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 skills add K-Dense-AI/drug-discovery-agent-skills --skill admet-predictiongit clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-skillsWrote 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/k-dense-ai/drug-discovery-agent-skills/admet-prediction)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/admet-prediction"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/admet-prediction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/admet-prediction"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/admet-prediction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00140 | $0.01808 |
| Opus 5 | $0.00070 | $0.00904 |
| Sonnet 5 | $0.00028 | $0.00362 |
| Haiku 4.5 | $0.00014 | $0.00181 |
Grade A, and why
admet-prediction 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 12d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADMET Prediction
Potency gets a compound into a programme; ADMET decides whether it survives one. ADMET-AI is a Chemprop-RDKit graph network trained on 41 Therapeutics Data Commons datasets, tops the TDC ADMET leaderboard, and runs thousands of molecules a minute on a CPU. This skill is about reading its output as a developability verdict rather than a wall of numbers.
Tool: ADMET-AI 2.0.1, MIT, pip install admet-ai
(requires Python 3.11+). Weights download on first use. No GPU needed.
Checked against: PyPI 2.0.1, February 2026.
Read references/running-admet-ai.md before your first run, references/endpoints.md to know which endpoints actually stop programmes, and references/interpreting-predictions.md before acting on a number — that one is judgement, not syntax.
The two scripts
| Script | Answers |
|---|---|
admet_batch.py |
How do I feed a library in without wasting the run? |
admet_report.py |
Which of these compounds has a liability worth acting on? |
Rank within a series; do not trust absolute values
This is the thing to get right. A public model has systematic offsets against your assay — different protocol, different lab, different chemistry. Within a congeneric series those offsets are largely shared, so the ordering survives even where the values do not.
Use predictions to decide which twenty of these hundred to make and assay. Do not use them to decide whether this compound will pass. A predicted hERG of 0.7 versus 0.3 within a series is a real signal; 0.7 in absolute terms is not a measurement.
The percentile column is the point
ADMET-AI reports every prediction against the distribution of approved drugs in DrugBank, in
<endpoint>_drugbank_approved_percentile. It is the most useful thing the tool adds over a bare
model and the column most often ignored.
"Predicted clearance 12" is hard to act on. "More extreme than 92% of approved drugs" prompts the right question: drugs exist out here, but not many — what is the argument that this one works?
What ships with it
5 files 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.
- 12d ago First seen · 137 lines · 140 tokens per session scan A 4684c76e2605
admet-prediction is a skill published in the GitHub repository K-Dense-AI/drug-discovery-agent-skills (28 stars, last pushed 5d ago), licensed MIT. It adds 140 tokens to every session and 1,808 once invoked, about $0.0007 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.
Other skills, from other repositories
patsnap-biological-modality
Biological sequence and modality intelligence via Patsnap MCP.
patsnap-chemical-molecular
Patsnap Chemical Molecular MCP for AI agents. Search 160M+ chemical structures, synthetic routes, and bioactivity data via specialized chemistry tools.
patsnap-scientific-translational-evidence
Patsnap Scientific & Translational Evidence MCP for AI agents. Retrieval platform focusing on scientific literature and translational outcomes, covering academic publication queries and translational medicine record tracking.
patsnap-target-disease
Patsnap Target & Disease MCP for AI agents. Target and disease profiling tool, covering target characterization, disease profiling, and epidemiology evidence retrieval.
patsnap-solution-engine
Patsnap TRIZ Concept Solution Engine MCP for AI agents. Generates innovation or product cost-reduction concepts through asynchronous TRIZ and TRIZ/DFMA workflows. Use for engineering problem solving, concept alternatives, cost-reduction analysis, task-progress retrieval, and selected-solution details.
patsnap-clinical-trials
Patsnap Clinical Trials MCP for AI agents. Intelligent clinical trial retrieval system, covering registered trial tracking, trial details and results analysis, and supporting clinical semantic search.