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 huifer/drug-discovery-skills --skill admet-predictiongit clone --depth 1 https://github.com/huifer/drug-discovery-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/huifer/drug-discovery-skills/admet-prediction)<a href="https://agentmods.dev/skills/huifer/drug-discovery-skills/admet-prediction"><img src="https://agentmods.dev/badge/skills/huifer/drug-discovery-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/huifer/drug-discovery-skills/admet-prediction"><img src="https://agentmods.dev/badge/skills/huifer/drug-discovery-skills/admet-prediction.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00073 | $0.01494 |
| Opus 5 | $0.00036 | $0.00747 |
| Sonnet 5 | $0.00015 | $0.00299 |
| Haiku 4.5 | $0.00007 | $0.00149 |
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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
3 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 · 200 lines · 73 tokens per session scan A 47ec92f4781b
admet-prediction is a skill published in the GitHub repository huifer/drug-discovery-skills (18 stars, last pushed 8mo ago), with no licence file. It adds 73 tokens to every session and 1,494 once invoked, about $0.0004 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
admet-reasoning
Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
drug-lead-analysis
Analyze drug candidate molecules for drug-likeness, ADMET properties, and safety profiles. Use this skill when: (1) Evaluating a molecule's potential as a drug candidate, (2) Checking drug-likeness scores (QED, Lipinski), (3) Predicting blood-brain barrier penetration, (4) Assessing side effects and ADMET properties…
admet-prediction
Predict comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties for drug candidate molecules using GraphMVP ensemble models. Use this skill when: (1) Predicting blood-brain barrier penetration, (2) Assessing side effect profiles, (3) Estimating Caco-2 permeability, half-life, or LD50…
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…
molecular-optimization
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).
target-based-lead-design
Generate diverse lead compounds for a specific protein target using structure-based drug design with MolCraft. Use this skill when: (1) Designing drug candidates for a known protein target (PDB ID or disease name), (2) Generating structurally diverse molecules with optimized binding affinity, (3) Filtering candidates…