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tooluniverse-protein-sae-variant-interpretation

Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations. For a given protein + variant, computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site. Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. 'why is this variant LoF?' Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism.

73

Quality

92%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

88%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-executed workflow skill: executable tool-call examples with exact parameters, an explicit ref-residue validation checkpoint, honest limitations, cost/latency transparency, and a cross-validation pattern that anchors the SAE evidence. The two minor weaknesses are slight redundancy (mismatch-handling explained twice) and a ~240-line single-file body where reference files could offload the long path and interpretation tables.

Suggestions

Deduplicate the ref_aa-mismatch guidance: state the 'return an explicit error, do not silently mutate' rule once (Step 3) and merely reference it from the Quick path instead of re-explaining tool error behavior in both sections.

Consider moving the long-path Step 4-5 raw-feature inspection code and/or the interpretation/cross-validation tables into a references/ file (one level deep, clearly signaled) to keep SKILL.md as a lean overview.

Trim Claude-obvious asides such as 'Python is 0-indexed, position is 1-indexed' — the code examples already make the indexing unambiguous.

DimensionReasoningScore

Conciseness

The body is dense and almost every line carries non-obvious domain knowledge (tool names, exact params, latency/credit costs, license caveats) rather than re-teaching known concepts — e.g. 'pip install \'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52\'' with the note that PyPI lacks SAEConfig. It is not a 5 because of small trims available: the ref_aa-mismatch error behavior is explained twice (Step 3 and the Quick path), and asides like 'Python is 0-indexed, position is 1-indexed' state what Claude already knows; it is well above 3 because padding is minor and localized.

4 / 5

Actionability

Fully executable, copy-paste-ready guidance throughout: concrete tool invocations with exact arguments and expected outputs ('ESM_explain_variant_mechanism(sequence=ref_sequence, position=175, ref_aa="R", alt_aa="H", window=8, top_k_features=5)'), a complete delta-computation function, a batch saturation example ('1 + 19 = 20 Forge calls, not 38'), and a filled-in reporting template. It is not a 4 because there are no gaps in the common path — inputs, prerequisites, error cases, and output format are all specified.

5 / 5

Workflow Clarity

A clearly sequenced 5-step workflow with an explicit validation checkpoint — 'Verify ref_sequence[174] == "R"' and 'If the reference residue does NOT match, return an explicit error — do not silently mutate the wrong position' — plus decision guidance between the recommended composite path and the long path, and a cross-validation table acting as a checklist ('If 3+ layers agree... the SAE feature analysis is the mechanistic explanation layer'). It is not a 4 because checkpoints and error-recovery behavior are explicit rather than implicit; the operations are read-only analysis, so the destructive/batch cap does not apply.

5 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are all absent) and the single SKILL.md is well-sectioned with clear headers ('When to use', 'Required inputs', 'Workflow (5 steps)', 'Interpretation table', 'Honest limitations', 'Cross-validation pattern', 'Reporting format') and correct internal navigation — no nested or dangling references. It is not a 5 because at ~240 lines the simple-skill exception (<50 lines) does not apply, and some inline content (the long-path Step 4-5 code and the interpretation/cross-validation tables) could plausibly live in one-level-deep reference files; it is above 3 because what is inline is cohesive and everything is easy to navigate as-is.

4 / 5

Total

18

/

20

Passed

Description

92%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 description: concrete capabilities, an explicit 'Use when' trigger with a natural example phrase, third-person voice, and explicit boundary-setting against sibling skills. The only minor gap is that a few natural trigger synonyms are missing, which keeps trigger-term coverage just short of comprehensive.

DimensionReasoningScore

Specificity

Multiple concrete actions with comprehensive coverage: 'Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations' and 'computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site' — the enumerated feature categories make the capability set concrete. It is not a 4 because coverage is comprehensive rather than having minor gaps; it is not below 5 because nothing is generic or abstract.

5 / 5

Completeness

Explicitly answers both questions: what — 'computes which interpretable SAE features ... are lost or gained at the mutation site' — and when — 'Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. "why is this variant LoF?"'. The trigger clause is explicit with a concrete user-facing phrase, matching the 5 anchor exactly; it is not a 4 because the 'when' is already fully specific, not merely present.

5 / 5

Trigger Term Quality

Strong natural terms users would say: 'missense variant', 'damaging', 'loss-of-function' plus the synonym 'LoF' ('why is this variant LoF?'), 'AlphaMissense', 'ClinVar', 'pathogenicity scores', 'mechanistic explanation'. It falls just short of the 5 anchor because a few natural variations (e.g. 'protein variant', 'gain-of-function', 'amino acid substitution') are absent; it is clearly above 3 because common phrasings and synonyms for the core triggers are present.

4 / 5

Distinctiveness Conflict Risk

Clear niche (SAE-based mechanistic explanation of missense variants) with explicit de-confliction: 'Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism'. Naming the sibling skills and what they cover makes wrong-skill triggering minimal, matching the 5 anchor.

5 / 5

Total

19

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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