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tooluniverse-antibody-engineering

Comprehensive antibody engineering and optimization for therapeutic development. Covers humanization, affinity maturation, developability assessment, and immunogenicity prediction. Use when asked to optimize antibodies, humanize sequences, or engineer therapeutic antibodies from lead to clinical candidate.

63

Quality

75%

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SecuritybySnyk

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tessl review fix ./skills/pharma/tooluniverse-antibody-engineering/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is thorough and domain-rich with a clear phased pipeline, but it is over-long, leans on non-executable pseudocode helpers, lacks inter-phase validation gates, and is monolithic with no external references. Solid but improvable across all content dimensions.

Suggestions

Condense or move the example report tables and fabricated metrics into a reference file, keeping the SKILL.md body focused on the actual workflow and tool calls.

Replace undefined helper-function pseudocode (e.g., extract_framework, predict_tango_score, calculate_kd_change) with executable implementations or explicit tool calls, or clearly justify the pseudocode.

Add explicit validation checkpoints between phases (e.g., 'verify humanization score ≥ threshold before proceeding to structure modeling') to give the batch pipeline feedback loops.

DimensionReasoningScore

Conciseness

The ~1580-line body avoids explaining basic concepts, but is heavily padded with fabricated example report tables, dummy metrics, and worked outputs that could be condensed; 'could be tightened' fits better than 'lean and efficient'.

2 / 3

Actionability

Concrete tool calls and scoring formulas are present, but much code is pseudocode relying on undefined helpers (extract_framework, predict_tango_score, calculate_kd_change) and is not fully executable as written.

2 / 3

Workflow Clarity

Phases 0–8 are clearly sequenced with per-phase outputs and a final checklist, but inter-phase validation checkpoints are missing; per the rubric this caps a batch/iterative workflow at 2.

2 / 3

Progressive Disclosure

No bundle files exist and the entire large body is a monolithic single-file wall of text; content like report templates and scoring rubrics would benefit from being split into reference files but none are provided.

2 / 3

Total

8

/

12

Passed

Description

100%

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 well-crafted description: it states concrete capabilities, provides natural trigger terms, explicitly covers both what and when, and occupies a clearly distinct niche. No significant weaknesses.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'humanization, affinity maturation, developability assessment, and immunogenicity prediction' — rather than vague language.

3 / 3

Completeness

Explicitly answers both what it does and when to use it via a clear 'Use when...' clause with explicit triggers.

3 / 3

Trigger Term Quality

'Use when asked to optimize antibodies, humanize sequences, or engineer therapeutic antibodies' uses natural phrasings a user would actually say.

3 / 3

Distinctiveness Conflict Risk

The therapeutic-antibody-engineering niche with named sub-capabilities is distinct and unlikely to trigger the wrong skill.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (1582 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
wu-yc/LabClaw
Reviewed

Table of Contents

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