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proteinmpnn

Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while holding interface residues fixed, or to generate a temperature-swept set of sequences for downstream folding.

65

Quality

80%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./configs/microservice/bff-service/configs/agent-skills/claude-science/proteinmpnn/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

76%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 highly actionable and token-efficient, with strong code, gotchas, and error guidance. Its main gap is the absence of an explicit validation checkpoint in the run workflow for a batch operation.

Suggestions

Add an explicit numbered run workflow with a validation checkpoint after generation (e.g., verify out/seqs/ contains the expected number of .fa records matching --num_seq_per_target before downstream folding).

Frame the 'Errors worth recognizing' table as a step in the workflow (run -> check output -> if KeyError/JSONDecodeError, apply fix) so recovery becomes an explicit feedback loop.

Consider moving the checkpoint comparison and/or error table into a references file so SKILL.md stays a lean overview, given the body exceeds 50 lines.

DimensionReasoningScore

Conciseness

Dense and information-rich with no padding of concepts Claude already knows; a few contextual framing sentences (e.g., the CPU-vs-GPU aside) could be tightened further.

4 / 5

Actionability

Fully executable copy-paste bash block with real flags, concrete output paths, named helper scripts, exact checkpoint identifiers, and a diagnostic error table with copy-ready commands covering common cases.

5 / 5

Workflow Clarity

A clear implicit run sequence and an error-recovery table exist, but this is a batch operation with no explicit numbered validation checkpoint in the run flow, so per the batch-operation cap workflow clarity cannot exceed 3.

3 / 5

Progressive Disclosure

Well-organized single-purpose skill with clear section headers and no nested references; the inlined checkpoint and error tables could arguably be split out, but the size makes inlining defensible.

4 / 5

Total

16

/

20

Passed

Description

83%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 strong: it states the concrete capability, gives three explicit use-when scenarios, and cites the tool and source. It is concise and highly actionable with low conflict risk.

DimensionReasoningScore

Specificity

Lists several concrete actions ('run sequence design on RFdiffusion backbones', 'redesign one chain of a PDB while holding interface residues fixed', 'generate a temperature-swept set of sequences'), approaching comprehensive coverage but with some overlap between the listed scenarios.

4 / 5

Completeness

Explicitly answers both what ('Inverse-fold a protein backbone ... into amino-acid sequence with ProteinMPNN') and when ('Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain ... or to generate a temperature-swept set ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes domain-natural phrases users would say ('PDB structure', 'backbone', 'sequence design', 'RFdiffusion backbones', 'temperature-swept'); a few synonyms or the .pdb extension are not explicitly covered.

4 / 5

Distinctiveness Conflict Risk

ProteinMPNN inverse-folding is a clearly distinct niche with specific triggers, but it has minor overlap risk with closely related sister skills (ligandmpnn, solublempnn) on backbone-design tasks.

4 / 5

Total

17

/

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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