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 YuliaNuzhnenko/bioinformatics-agent-skills --skill rdkit-qsar-pharmacophoregit clone --depth 1 https://github.com/YuliaNuzhnenko/bioinformatics-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/yulianuzhnenko/bioinformatics-agent-skills/rdkit-qsar-pharmacophore)<a href="https://agentmods.dev/skills/yulianuzhnenko/bioinformatics-agent-skills/rdkit-qsar-pharmacophore"><img src="https://agentmods.dev/badge/skills/yulianuzhnenko/bioinformatics-agent-skills/rdkit-qsar-pharmacophore/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/yulianuzhnenko/bioinformatics-agent-skills/rdkit-qsar-pharmacophore"><img src="https://agentmods.dev/badge/skills/yulianuzhnenko/bioinformatics-agent-skills/rdkit-qsar-pharmacophore.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 | $0.00046 | $0.00497 |
| Opus 5 | $0.00023 | $0.00249 |
| Sonnet 5 | $0.00009 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
rdkit-qsar-pharmacophore 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.
What it actually says
Agent Skill: RDKit QSAR & Small-Molecule Bioactivity Skill
📌 Description
Computes 2048-bit ECFP4 Morgan fingerprints from SMILES, trains LightGBM regressors for pIC50 prediction, and extracts SHAP feature attributions.
🤖 Agent Execution Protocol
When an AI Agent is tasked with rdkit-qsar-pharmacophore:
- Input Validation: Verify that the required input files or coordinates are supplied.
- Environment Check: Ensure dependencies (
RDKit, LightGBM, SHAP, Scikit-Learn) are installed. - Execution: Run the protocol pipeline snippet below.
- Output Generation: Produce actionable Markdown/JSON summaries with publication figures.
💻 Protocol Code Snippet
from rdkit import Chem
from rdkit.Chem import AllChem
def get_ecfp4(smiles):
mol = Chem.MolFromSmiles(smiles)
return list(AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048)) if mol else None
📥 Input & Output Specifications
Input Contract
- Target Files: Valid input data matching domain formats.
- Parameters: Quality thresholds and cutoffs.
Output Contract
- Results Table: Structured summary dataframe or matrix.
- Visualization: Rendered SVG/PNG figures.
📄 License
Distributed under the MIT License. See LICENSE for details.
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 · 59 lines · 46 tokens per session scan A 5746ff270c42
rdkit-qsar-pharmacophore is a skill published in the GitHub repository YuliaNuzhnenko/bioinformatics-agent-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 497 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 2026-08-31.
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