CD tooluniverse-variant-analysis
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions.
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Shorten the description to 1024 characters.
- 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: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1119 chars, limit 1024
Process rating: all ten parameters 44/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 85Steps. 94 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3767 tokens
- 100Running it twice. No mutating operations
- low 12 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)
- +3Description length 1119: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +4Structure: 30 headings
- +3Step-by-step instructions: 94 items
- +4Has examples (14 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.