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AB tooluniverse-gwas-snp-interpretation

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".

FreedomIntelligence/OpenClaw-Medical-Skills Agent Skills author: FreedomIntelligence 9 files · 4 scripts body ≈ 1 684 tokens Open the sourcegithub.com↗ analyzed 12 h ago

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar).

As a process B 72/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

ProcedureSoftware developmentData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
B
72/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 72/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 65 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1684 tokens
    • low 15 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 490: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 65 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.