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chai1

Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.

72

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

93%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.

Excellent content: fully executable guidance, dense with non-obvious operational knowledge (download dir, ESM VRAM behavior, output refusal on non-empty dirs) and a well-organized error table. The only gap is the absence of an explicit validation loop for batched multi-call campaigns.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence: positioning versus Boltz-2/AlphaFold3, licensing, an executable snippet, format spec, and two high-value gotchas with no filler or explanation of concepts Claude already knows.

5 / 5

Actionability

It provides a complete, copy-paste-ready run_inference call, exact FASTA header syntax ('>{entity_type}|name={id}'), the CLI equivalent ('chai-lab fold complex.fasta out/ --use-msa-server'), output filenames, score fields, and a ranking rule with an ipTM threshold — covering the common cases fully.

5 / 5

Workflow Clarity

The sequence (write FASTA, run, rank by aggregate_score with iptm > 0.5 as a soft pass, recognize errors, proceed to filtering) is clear and backed by an error-recovery table, but there is no explicit validate-then-proceed feedback loop for the batched campaigns the description invites, so it falls short of the 5 anchor.

4 / 5

Progressive Disclosure

No bundle files exist and none are needed; the body is compact and cleanly sectioned (running, two gotcha sections, error table, next-step pointer) with no dangling references, satisfying the simple-skill standard for full marks.

5 / 5

Total

19

/

20

Passed

Description

88%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, third-person, and explicitly trigger-scoped with three realistic use cases. Keyword coverage and distinctiveness against closely related structure-prediction skills are the only slight weaknesses.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'predict an antibody-antigen or protein-ligand complex from a single FASTA', 're-fold designed binders', 'drive co-folding from Python for batched campaigns on a GPU' — comprehensively covering the tool's capability surface in third-person voice.

5 / 5

Completeness

It explicitly answers both 'what' ('Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model') and 'when' ('Reach for this skill to...' with three concrete trigger scenarios), matching the top anchor rather than the merely-adequate 'when' of a 4.

5 / 5

Trigger Term Quality

Natural domain phrases like 'antibody-antigen', 'protein-ligand complex', 'FASTA', 'co-folding', and 're-fold designed binders' are present, but common variations users might say ('protein structure prediction', 'docking', 'complex prediction') are missing, so coverage is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

The Chai-1 niche and specific triggers are distinct, but generic phrasing like 'structure prediction for protein... complexes' plus 'AlphaFold-multimer alternative' creates minor overlap risk with sibling co-folding skills (e.g., boltz or alphafold skills); it is mostly distinct rather than a fully clear niche.

4 / 5

Total

18

/

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.

Validation — 13 / 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

Path

configs/microservice/bff-service/configs/agent-skills/claude-science/chai1/SKILL.md

Reviewed

Table of Contents

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