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alphafold2

Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.

78

Quality

100%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

100%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.

The body is lean and highly actionable: one executable command, exact I/O conventions, two sharp operational gotchas, and a feedback-loop error table. It respects Claude's competence and adds only irreducible operational knowledge.

DimensionReasoningScore

Conciseness

Assumes Claude's knowledge of AF2/JAX/gVisor/pLDDT/MSA and devotes every line to non-obvious operational knowledge (the gVisor unified-memory loop, the shared MSA bottleneck), with no concept padding; well below the over-explanation of a 4.

5 / 5

Actionability

Provides a copy-paste-ready `colabfold_batch` command with exact flags, precise input/output naming conventions, concrete env-var overrides, and an error table with specific remediation actions.

5 / 5

Workflow Clarity

Sequences the caching workflow (`--msa-only` → keep `.a3m` → re-feed directory), gives validation thresholds (ipTM > 0.5), and includes an error-recovery feedback table; the batch-operation cap does not apply because validation is present.

5 / 5

Progressive Disclosure

No bundle files exist and none are needed; the single file is well-organized with clear section headers, a diagnostic table, and code blocks, giving easy navigation without nested references.

5 / 5

Total

20

/

20

Passed

Description

100%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.

The description is concise, concrete, and uses third-person voice throughout, naming multiple specific actions and an explicit "Reach for this skill to…" trigger clause. It is highly specific and distinctive within the biomodels category.

DimensionReasoningScore

Specificity

Lists multiple concrete actions—"fold a sequence or complex", "validate designed sequences by self-consistency pLDDT, ipTM, and RMSD", "run a quick MSA-backed prediction using the public MMseqs2 server"—giving comprehensive coverage rather than the minor gaps of a 4.

5 / 5

Completeness

Explicitly answers both what ("Predict protein structure… via the ColabFold runner") and when ("Reach for this skill to fold…, to validate…, or to run…"), with concrete trigger phrases.

5 / 5

Trigger Term Quality

Covers natural terms a structural-biology user would say—"predict protein structure", "fold a sequence", "complex", "monomers and multimers", "pLDDT/ipTM/RMSD", "MSA", "MMseqs2"—with synonyms (fold/predict, monomer/multimer/complex).

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (AlphaFold2/ColabFold monomer and multimer folding) and even names sibling routers (boltz, chai1, openfold3), minimizing wrong-skill triggers.

5 / 5

Total

20

/

20

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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