CtrlK
BlogDocsLog inGet started
Tessl Logo

tooluniverse-data-integration-analysis

Integrate computed statistical results (DEGs, GWAS hits, associations) with biological context from ToolUniverse databases (UniProt, GO, Reactome, ClinVar, OpenTargets). Use for adding gene function/pathway/disease annotations to a result list, building biological narrative around statistical findings, and going beyond p-values to mechanism.

64

Quality

75%

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

Fix and improve this skill with Tessl

tessl review fix ./plugin/skills/tooluniverse-data-integration-analysis/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 methodology skill with a clear five-step workflow and a built-in validation step. Its main weakness is conciseness — it over-explains causal-inference concepts Claude already knows — and the absence of any executable code examples or external reference files.

Suggestions

Tighten the Step 3 Causal Reasoning section: replace the MR and DAG concept explanations with terse procedural prompts (e.g. 'sketch DAG; flag confounders to adjust, mediators to leave, colliders to avoid') rather than teaching the underlying theory.

Add at least one concrete, executable example showing a ToolUniverse tool call followed by the Python integration/grading step, to make the COMPUTE, DON'T DESCRIBE directive immediately actionable.

Add an explicit feedback loop after Step 4 (e.g. 'if cross-validation contradicts the finding, return to Step 2 to re-query evidence before reporting') to raise workflow clarity to a 5.

DimensionReasoningScore

Conciseness

Mostly efficient procedural guidance, but several paragraphs teach concepts Claude already knows — e.g. the Mendelian randomization explanation ('Genetic variants are assigned at conception, so they are not confounded…') and the DAG confounder/mediator/collider definitions — which could be tightened.

3 / 5

Actionability

Provides concrete tool-discovery queries like `find_tools("gene enrichment pathway analysis")`, named databases (KEGG, Reactome, STRING, ClinVar, DGIdb), an evidence-grading table, and a report template; no executable code, but as an instruction-only skill the guidance is actionable with only minor gaps.

4 / 5

Workflow Clarity

Clear five-step sequence (results→integration→causal reasoning→cross-validation→reporting) with Step 4 serving as an explicit falsification/validation gate; lacks an explicit error-recovery feedback loop, so it stops just short of a 5.

4 / 5

Progressive Disclosure

Well-organized into clearly headed sections (When to Use, Steps 1–5) with no nested references and no bundle files present; the detailed causal-reasoning deep-dive could arguably be split out, but the inline structure is sound.

4 / 5

Total

15

/

20

Passed

Description

83%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 answers both what the skill does and when to use it, with concrete domain trigger terms and named databases. The only soft spot is minor overlap with sibling ToolUniverse skills and a slightly abstract closing phrase.

DimensionReasoningScore

Specificity

Lists multiple concrete actions (integrate statistical results with biological context, add gene function/pathway/disease annotations, build biological narrative) and names specific databases (UniProt, GO, Reactome, ClinVar, OpenTargets); the closing phrase 'going beyond p-values to mechanism' leans slightly abstract, keeping it just below a 5.

4 / 5

Completeness

Clearly answers both 'what' (integrate computed statistical results with biological context from ToolUniverse databases) and 'when' via an explicit 'Use for…' clause with concrete trigger phrases, satisfying the explicit-trigger-guidance requirement.

5 / 5

Trigger Term Quality

Strong natural domain terms a bioinformatics user would actually say — 'DEGs', 'GWAS hits', 'associations', 'gene function/pathway/disease annotations', 'p-values'; a few common synonyms (e.g. 'enrichment', 'variant annotation') are absent.

4 / 5

Distinctiveness Conflict Risk

Has a clear niche (statistical-to-biological integration across ToolUniverse knowledge DBs) with distinct triggers, but the skill body itself flags overlap with several sibling ToolUniverse skills, indicating minor conflict risk.

4 / 5

Total

17

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
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.