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 beita6969/ScienceClaw --skill drug-discoverygit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/drug-discovery)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/drug-discovery"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/drug-discovery/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/beita6969/scienceclaw/drug-discovery"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/drug-discovery.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 | $0.00052 | $0.00845 |
| Opus 5 | $0.00026 | $0.00423 |
| Sonnet 5 | $0.00010 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
drug-discovery 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 8d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- Drug target identification, druggability assessment
- Virtual screening, molecular docking, pharmacophore
- ADMET (absorption, distribution, metabolism, excretion, toxicity)
- Lead optimization, SAR (structure-activity relationship)
- Pharmacokinetics (PK), pharmacodynamics (PD), PK/PD modeling
- Drug repurposing, off-label, drug-disease associations
- SMILES, InChI, compound libraries, chemical fingerprints
- IC50, EC50, Ki, dose-response curves
Step-by-Step Methodology
- Target identification and validation - Identify therapeutic target from literature, GWAS hits, or omics data. Assess druggability using Open Targets, DGIdb, or structural pocket analysis. Confirm target-disease association strength.
- Compound sourcing - Search ChEMBL, PubChem, ZINC, or DrugBank for known active compounds. For novel scaffolds, consider de novo design tools (REINVENT, MolGPT).
- Virtual screening - Structure-based: dock compound library against target (AutoDock Vina, Glide). Ligand-based: use pharmacophore models or molecular fingerprint similarity. Filter by drug-likeness (Lipinski Ro5, Veber rules).
- ADMET prediction - Predict absorption (Caco-2 permeability, logP), distribution (plasma protein binding, Vd), metabolism (CYP inhibition/induction), excretion (clearance), and toxicity (hERG, hepatotoxicity, AMES mutagenicity). Use SwissADME, pkCSM, or ADMETlab.
- Lead optimization - Analyze SAR from dose-response data. Identify key pharmacophoric features. Suggest modifications to improve potency, selectivity, or ADMET profile while maintaining drug-likeness.
- PK/PD modeling - Build compartmental PK models. Estimate key parameters: Cmax, Tmax, AUC, half-life, bioavailability. For PD, model dose-response (Emax model, Hill equation).
- Drug repurposing analysis - Query drug-gene interaction databases. Analyze shared pathways between drug targets and disease mechanisms. Check clinical trial databases for existing evidence.
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.
- 8d ago First seen · 55 lines · 52 tokens per session scan A c7da68601444
drug-discovery is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 845 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
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scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.