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tooluniverse-gene-regulatory-networks

Gene regulatory network analysis — TF-target inference (JASPAR motifs, ChIP-seq), motif scanning, eQTL integration, perturbation evidence (knockout/overexpression). Use for 'which TF regulates gene X', 'which genes does TF Y target', regulatory pathway reconstruction. Distinguishes direct (binding) vs indirect (co-expression) regulatory evidence.

Quality

80%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/tooluniverse/skills/tooluniverse-gene-regulatory-networks/SKILL.md

The canonical home for this skill is tooluniverse-gene-regulatory-networks in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

63%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 highly actionable — exact tool signatures, JSON examples, return shapes, common mistakes, and question-to-workflow patterns — with a coherent phase structure. Its weaknesses are moderately padded with biology Claude already knows, and a monolithic single-file layout that inlines what belongs in reference files.

Suggestions

Split the per-tool parameter/return reference (Phases 1-7) into a references/ file (e.g. tools.md), keeping SKILL.md as a lean overview with the workflow, patterns, and common mistakes.

Delete or compress the histone-mark meanings list and the direct-vs-indirect evidence primer; keep only the skill-specific evidence-grading tiers (T1-T4).

Add one-line JSON call examples for the tools currently lacking them (RegulomeDB, IntAct, BioGRID, STRING_functional_enrichment, OLS) to close the actionability gap.

DimensionReasoningScore

Conciseness

The tool parameter blocks and Common Mistakes are genuinely non-obvious and earn their tokens, but the body also explains things Claude already knows: "H3K27ac -- Active enhancers and promoters", "H3K27me3 -- Polycomb-repressed regions", and an opening paragraph reiterating that direct binding evidence outweighs co-expression. Fits the 'mostly efficient but includes some unnecessary explanation or could be tightened' anchor rather than 4, where over-explanation would be only minor.

3 / 5

Actionability

Concrete tool names, exact parameter lists, copy-paste JSON call examples, and documented return shapes for the core tools (JASPAR, Enrichr, GTEx, STRING), plus a 'COMPUTE, DON'T DESCRIBE' mandate. Not 5 because several tools (RegulomeDB, IntAct, BioGRID, STRING_functional_enrichment, OLS, PubMed) have parameter tables but no call example or return-shape example.

4 / 5

Workflow Clarity

Phases 0-7 give a clear interrogable sequence and five named use patterns map question shapes to tool chains; the 'LOOK UP DON'T GUESS' rule and the Common Mistakes list act as checkpoints. It falls short of 5 because there are no explicit result-validation steps (e.g., verifying a JASPAR matrix ID before use is stated, but no check that returned enrichment terms are meaningful), though for read-only retrieval this is a minor gap, not the score-3 cap scenario.

4 / 5

Progressive Disclosure

Section headers are clean and navigable, but there are no bundle files at all — ~200 lines of per-tool API reference (parameters, returns, and the mistakes list) live inline in SKILL.md where the rubric's ideal is a lean overview pointing one level deep into references/. This is the 'content that should be separate is inline' anchor; it avoids a 2 only because the inline content is well-structured with clear headers rather than a wall of text.

3 / 5

Total

14

/

20

Passed

Description

87%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: concrete, source-attributed capabilities plus an explicit 'Use for' clause with natural trigger phrasings and a well-defined niche. The only weaknesses are minor missing synonym coverage and omission of a couple of capabilities the body actually supports.

Suggestions

Add spelled-out synonyms in the trigger phrasing, e.g. "transcription factor targets" and "transcription factor binding sites", since users may not abbreviate to 'TF'.

Mention the protein-interaction and literature-search capabilities (STRING/IntAct/BioGRID, Europe PMC) or trim them from the body so description and scope stay aligned.

DimensionReasoningScore

Specificity

Quotes several concrete actions — "TF-target inference (JASPAR motifs, ChIP-seq)", "motif scanning", "eQTL integration", "perturbation evidence (knockout/overexpression)" — each naming the underlying data source. It stops short of 5 because actions the skill also supports (protein interaction networks, literature context) are omitted, a minor coverage gap.

4 / 5

Completeness

The what is explicit and concrete ("TF-target inference... motif scanning, eQTL integration, perturbation evidence") and the when is an explicit "Use for" clause with concrete trigger phrases, matching the score-5 anchor exactly. It would need to lose the 'Use for' clause to drop to 4.

5 / 5

Trigger Term Quality

Natural user phrasings are present verbatim: "'which TF regulates gene X'", "'which genes does TF Y target'", "regulatory pathway reconstruction", alongside "gene regulatory network analysis", "ChIP-seq", "eQTL". Not 5 because common variants like "transcription factor binding sites", "gene regulation", or spelled-out "transcription factor" phrasing are missing, and "TF-target inference" leans technical.

4 / 5

Distinctiveness Conflict Risk

A clear niche (gene regulatory networks) with domain-specific triggers — JASPAR, ChIP-seq, eQTL, TF-target — that a user would only say when they want this exact skill; minimal overlap risk with general bioinformatics or protein-interaction skills.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

mims-harvard/ToolUniverse

Path

plugins/tooluniverse/skills/tooluniverse-gene-regulatory-networks/SKILL.md

Reviewed

Table of Contents

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