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 bagel-essentialitygit 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/bagel-essentiality)<a href="https://agentmods.dev/skills/gptomics/bioskills/bagel-essentiality"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/bagel-essentiality/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/bagel-essentiality"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/bagel-essentiality.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 | $0.00220 | $0.03880 |
| Opus 5 | $0.00110 | $0.01940 |
| Sonnet 5 | $0.00044 | $0.00776 |
| Haiku 4.5 | $0.00022 | $0.00388 |
Grade A, and why
bio-crispr-screens-bagel-essentiality 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-bagel-essentiality — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: BAGEL2 2.0 (hart-lab/bagel, build 115), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
BAGEL.py fc --help;BAGEL.py bf --help;BAGEL.py pr --help - Python: BAGEL2 is distributed via
git clone(no canonical PyPI release); confirmBAGEL.py versionafter checkout.
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
BAGEL2 Essentiality Analysis
"Identify essential genes from my CRISPR fitness screen using BAGEL2" -> Compute per-sgRNA fold changes from counts, derive per-gene log-likelihood ratios against reference essential and non-essential gene sets, sum to Bayes Factor, and apply BF threshold calibrated by precision-recall against the reference.
- CLI:
BAGEL.py fcto compute fold changes - CLI:
BAGEL.py bfto compute Bayes Factors - CLI:
BAGEL.py prfor precision-recall curves - Reference sets: CEGv2 (essentials) and NEGv1 (non-essentials); both at https://github.com/hart-lab/bagel
The BAGEL2 Bayesian Framework (under the hood)
Why this matters for postdoc-level use: BAGEL2 uses a Bayes-factor classifier trained on known essential and non-essential genes. The chain:
- For each sgRNA, compute log-fold-change (LFC) treatment vs control.
- For each gene, look up per-sgRNA LFCs.
- For each sgRNA, compute the log-likelihood ratio:
log( P(LFC | gene is essential) / P(LFC | gene is non-essential) ). The numerator and denominator are KDEs (kernel density estimates) of LFC distributions from CEGv2 and NEGv1 reference sgRNAs. - Sum per-gene log-likelihood ratios across all sgRNAs targeting the gene -> per-gene Bayes Factor.
- Resampling for the confidence interval (default: 10-fold cross-validation;
-bswitches to bootstrapping with-NB, default 1000); BF >6 corresponds to ~90% posterior probability (Hart 2017 G3); ~5% FDR by BAGEL convention.
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 · 254 lines · 220 tokens per session scan A f6ddc99b0bea
bio-crispr-screens-bagel-essentiality is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 26d ago), licensed MIT. It adds 220 tokens to every session and 3,880 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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