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mendelian-randomization-protocol-designer

Generates complete Mendelian randomization study designs from a user-provided exposure and outcome direction. Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study — even if phrased as "help me write a paper on X", "design an MR study for Y", or "I want to test whether A causally affects B using GWAS". Covers core two-sample MR design, optional bidirectional follow-up, optional multivariable MR, IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality, heterogeneity / pleiotropy checks, and explicit claim-boundary control. Always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, validation ladder, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references.

69

Quality

85%

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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.

The content is a well-structured, actionable MR-protocol generation framework with excellent progressive disclosure via explicitly mapped, verified reference modules. Its main weakness is redundancy across the Hard Rules, What-Not-To-Do, and Quality Standard sections that restates earlier guidance, and the absence of an explicit output self-validation step or a worked example deliverable.

Suggestions

Consolidate the redundant rules: fold the 'Hard Rules', 'What This Skill Should Not Do', and 'Quality Standard' sections into the relevant execution steps and Mandatory Output Structure to remove restated guidance and reduce token cost.

Add a final 'Self-validation' step (e.g. Step 9) that checks the generated output against the A–L structure and the reference-module usage gate before delivery, with a fix-and-retry loop for any missing section.

Include one short worked example of a completed output (e.g. a condensed Standard-plan protocol for a sample exposure→outcome pair) so the expected deliverable format is concrete rather than implied.

DimensionReasoningScore

Conciseness

The body is mostly efficient operational specification (it does not tutor Claude on what MR is), but the Hard Rules, 'What This Skill Should Not Do', and 'Quality Standard' sections substantially restate rules already given in the 8 steps and Mandatory Output Structure (e.g. IVW default, bidirectional/MVMR cautions, claim boundaries), which is more than minor padding and could be tightened.

3 / 5

Actionability

Concrete and specific guidance throughout — verbatim out-of-scope redirect text, sample input triggers, a fixed A–L output structure, named methods (IVW, weighted median, MR-Egger, MR-PRESSO, Steiger), and explicit per-section requirements — but it lacks a fully worked example of a completed output, leaving a minor gap for a generation skill.

4 / 5

Workflow Clarity

A clearly sequenced 'Execution — 8 Steps (always run in order)' workflow with explicit input validation and a stop-on-out-of-scope gate, plus the Mandatory Output Structure acting as a checklist; the gap is that there is no explicit final self-validation/retry feedback loop before emitting the deliverable.

4 / 5

Progressive Disclosure

A dedicated 'Reference Module Integration' section explicitly maps each of the eight real one-level-deep reference files to its target output section, the body serves as an orchestration/overview layer while operational rules live in references/, and all referenced paths exist in the bundle, giving clear, well-signaled navigation.

5 / 5

Total

16

/

20

Passed

Description

100%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.

The description is specific, trigger-rich, and complete, clearly answering both what the skill does and when to invoke it with natural user phrasings. It occupies a distinct niche with minimal overlap risk and uses appropriate third-person voice.

DimensionReasoningScore

Specificity

Lists many concrete capabilities ('IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality') plus concrete deliverables (four workload configs, stepwise workflow, validation ladder, figure plan), giving comprehensive coverage rather than just a few actions.

5 / 5

Completeness

Explicitly states both what ('Generates complete Mendelian randomization study designs from a user-provided exposure and outcome direction' plus the coverage list) and when ('Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Enumerates natural phrasings a user would actually say ('help me write a paper on X', 'design an MR study for Y', 'I want to test whether A causally affects B using GWAS') alongside synonyms (MR, Mendelian randomization, GWAS), matching the comprehensive-synonym anchor.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear, specialized niche (genetically-proxied causal inference via GWAS summary statistics) with distinct triggers unlikely to fire for general epidemiology, PRS, or clinical-treatment skills, so conflict risk is minimal.

5 / 5

Total

20

/

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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