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non-tumor-mechanism-guided-diagnostic-ml-research-planner

Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns 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.

60

Quality

71%

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tessl review fix ./awesome-med-research-skills/Protocol Design/non-tumor-mechanism-guided-diagnostic-ml-research-planner/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 planning skill with a clear ordered workflow and validation checkpoint, undermined by notable duplication between the step sections and the Hard Rules list and by three orphaned reference files that the body never links to.

Suggestions

De-duplicate the Hard Rules section: keep the step sections as the source of truth and reduce Hard Rules to only the cross-cutting constraints not already stated inline (e.g., the DESeq2/limma data-type rule and the stop-and-redirect rule).

Link the three orphaned reference files (analysis-modules.md, method-library.md, workflow-step-template.md) from the relevant steps — e.g., Step 6 for analysis modules, Step 4/Step 7-D for the workflow-step template — or remove them from the bundle if unused.

Add an explicit fix-and-retry loop to Step 5 (dependency check → fix declared dependencies → re-check before output) instead of only a one-directional downgrade, to reach the top workflow-clarity anchor.

DimensionReasoningScore

Conciseness

Mostly efficient and domain-specific, but the 23-item 'Hard Rules' section substantially restates content already in the step sections (reference rules repeated across Step 4.5, Step 7-I, and Rules 11–13/21; dependency rules across Step 5 and Rules 16–18/20), so it could be tightened.

3 / 5

Actionability

Highly actionable for an instruction-only skill: exact input format, named pattern/config tables, mandatory A–J output sections with explicit required elements, verbatim redirect and Dataset Disclaimer text, and logic-formula examples; minor gaps because the concrete research content depends on user input.

4 / 5

Workflow Clarity

Seven ordered steps with a mandatory Step 5 dependency-consistency check (an explicit checklist) and a feedback directive ('If dependency fails, remove or downgrade the downstream claim'); the recovery is one-directional downgrade rather than a fix-and-retry loop, keeping it just below the top anchor.

4 / 5

Progressive Disclosure

Five reference files are well-signaled one-level-deep (study-patterns, workload-configurations, literature-retrieval-and-citation, figure-deliverable-plan, validation-evidence-hierarchy), but three bundle files (analysis-modules, method-library, workflow-step-template) are never referenced from the body, leaving them orphaned and unnavigable.

4 / 5

Total

15

/

20

Passed

Description

76%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 covers what the skill produces and when to use it with concrete trigger phrases and a clearly delineated non-oncology diagnostic-ML niche. Minor room to add a few common trigger synonyms.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions with comprehensive coverage: 'Generates complete…research designs', 'outputs Lite / Standard / Advanced / Publication+', 'recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval'.

5 / 5

Completeness

Clearly answers both 'what' (generates complete diagnostic-ML research designs with the listed outputs) and 'when' via an explicit 'Use when a study centers on…' clause with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good keyword coverage with natural domain phrases a researcher would say ('disease context', 'mechanism theme', 'diagnostic model', 'ROC / calibration / DCA', 'transcriptome comparison', 'validation direction'), but a few common synonyms are missing (e.g., biomarker, GEO, DEG).

4 / 5

Distinctiveness Conflict Risk

A clear narrow niche ('conventional non-oncology diagnostic machine-learning research designs', 'disease-vs-control transcriptome comparison') with distinct triggers and minimal overlap risk 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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