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tooluniverse-admet-prediction

Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.

66

Quality

78%

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tessl review fix ./plugins/tooluniverse/skills/tooluniverse-admet-prediction/SKILL.md

The canonical home for this skill is tooluniverse-admet-prediction in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

70%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 highly actionable, well-sequenced profiling workflow with exact tool signatures, explicit fallbacks, and a strong mandatory checklist. Its weaknesses are token efficiency — it re-teaches domain pharmacology Claude already knows — and the absence of any progressive disclosure: encyclopedic endpoint and interpretation material is inlined in an already long single file.

Suggestions

Trim the domain-knowledge explanations (CYP metabolism percentages, hERG/QT prolongation mechanism, named withdrawn-drug examples, 'Why It Matters' table column) down to one-line significance notes or drop them, keeping only the operational thresholds and verdict rules.

Move the Phase 4 endpoint encyclopedics, the interpretation/score tables, and the evidence-tier definitions into a references/ file (e.g., references/endpoints.md), keeping SKILL.md as a lean overview with clearly signaled one-level-deep pointers.

Add a step showing how to obtain the ChEMBL ID used by 'ChEMBL_get_molecule(chembl_id=...)' (e.g., resolve it during Phase 1 identity resolution), since it is currently invoked with an ID the workflow never acquires.

DimensionReasoningScore

Conciseness

The bulk is genuinely operational (tool call signatures, parameter shapes, verdict thresholds), but several sections explain textbook pharmacology Claude already knows: '~75% of drugs are metabolized by CYP enzymes... Inhibiting CYP3A4... causes dangerous drug-drug interactions', 'hERG potassium channel inhibition. Causes QT prolongation and fatal cardiac arrhythmia... (e.g., terfenadine, cisapride)', and the 'Why It Matters' table column restating Lipinski/Veber rationale. Not 2 because most of the content is skill-specific operational detail, not padding.

3 / 5

Actionability

Concrete, near copy-paste-ready guidance throughout: exact tool signatures ('ADMETAI_predict_toxicity(smiles=["<SMILES>"])', 'PubChem_get_compound_properties_by_CID(cid=<CID>)'), install command ('uv pip install tooluniverse[ml]'), explicit input formats (list vs string per tool), and numeric verdict thresholds. Not 5 because Phase 5 calls 'ChEMBL_get_molecule(chembl_id="<CHEMBL_ID>")' without ever explaining how to obtain the ChEMBL ID, and the Phase 1 identity record does not include it.

4 / 5

Workflow Clarity

A clearly sequenced 5-phase workflow (identity resolution -> physicochemical -> ADME -> toxicity -> scorecard) with explicit validation: 'LOOK UP DON'T GUESS: never assume SMILES, CID, or experimental LD50 values', per-phase fallbacks when ADMETAI is unavailable, guidance on expected console noise vs real errors, and a mandatory 10-item completeness checklist before reporting. This matches the level-5 anchor (explicit validation steps, feedback loops, checklist).

5 / 5

Progressive Disclosure

No bundle files exist (no references/, scripts/, or assets/), so everything lives inline in a ~305-line SKILL.md. Sections and headers are well-organized, but reference-grade material is inlined — the Phase 4 endpoint encyclopedics (AMES/DILI/hERG/ClinTox/LD50_Zhu explanations), the 9-row interpretation table, and the evidence-grading definitions are all content that belongs in a separate reference file per the 'some structure; content that should be separate is inline' anchor. Not 2 because the structure present is clear, not impenetrable.

3 / 5

Total

15

/

20

Passed

Description

87%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 that clearly and explicitly states both what the skill does and when to use it, with a specific list of integrated data sources and natural trigger terms. Minor improvements possible: add common trigger synonyms (toxicity prediction, LD50, pharmacokinetics) and name the concrete deliverable (pass/warn/fail scorecard).

DimensionReasoningScore

Specificity

Names the domain ('Comprehensive ADMET profiling for drug candidates') and lists several concrete integrated capabilities ('ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data'). Not 5 because the core action is a generic 'profiling' verb — concrete outputs like the pass/warn/fail scorecard are not mentioned; not 3 because multiple specific actions are named, matching the 'several specific actions; minor gaps' anchor.

4 / 5

Completeness

Explicitly answers both what ('Comprehensive ADMET profiling... Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data') and when ('Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing') with concrete trigger phrases — a direct match to the level-5 anchor. Third-person voice is used throughout.

5 / 5

Trigger Term Quality

'Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries' gives good natural keyword coverage users would actually say. Not 5 because common variations are missing (e.g., 'toxicity prediction', 'LD50', 'safe compound', 'pharmacokinetics' spelled out); not 3 because coverage goes well beyond a couple of generic keywords.

4 / 5

Distinctiveness Conflict Risk

A clear niche (ADMET/drug-likeness profiling) with distinct triggers ('BBB penetration', 'hepatotoxicity prediction', 'ADME/PK profiling') that would not naturally fire for unrelated skills, matching the 'clear niche with distinct triggers; minimal conflict risk' anchor. Only negligible overlap risk with a hypothetical general toxicity-lookup skill.

5 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

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