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 GPTomics/bioSkills --skill drugz-chemogenomicgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/drugz-chemogenomic)<a href="https://agentmods.dev/skills/gptomics/bioskills/drugz-chemogenomic"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/drugz-chemogenomic/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/gptomics/bioskills/drugz-chemogenomic"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/drugz-chemogenomic.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 | $0.00223 | $0.03683 |
| Opus 5 | $0.00112 | $0.01842 |
| Sonnet 5 | $0.00045 | $0.00737 |
| Haiku 4.5 | $0.00022 | $0.00368 |
Grade A, and why
bio-crispr-screens-drugz-chemogenomic 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-crispr-screens-drugz-chemogenomic — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: drugZ Aug-2019+ (hart-lab/drugz; Python 3.6+), MAGeCK 0.5.9+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
python drugz.py --help(the repo has no setup.py, so there is nodrugzconsole script) - GitHub: install via
git clone https://github.com/hart-lab/drugz
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
drugZ Chemogenomic Analysis
"Identify genes that sensitize or confer resistance to my drug in a CRISPR screen" -> Compare drug-treated vs vehicle-treated arms (NOT Day-0 baseline) using bidirectional Z-scores per sgRNA, sum to per-gene normalized Z, and rank genes for sensitizer (synthetic lethal) vs suppressor (resistance) phenotype.
- CLI:
python drugz.py -i counts.txt -o drugz.txt -c Vehicle_r1,Vehicle_r2 -x Drug_r1,Drug_r2 - Python: programmatic via
drugz.drugZ_analysis(args)(takes an argparse Namespace) - Workflow: vehicle-anchored counts -> Z-scoring -> per-gene summation -> direction-specific FDR
Why drugZ for Drug Screens (not MAGeCK)
| Property | drugZ | MAGeCK RRA | MAGeCK MLE |
|---|---|---|---|
| Bidirectional sensitivity | YES (sensitizer + resistance same scale) | Asymmetric (neg/pos separately) | Asymmetric |
| Drug-anchored baseline | YES (drug vs vehicle) | Either (drug vs vehicle or vs Day 0) | Either |
| Sensitivity to small effects | Highest (bidirectional Z; Colic et al. 2019) | Moderate | Moderate |
| Statistical framework | Empirical-Bayes windowed Z-score on guide-level log fold change | NB + alpha-RRA | NB GLM with design matrix |
| Handles guide-level noise | sgRNA-level z aggregation | Rank-based aggregation | Built-in guide-efficacy term (optional) |
| Best for | Drug-modifier / chemogenomic screens | General essentiality / standard 2-condition | Time course / multi-condition |
What ships with it
2 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.
- 8d ago First seen · 255 lines · 223 tokens per session scan A 85b91bb5e39a
bio-crispr-screens-drugz-chemogenomic is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 223 tokens to every session and 3,683 once invoked, about $0.0011 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.
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