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esmfold2

Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.

66

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

An excellent, dense, actionable body with verified one-level references and strong progressive disclosure. The only gap is workflow clarity: stochastic/batch folding lacks an explicit validate-or-rank checkpoint loop, capping it just below the top anchor.

Suggestions

Add an explicit ranking/selection checkpoint after fold() (e.g. 'verify each Prediction has finite pLDDT/ipTM before ranking; discard NaN samples') to close the validate-then-retry loop for batch diffusion sampling.

For the cusolver SVD poison, frame a recovery loop (detect illegal-memory-access → apply monkeypatch → retry) rather than only presenting the patch as a one-shot fix.

DimensionReasoningScore

Conciseness

The body is lean and dense with actionable, non-obvious detail (version pins, kernel-backend caveats, SVD monkeypatch) and assumes Claude's competence without explaining basic concepts, matching the 'lean and efficient; every token earns its place' anchor.

3 / 3

Actionability

It provides fully executable install commands, copy-paste folding code with exact kwargs, and a concrete SVD monkeypatch — copy-paste ready guidance per the top anchor.

3 / 3

Workflow Clarity

Sequences are present (install → load → set_kernel_backend → fold → rank) but there is no explicit validation/verification checkpoint for the batch/stochastic folding runs, and the destructive-adjacent cusolver poison fix is given as a patch rather than a validate-then-retry loop, so the top anchor's explicit checkpoints are not fully met.

2 / 3

Progressive Disclosure

The SKILL.md is a clear overview with well-signaled one-level-deep references to real bundle files (`references/design-hook.md`, `references/esmc.md`), both verified to exist, with appropriately split detail and easy navigation.

3 / 3

Total

11

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12

Passed

Description

77%

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 highly specific, complete description with explicit enumerated 'Use when' triggers covering both folding and ESMC use cases. It loses a point on trigger-term quality because the wording leans technical rather than mirroring natural user phrasing.

DimensionReasoningScore

Specificity

Lists many concrete actions — all-atom co-folding in single-sequence and MSA modes, masked-LM logits, hidden states, mutation scoring, contact prediction, SAE head — matching the 'multiple specific concrete actions' anchor.

3 / 3

Completeness

It clearly answers 'what does this do' (folding + ESMC capabilities) and 'when to use it' via the explicit 'Use this skill when: (1)...(5)' trigger list, hitting the top anchor.

3 / 3

Trigger Term Quality

It includes domain terms (co-folding, MSA, embeddings, mutation scores) but most are technical jargon rather than the natural phrases a user would say (e.g. 'predict protein structure', 'design a binder'); common user-facing variations are only partially covered.

2 / 3

Distinctiveness Conflict Risk

The Biohub ESMFold2/ESMC niche with specific model names and concrete triggers is clearly distinguishable and unlikely to fire for an unrelated skill.

3 / 3

Total

11

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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