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 library-designgit 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/library-design)<a href="https://agentmods.dev/skills/gptomics/bioskills/library-design"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/library-design/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/library-design"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/library-design.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 | $0.00192 | $0.06819 |
| Opus 5 | $0.00096 | $0.03410 |
| Sonnet 5 | $0.00038 | $0.01364 |
| Haiku 4.5 | $0.00019 | $0.00682 |
Grade A, and why
bio-crispr-screens-library-design 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 7d 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-library-design — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: CRISPOR 5.01+, BioPython 1.83+, pandas 2.2+, numpy 1.26+, Azimuth 2.0+ (Doench 2016), CRISPRon 1.0+ (Xiang 2021), DeepSpCas9 1.0+ (Kim 2019).
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
crispor.py --helpfrom the crisporWebsite clone - Python: Azimuth has no console script; call
azimuth.model_comparison.predict(...)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
sgRNA Library Design
"Design a CRISPR library for my screen" -> Pick a chemistry (Cas9 KO, CRISPRi, CRISPRa, Cas12a, base or prime editor), score candidate guides for on-target activity and off-target liability, position them relative to gene/TSS, add appropriate controls, lay out the oligo for synthesis, and validate the cloned pool.
- Python:
crispor.py(web + CLI) for batch genome-wide guide scoring with CFD+MIT off-target - Python:
azimuth(Microsoft Research) for Rule Set 2 on-target predictions (Brunello-style) - Python:
CRISPRon,DeepSpCas9for modern deep-learning predictors - R:
crisprDesign(Bioconductor) for integrated annotation-aware design
Library Chemistry Decision Tree
| Goal | Chemistry | Canonical library | Guides/gene | TSS / target window |
|---|---|---|---|---|
| Loss-of-function essentiality, fitness | SpCas9 KO | Brunello, TKOv3, Avana | 4 (Brunello), 4 (TKOv3), 6 (Avana) | Constitutive exons, prefer aa 5-65% from N-terminus |
| Knockdown of non-cuttable genes, dosage-sensitive | dCas9-KRAB (CRISPRi) | Dolcetto, Horlbeck v2 | 6 (Dolcetto), 5 (Horlbeck) | Optimum +25 to +75 downstream of FANTOM5 TSS, searched out to -50/+300 (Dolcetto, Sanson 2018); -25 to +500 (Horlbeck v2) |
| Gain-of-function, gene activation | dCas9-VP64 / SAM / SunTag (CRISPRa) | Calabrese, Horlbeck-CRISPRa | 6 (Calabrese), 5 (Horlbeck) | -150 to -75 from TSS (Calabrese); -550 to -25 (Horlbeck v2) |
| Paralog buffering, GI screens | enAsCas12a multiplex | Inzolia, in4mer | 4-guide arrays | Constitutive exons |
| Variant function, SNV scanning | CBE / ABE | Custom tiling library | Tile editing windows | Editing window pos 4-8 from PAM-distal end |
| Precise edit, indel-free | Prime editor | Custom PRIDICT-designed | Tile pegRNAs | Anywhere with NGG PAM within 30 nt of edit |
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.
- 7d ago First seen · 314 lines · 192 tokens per session scan A c690a115d385
bio-crispr-screens-library-design is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 192 tokens to every session and 6,819 once invoked, about $0.0010 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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