CtrlK
BlogDocsLog inGet started
Tessl Logo

process-related-diagnostic-biomarker-nomogram-research-planner

Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and 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.

72

Quality

88%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

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.

The body is a well-structured, actionable research-design framework with clear sequencing, an explicit dependency-check validation loop, and excellent progressive disclosure via verified reference files. Its main weakness is redundancy between the Hard Rules and the step/output sections.

Suggestions

Consolidate the 23 'Hard Rules' with the Step 5 dependency check and Step 7 mandatory-section list — many rules (15, 17, 20, 22, 23) restate those requirements, adding tokens without new information.

Move any repeated guidance (e.g., the Dataset Disclaimer mandate, which appears in Step 7-D and rule 22) to a single authoritative location and reference it once.

Tighten the 'Execution — 7 Steps' intro and the Hard Rules list by keeping only rules that add constraints not already implied by the step structure.

DimensionReasoningScore

Conciseness

Mostly directive and free of generic background padding, but the 23-item 'Hard Rules' section substantially restates mandates already specified in Step 5 and the Step 7 output sections (e.g., rules 15, 17, 20, 22, 23 duplicate earlier requirements), which could be consolidated.

3 / 5

Actionability

Provides concrete, specific guidance — a 7-step ordered process, study-pattern and config tables, mandatory A–J output sections, and exact rules (DESeq2 for raw counts, limma for non-count matrices; DOI/PMID/PMCID required for references) — with only minor gaps since most granular detail is delegated to the eight reference files.

4 / 5

Workflow Clarity

Clear ordered 7-step sequence with an explicit validation checkpoint (Step 5 dependency consistency check, an 8-item checklist) and a feedback loop ('If any dependency inconsistency is found, revise the plan before outputting'), plus an out-of-scope redirect and validation-tier separation.

5 / 5

Progressive Disclosure

SKILL.md is a well-organized overview that points to eight clearly-signaled, one-level-deep reference files via markdown links; all referenced paths (study-patterns.md, workload-configurations.md, literature-retrieval-and-citation.md, workflow-step-template.md, analysis-modules.md, method-library.md, figure-deliverable-plan.md, validation-evidence-hierarchy.md) exist as real files.

5 / 5

Total

17

/

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, complete, and well-scoped: it states concrete capabilities, an explicit 'Use when' trigger with natural domain keywords, and a distinctive niche with low conflict risk. Voice and trigger guidance are correct.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and outputs — 'Generates complete ... research designs', four configs (Lite/Standard/Advanced/Publication+), 'recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path' — with comprehensive coverage; third-person voice ('Generates') is used correctly.

5 / 5

Completeness

Explicitly answers both 'what' (generates structured research designs with the listed deliverables) and 'when' ('Use when a study centers on process-related genes ...') with concrete trigger phrases; no ambiguity.

5 / 5

Trigger Term Quality

Comprehensive natural domain terms a researcher would say — 'process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation' — plus synonyms and the explicit 'Use when a study centers on' trigger.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear, narrow niche — process-related diagnostic biomarker nomogram bioinformatics study design — with distinct triggers (DEG/WGCNA, nomogram, immune infiltration) that are unlikely to fire for unrelated skills.

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

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.