variant-annotation
Query and annotate gene variants from ClinVar and dbSNP databases. Trigger when: - User provides a variant identifier (rsID, HGVS notation, genomic coordinates) and asks about clinical significance - User mentions "ClinVar", "dbSNP", "variant annotation", "pathogenicity", "clinical significance" - User wants to know if a mutation is pathogenic, benign, or of uncertain significance - User provides VCF content or variant data requiring interpretation - Input: variant ID (rs12345), HGVS notation (NM_007294.3:c.5096G>A), or genomic coordinates (chr17:43094692:G>A) - Output: clinical significance, ACMG classification, allele frequency, disease associations
Install with Tessl CLI
npx tessl i github:aipoch/medical-research-skills --skill variant-annotation85
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that excels across all dimensions. It provides specific capabilities, comprehensive trigger terms using domain-appropriate vocabulary, explicit 'Trigger when' guidance with multiple scenarios, and a highly distinctive focus on genomic variant annotation that clearly differentiates it from other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Query and annotate gene variants', with detailed input types (rsID, HGVS notation, genomic coordinates) and output types (clinical significance, ACMG classification, allele frequency, disease associations). | 3 / 3 |
Completeness | Clearly answers both what (query and annotate gene variants from ClinVar/dbSNP) and when (explicit 'Trigger when:' section with five detailed trigger conditions covering user inputs, keywords, and use cases). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'ClinVar', 'dbSNP', 'variant annotation', 'pathogenicity', 'clinical significance', 'rsID', 'HGVS notation', 'VCF', 'pathogenic', 'benign', 'uncertain significance', plus concrete examples like 'rs12345'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche in genomic variant annotation with specific database names (ClinVar, dbSNP), domain-specific terminology (ACMG, HGVS, VCF), and concrete identifier formats that are unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides excellent actionable content with executable code examples, comprehensive input/output documentation, and thorough ACMG classification details. However, it suffers from verbosity with unnecessary boilerplate sections (Evaluation Criteria, Lifecycle Status, Security Checklist) and could benefit from moving reference material like ACMG criteria to separate files. The workflow lacks explicit validation and error recovery steps for API operations.
Suggestions
Move ACMG classification criteria tables to a separate ACMG_REFERENCE.md file and link to it, reducing the main skill by ~60 lines
Remove boilerplate sections (Evaluation Criteria, Lifecycle Status, Test Cases, Security Checklist) that don't provide actionable guidance for Claude
Add explicit error handling workflow: validate input format → check API connectivity → handle rate limits → retry logic → validate response
Fill in the empty Parameter descriptions in the table (--variant, --file, --output, --delay are missing descriptions)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains substantial useful content but includes unnecessary sections like 'Evaluation Criteria', 'Lifecycle Status', and 'Test Cases' that add little value. The ACMG criteria tables are comprehensive but could be referenced externally rather than inline. | 2 / 3 |
Actionability | Provides fully executable Python code and CLI commands with clear examples. The input format table, output JSON schema, and batch query examples are copy-paste ready and cover multiple use cases. | 3 / 3 |
Workflow Clarity | While individual operations are clear, there's no explicit workflow for handling errors, validating inputs before API calls, or dealing with rate limits. The batch processing lacks validation checkpoints between queries. | 2 / 3 |
Progressive Disclosure | References to 'references/' directory and 'requirements.txt' exist, but the ACMG criteria (50+ lines) should be in a separate reference file. The skill is monolithic with content that could be better organized across files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Reviewed
Table of Contents
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