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 PKU-YuanGroup/OpenAI4S --skill bio-crispr-screens-jacks-analysisgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-crispr-screens-jacks-analysis)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-crispr-screens-jacks-analysis"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-jacks-analysis/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/pku-yuangroup/openai4s/bio-crispr-screens-jacks-analysis"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-jacks-analysis.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 | $0.00201 | $0.04330 |
| Opus 5 | $0.00101 | $0.02165 |
| Sonnet 5 | $0.00040 | $0.00866 |
| Haiku 4.5 | $0.00020 | $0.00433 |
Grade A, and why
bio-crispr-screens-jacks-analysis 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 9d 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.
This is a copy
95% identical to bio-crispr-screens-jacks-analysis — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: JACKS 0.2.0+ (felicityallen/JACKS), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
python run_JACKS.py --help(run_JACKS.py at the JACKS repo root after clone) - Python: from jacks.jacks_io import runJACKS; help(runJACKS)
- GitHub: install via
git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install .
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
JACKS CRISPR Screen Analysis
"Analyze CRISPR screens with guide-level efficacy modeling" -> Jointly model per-sgRNA log-fold-change across one or more screens as the product of gene essentiality and guide efficacy, sharing efficacy across screens with the same library so that low-quality guides are down-weighted automatically.
- CLI:
python run_JACKS.py countfile replicatefile guidemappingfile [options](script at JACKS repo root) - Python:
from jacks.jacks_io import runJACKSfor programmatic use; lower-levelfrom jacks.infer import inferJACKS - Output: per-gene essentiality (
X1), per-sgRNA efficacy (X1), log-likelihood ratio per gene
The JACKS Model (under the hood)
Why this matters for postdoc-level use: JACKS decomposes the observed per-sgRNA log-fold-change as:
LFC[i, c] = gene_effect[g(i), c] * guide_efficacy[i] + noise
where i is sgRNA index, c is screen condition, g(i) is the gene targeted by sgRNA i. Gene effect varies by condition (different cell lines, different treatments) but guide efficacy is intrinsic to the sgRNA sequence and is treated as constant across screens. The model fits both parameters via variational Bayes with hierarchical priors:
guide_efficacy[i] ~ Normal(1, 1)(Gaussian prior, mean 1, variance 1), shared across all sgRNAsgene_effect[g, c] ~ Normal(0, sigma_c^2)per 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.
- 9d ago First seen · 282 lines · 201 tokens per session scan A 488557487f01
bio-crispr-screens-jacks-analysis is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 201 tokens to every session and 4,330 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-crispr-screens-jacks-analysis, differing in 12 lines, and is treated as a copy.
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