully specified output structure ('Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path'). Coverage is comprehensive across the domain. It is written in third person ('Generates'), so no voice penalty applies. Not below 5: no capability area of the skill is left unnamed; not applicable above since 5 is the max.

5 / 5

Completeness

It explicitly answers both questions: 'what' — 'Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction' — and 'when' — 'Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis...'. Both are concrete and explicit, matching the top anchor. Not below 4, because the 'when' clause is explicit and specific, not merely present.

5 / 5

Trigger Term Quality

Good keyword coverage with terms a bioinformatics user would naturally say: 'DEG analysis', 'GSEA', 'enrichment analysis', 'hub-gene', 'immune infiltration', 'biomarker', 'transcriptome'. It is not a 5 because natural synonym variations are missing — e.g., 'differentially expressed genes' spelled out, 'transcriptomics', 'gene expression', 'study design' as a plain phrase — and the trigger phrasing leans toward dense domain jargon rather than the varied wording real users type. It is clearly above 3 ('some relevant keywords but missing common variations') because most core natural terms are present.

4 / 5

Distinctiveness Conflict Risk

The description carves out a very specific niche — 'conventional non-oncology' hub-gene bioinformatics with an explicit exclusion of oncology-style survival/pan-cancer logic implicit in the domain qualifier — with distinct trigger terms (bulk transcriptome, PPI hub-gene, TF/miRNA, immune infiltration). Minimal conflict risk with other skills. Not below 4: the domain qualifier plus method stack makes it hard to mistake for a generic research or oncology skill.

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

Path

awesome-med-research-skills/Protocol Design/conventional-non-oncology-hub-gene-research-planner/SKILL.md

Reviewed

Table of Contents

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