tooluniverse-immunology

Immunology research workflows: antibody-antigen interactions, T/B cell repertoire, MHC/HLA binding prediction, autoimmune disease genetics, vaccine epitope mapping. Uses IEDB, IMGT, SAbDab, UniProt. Use for adaptive immunity questions, immune response analysis, antibody/TCR/BCR characterization, immunogenicity prediction, and immune-pathway-to-disease mapping.

Quality

82%

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Securityby

Low

Low-risk findings worth noting

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Highly actionable and well-structured tool reference with strong gotchas and workflows, but it is verbose in places (teaching immunology basics), lacks validation checkpoints in workflows, and keeps a large reference section inline rather than in a separate file.

Suggestions

Move the Tool Reference tables and pathway-ID catalogue into a references/ file and signal it one level deep from SKILL.md, keeping the body as an overview.

Trim the reasoning-framework paragraphs to decision rules (which arm/database to use) without re-explaining innate/adaptive biology or complement biochemistry Claude already knows.

Add explicit validation checkpoints to the Workflows (e.g., confirm IEDB/UniProt hits before enrichment, verify GWAS p < 5e-8 per the evidence-grading rule) so each chain has a verify-then-proceed step.

Dimension	Reasoning	Score
Conciseness	Mostly efficient tool guidance, but reasoning-framework paragraphs re-explain immunology Claude already knows (innate vs adaptive phases, complement pathway biochemistry, JAK-STAT cascades), so it is not fully lean.	2 / 3
Actionability	Concrete tool names with exact parameters, pathway IDs, a parameter-gotchas table, and sequenced workflows provide copy-paste-ready, executable guidance.	3 / 3
Workflow Clarity	Workflows list clear tool sequences, but there are no explicit validation checkpoints or feedback loops; the reasoning frameworks are principles rather than verify-then-proceed steps.	2 / 3
Progressive Disclosure	A single ~150-line file with no bundle files; the large Tool Reference tables and pathway-ID lists are inlined where a one-level-deep reference file would improve navigation.	2 / 3
	Total	9 / 12 Passed

Description

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.

A strong, third-person description that states concrete capabilities, lists the authoritative databases used, and gives explicit 'Use for ...' trigger guidance. It cleanly covers what the skill does and when to invoke it.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'antibody-antigen interactions, T/B cell repertoire, MHC/HLA binding prediction, autoimmune disease genetics, vaccine epitope mapping' — rather than vague language, matching the top anchor.	3 / 3
Completeness	Explicitly answers 'what' (the listed workflows) and 'when' via a clear 'Use for ...' clause, the top anchor for completeness.	3 / 3
Trigger Term Quality	Natural trigger terms a user would say ('adaptive immunity questions', 'antibody/TCR/BCR characterization', 'immunogenicity prediction', 'immune-pathway-to-disease mapping') give good coverage; not generic jargon.	3 / 3
Distinctiveness Conflict Risk	The immunology niche (IEDB/IMGT/SAbDab/UniProt, adaptive immunity) is specific and unlikely to trigger for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: mims-harvard/ToolUniverse
Path: plugin/skills/tooluniverse-immunology/SKILL.md
Commit: 089eb8e

Reviewed: about 9 hours ago

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