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tooluniverse-hla-immunogenomics

HLA gene-family analysis and MHC-peptide binding for transplant compatibility, vaccine epitope coverage, and cancer immunotherapy. Uses IMGT (HLA polymorphism), IEDB (epitope-MHC binding), UniProt (annotation), DGIdb (druggability). Use for HLA typing/imputation review, vaccine HLA coverage, and immunotherapy prediction biomarkers (HLA-LOH, neoantigen presentation).

64

Quality

75%

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-hla-immunogenomics/SKILL.md

The canonical home for this skill is tooluniverse-hla-immunogenomics 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.

A well-structured, actionable workflow guide with clear phase sequencing and concrete tool documentation. Weaker on conciseness (concept re-explanation) and progressive disclosure (monolithic inline content with no reference files).

Suggestions

Trim the 'Reasoning Strategy' paragraph to remove background immunology Claude already knows (e.g. the HLA-B27/ankylosing spondylitis and abacavir examples), keeping only the operational 'LOOK UP DON'T GUESS' directive.

Add at least one executable Python snippet under 'COMPUTE, DON'T DESCRIBE' so the compute guidance is copy-paste ready rather than directive-only.

Extract the per-phase tool schemas and binding-affinity/drug-association tables into reference files under ./references/ and link them from SKILL.md to establish a one-level-deep overview-to-detail structure.

DimensionReasoningScore

Conciseness

Mostly operational, but the 'Reasoning Strategy' section explains immunology concepts Claude already knows (peptide presentation, HLA-B27/ankylosing spondylitis, abacavir hypersensitivity) that could be trimmed.

3 / 5

Actionability

Concrete tool names with input/output schemas, specific binding-affinity thresholds, and numbered workflows; minor gap is the 'COMPUTE, DON'T DESCRIBE' directive giving no executable code example.

4 / 5

Workflow Clarity

Seven phases are clearly sequenced with an ASCII diagram and per-phase Objective/Tools/Workflow; validation is fallback-style rather than explicit checkpoints, but the skill is non-destructive so no cap applies.

4 / 5

Progressive Disclosure

No bundle files exist and the ~230-line body inlines full tool schemas and per-phase detail that could be split into references; sections are well-organized but there is no overview-to-reference layering.

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, specific description that covers what the skill does, which databases it uses, and when to invoke it, with concrete trigger phrases. Slight specificity gap from domain-style verbs rather than discrete concrete actions.

DimensionReasoningScore

Specificity

Lists multiple specific actions across three clinical domains (transplant, vaccine, cancer) plus named data sources, but verbs skew toward domains ('analysis', 'binding') rather than discrete concrete verbs as in the anchor-5 example.

4 / 5

Completeness

Explicitly answers 'what' (analysis + IMGT/IEDB/UniProt/DGIdb) and 'when' via a concrete 'Use for ...' trigger clause naming HLA typing, vaccine HLA coverage, HLA-LOH, and neoantigen presentation.

5 / 5

Trigger Term Quality

Rich natural domain terms with synonyms (HLA/MHC/epitope/peptide/neoantigen) plus tool names a specialist would actually say; a few common variants like 'tissue typing' or 'matching' are absent.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear HLA-immunogenomics niche with distinct triggers and named databases, making conflict with unrelated skills unlikely.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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