Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/synthetic-sciences/openscience/admet-reasoningnpx skills add synthetic-sciences/openscience --skill admet-reasoninggit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/admet-reasoning)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/admet-reasoning"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/admet-reasoning.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00047 | $0.00611 |
| Opus 5 | $0.00023 | $0.00305 |
| Sonnet 5 | $0.00009 | $0.00122 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
admet-reasoning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADMET Reasoning
Overview
Standard ADMET prediction tools output scores (e.g., "hERG = 0.85") without explaining why. This skill adds mechanistic reasoning — mapping each ADMET liability to its structural cause, the biological mechanism it affects, and a suggested structural fix.
Based on:
- CoTox (Park et al., 2025): Chain-of-thought toxicity reasoning with structural + biological context improved F1 from 0.37 to 0.66
- DrugR (Liu et al., 2026): Explicit liability reasoning before optimization improved scores 18×
When to Use This Skill
- After ADMET prediction: Interpret flagged liabilities with structural explanations
- Lead optimization planning: Understand which structural features to modify and why
- Toxicity reports: Generate interpretable toxicity assessments for medicinal chemistry teams
- Design review: Evaluate whether proposed modifications address the right liabilities
Do NOT use this skill for:
- Raw ADMET score computation (use
admet-predictioninstead) - Molecular optimization (use
molecular-optimizationinstead)
Related Skills
- admet-prediction: Compute ADMET properties (run this first)
- molecular-optimization: Iterative optimization using liability analysis
- rdkit: Core molecular operations
Installation
Required dependencies
pip install rdkit-pypi numpy pandas
Core Workflows
1. Full Liability Report
Generate interpretable ADMET analysis for a molecule:
python scripts/reason_admet.py --smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" --output report.json
2. Batch Liability Analysis
python scripts/reason_admet.py --input compounds.csv --output liability_report.csv
3. Targeted Toxicity Reasoning
Focus on specific endpoints:
python scripts/reason_admet.py --smiles "CCN1CCCC1" --endpoints hERG,DILI,CYP --output tox_report.json
Script Reference
| Script | Purpose | Key Outputs |
|---|---|---|
reason_admet.py |
Full ADMET reasoning with structural explanations | JSON report with liabilities, causes, mechanisms, fixes |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 76 lines · 47 tokens per session scan A be1181043ee5
admet-reasoning is a skill published in the GitHub repository synthetic-sciences/openscience (3,432 stars, last pushed today), licensed Apache-2.0. It adds 47 tokens to every session and 611 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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