Conciseness

The body is dense with non-obvious information ('a multi-entity FASTA in, mmCIF plus pTM/ipTM/pLDDT out', the ~5 GB mid-run download, the 3B-parameter ESM VRAM gotcha) and never explains concepts Claude already knows. The intro's positioning against Boltz-2/AlphaFold3 is decision-relevant, not padding, so it matches the score-5 anchor (every token earns its place) rather than 4's 'minor instances of over-explanation'.

Actionability

The guidance is fully executable: a complete run_inference call with imports and parameters, the exact FASTA header grammar ('>{entity_type}|name={id}'), the shell equivalent ('chai-lab fold complex.fasta out/ --use-msa-server'), concrete output files, and a ranking threshold ('treat iptm > 0.5 as a soft pass'). This matches the score-5 anchor (copy-paste ready, covers common cases); the truncated sequences with '...' are inherently user-specific inputs, not pseudocode.

Workflow Clarity

For a single-task skill the action is unambiguous: write the multi-entity FASTA, call run_inference, rank by aggregate_score, threshold on iptm, and clear output_dir between calls — with post-hoc confidence filtering as the validation checkpoint. The simple-skill exception applies, so it matches the score-5 anchor rather than 4; the score-3 cap for destructive/batch operations does not apply since inference is neither destructive nor an unvalidated batch loop.

Progressive Disclosure

There are no bundle files (references/, scripts/, assets/ do not exist) and none are needed: the body is a compact, well-sectioned single page ('Running it', two gotcha sections, 'Errors worth recognizing') with no content that belongs in a separate file and no nested references. This matches the score-5 anchor for well-organized single-page skills rather than 4, which requires organization gaps.

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' — with comprehensive coverage of the skill's use surface. It clearly matches the score-5 anchor (multiple specific concrete actions, comprehensive) rather than 4, which requires minor gaps in coverage.

Completeness

It explicitly answers both: 'what' ('Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model') and 'when' with concrete trigger phrases ('Reach for this skill to predict an antibody-antigen or protein-ligand complex..., to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python...'). This matches the score-5 anchor exactly; the score-4 anchor would require a less explicit 'when'.

Trigger Term Quality

Good natural-term coverage: 'structure prediction', 'complex', 'FASTA', 'antibody-antigen', 'protein-ligand', 'co-folding', 'GPU'. A few natural terms users might say are missing (e.g. 'protein structure', 'binding', output formats like .cif/.pdb), matching the score-4 anchor rather than the comprehensive synonym coverage of 5.

Distinctiveness Conflict Risk

The niche is clear (Chai-1 co-folding), but the description itself acknowledges overlap with closely related skills — 'as an AlphaFold-multimer alternative' and the same surface as boltz — so a generic 'predict this complex' request could route to a sibling skill. This matches the score-4 anchor (mostly distinct, minor overlap risk with closely related skills) rather than 5's minimal conflict risk.