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bulk-omics-integrative-planner

Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

Quality

85%

Does it follow best practices?

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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-engineered planning skill: explicit step sequence with a mandatory pre-output consistency check, a fully specified output contract, and exemplary one-level-deep reference wiring. Its main cost is token redundancy from rules restated multiple times across sections, plus the lack of a worked example to anchor output quality.

Suggestions

Deduplicate the repeated rule sets: state the self-critical risk review checklist once (Section L), the fabrication ban once (Hard Rules), the Dataset Disclaimer placement once (Section H/Step 6), and the DESeq2/limma default once (Hard Rules), then cross-reference by name from other sections.

Add a short worked example (one abbreviated filled-out section, e.g. a sample 'B. Best-Fit Study Pattern' and one row of the Section C table) so the expected output shape and depth are anchored, improving actionability.

Collapse the three overlapping 'should not' lists (Core Function, Hard Rules, What This Skill Should Not Do) into a single canonical list to reduce token cost without losing coverage.

DimensionReasoningScore

Conciseness

The body is operational throughout — no space is spent explaining concepts Claude already knows — but it is noticeably padded by repetition: the self-critical risk review checklist appears three times (Step 7, Section L, Hard Rule 13), the Dataset Disclaimer requirement appears in at least five places, the fabrication ban in four, and the DESeq2/limma rule twice. It sits between 'mostly efficient, could be tightened' and 'several unnecessary padded sections'.

3 / 5

Actionability

For an instruction-only skill the guidance is highly concrete: a fixed 7-step execution order, an exact out-of-scope redirect quote template, fully specified output sections A–L with per-section content requirements, and specific method defaults (DESeq2 for count data, limma for non-count normalized). The minor gap is the absence of a worked example or sample filled-out section showing what good output looks like.

4 / 5

Workflow Clarity

The 7 steps are strictly ordered with an explicit validation checkpoint ('Step 5 — Dependency Consistency Check (mandatory before output)'), a decision gate for out-of-scope input with a ready-made redirect, a conditional Step 4.5, and a terminal self-critical risk review with fallback planning. The A–L mandatory output structure functions as a completion checklist.

5 / 5

Progressive Disclosure

The SKILL.md is a clear orchestration overview, and each of the 9 reference files (all verified to exist) is explicitly mapped to the output section where it must be used ('references/study-patterns.md → Section B', etc.). References are one level deep, well signaled, and the body warns that output generated without its reference module is incomplete — matching the top anchor.

5 / 5

Total

17

/

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 description: it states comprehensive concrete capabilities in third person, gives an explicit 'use when' clause with named project types, and even encodes hard behavioral constraints (no fabrication, mandatory disclaimer). Its only weaknesses are a few missing natural trigger synonyms and residual overlap risk with adjacent bioinformatics skills.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'define the research question', 'choose the best-fit study pattern', 'recommend example datasets', 'specify the core analysis modules and method choices', 'propose a validation ladder', 'output four workload configurations (Lite / Standard / Advanced / Publication+)' — giving comprehensive, third-person coverage of what the skill does.

5 / 5

Completeness

It explicitly answers both 'what' (the enumerated plan-generation actions) and 'when' ('Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused... projects') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Good keyword coverage including 'bulk transcriptomics, proteomics, metabolomics', 'multi-omics', 'design, scope, or structure', and project types (disease-focused, biomarker-focused, stratification-oriented). A few natural terms users would say are missing, e.g. 'RNA-seq', 'study design', 'omics workflow', so it falls just short of the comprehensive-synonym anchor.

4 / 5

Distinctiveness Conflict Risk

The bulk-omics study-planning niche is well demarcated by modality names and the 'bulk' qualifier, with clear triggers. Minor overlap risk remains with closely related bioinformatics or single-cell planner skills that share 'omics' and 'study design' trigger language.

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.

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/bulk-omics-integrative-planner/SKILL.md

Reviewed

Table of Contents

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