CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

fair-esm2

Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

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.

A tight, code-first skill that assumes competence and delivers executable snippets for its core tasks. The main weaknesses are the fragmentary mutation-scoring example and a batch remote-compute workflow lacking any validation or verification step.

Suggestions

Add an executable mutation-scoring snippet in the Masked-LM section (e.g., mask the WT position, extract logits[mut] − logits[wt]) instead of describing it in a comment.

Add a verification checkpoint to the remote-compute workflow: after compute_done, check job status/errors and validate the returned embeddings (shape, non-NaN) before save_artifacts.

Consider moving the remote-compute details into a reference file (e.g., references/remote-compute.md) to keep SKILL.md as a lean overview.

DimensionReasoningScore

Conciseness

Lean and operational throughout — terse code with inline shape comments, tables for prerequisites and models, no explanation of concepts Claude already knows; every section (disambiguation, troubleshooting, output format) earns its tokens.

5 / 5

Actionability

Embedding and contact-prediction blocks are copy-paste ready, but the masked-LM mutation-scoring case is described rather than shown ('mask the position and compare logit[mut] − logit[wt]') and the job submission uses an `environment=...` placeholder — minor gaps versus the fully-executable 5-anchor.

4 / 5

Workflow Clarity

The remote-compute path is clearly sequenced (compute_details → create → submit → wait_for_notification → save_artifacts), but it is a batch operation (200 sequences) with no output validation or error-recovery checkpoint, which per the rubric caps workflow clarity at 3.

3 / 5

Progressive Disclosure

No bundle files exist and the body is well-organized into clear sections with only one-level-deep pointers to related skills, but at ~118 lines with all content inline — including a ~35-line remote-compute section that is arguably reference-file material — it matches the 4-anchor's 'minor organization gaps' rather than a fully split structure.

4 / 5

Total

16

/

20

Passed

Description

92%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 capabilities, an explicit 'Use when' clause with three triggers, and a distinct niche anchored to a specific package. Only gap is modest synonym/extension coverage in trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Extracting per-residue or per-sequence embeddings', 'Masked-LM likelihood / mutation effect scoring', 'Contact prediction' — covering all of the skill's advertised capabilities comprehensively.

5 / 5

Completeness

Explicitly answers both 'what' ('Embed proteins with Meta AI's ESM-2 (fair-esm package)') and 'when' ('Use this skill when: (1)... (2)... (3)') with three concrete trigger clauses.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('embed proteins', 'mutation effect', 'contact prediction', 'fair-esm'), but missing common synonyms like 'protein language model' and file extensions like .fasta that the 5-anchor requires.

4 / 5

Distinctiveness Conflict Risk

Names a specific model (ESM-2) and package (fair-esm), creating a clear niche; the body further disambiguates against the Biohub esm fork, minimizing wrong-skill triggering.

5 / 5

Total

19

/

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/fair-esm2/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.