RepurposeDrugs-query

RepurposeDrugs-query is a skill for Claude Code, Codex from QSong-github/DrugClaw. It costs 67 tokens per session (389 once invoked), scanned A, original, no licence file.

A lookup tool for RepurposeDrugs, a database of single-drug studies on using medicines for different diseases.

In plain words
What is it for?
Use it to search repurposing associations, check clinical-trial phases, or look up drugs, diseases, and clinical-trial IDs.
Why use it?
It makes drug–disease links and the clinical-trial progress of repurposed medicines easier to find.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to search repurposing associations, check clinical-trial phases, or look up drugs, diseases, and clinical-trial IDs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/qsong-github/drugclaw/repurposedrugs
Install

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.

Any agent
npx skills add QSong-github/DrugClaw --skill repurposedrugs
Clone the repo
git clone --depth 1 https://github.com/QSong-github/DrugClaw

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for RepurposeDrugs-query

README.md
[![agentmods](https://agentmods.dev/badge/skills/qsong-github/drugclaw/repurposedrugs.svg)](https://agentmods.dev/skills/qsong-github/drugclaw/repurposedrugs)
Your own site
<a href="https://agentmods.dev/skills/qsong-github/drugclaw/repurposedrugs"><img src="https://agentmods.dev/badge/skills/qsong-github/drugclaw/repurposedrugs.svg" alt="Measured on agentmods" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 389 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.00389
Opus 5 $0.00034 $0.00195
Sonnet 5 $0.00013 $0.00078
Haiku 4.5 $0.00007 $0.00039

Measured 8d ago against content hash a163054dfb8a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

RepurposeDrugs-query 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.

The scan reads SKILL.md. This mod also ships 4 executable files (__init__.py, example.py, repurposedrugs_skill.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/drug_repurposing/repurposedrugs/SKILL.md · 40 lines

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.

Read it on GitHub

Files

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.

Changes

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.

  1. 8d ago First seen · 40 lines · 67 tokens per session scan A a163054dfb8a

Subscribe to this mod's changes

RepurposeDrugs-query is a skill published in the GitHub repository QSong-github/DrugClaw (116 stars, last pushed 13d ago), with no licence file. It adds 67 tokens to every session and 389 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 2026-08-30.

Related

Other skills, from other repositories

rdkit

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom…

synthetic-sciences/openscience · 80 tokens

datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…

synthetic-sciences/openscience · 67 tokens

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…

synthetic-sciences/openscience · 78 tokens

binding-affinity

Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.

synthetic-sciences/openscience · 38 tokens

drug-design

End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.

synthetic-sciences/openscience · 44 tokens

molecular-optimization

Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).

synthetic-sciences/openscience · 34 tokens