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 hit-callinggit 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/hit-calling)<a href="https://agentmods.dev/skills/gptomics/bioskills/hit-calling"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/hit-calling/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/hit-calling"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/hit-calling.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 | $0.00176 | $0.05100 |
| Opus 5 | $0.00088 | $0.02550 |
| Sonnet 5 | $0.00035 | $0.01020 |
| Haiku 4.5 | $0.00018 | $0.00510 |
Grade A, and why
bio-crispr-screens-hit-calling 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-hit-calling — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MAGeCK 0.5.9+, BAGEL2 2.0, drugZ Aug 2019+, JACKS 0.2.0+, Chronos 2.0+ (DepMap), CERES 1.0+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
mageck --version,BAGEL.py version,python drugz.py --help - Python:
pip show crispr_chronos(JACKS installs from GitHub, not PyPI)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Hit Calling Decision Tree
"Identify significant hits in my CRISPR screen" -> Choose the analysis method that matches the experimental design, statistical assumptions, and quality grade of the screen. Reconcile across methods when high-stakes hits must be validated.
The primary hit-calling methods cover non-overlapping niches; the decision is not "which is best" but "which matches the design."
| Design / question | Primary method | Why | Secondary check |
|---|---|---|---|
| Two-condition essentiality, one cell line, no CN concerns | MAGeCK RRA | Robust, fast, gold-standard for ranked analysis | BAGEL2 (Bayes factor on same data) |
| Time course (3+ timepoints) | MAGeCK MLE | RRA cannot model multi-condition | JACKS (efficacy-aware) |
| Multi-cell-line panel (cancer dependency) | Chronos | Models CN bias + screen quality jointly | MAGeCK MLE per line + meta-analysis |
| Drug screen (vehicle vs drug) | drugZ | Bidirectional Z; vehicle-anchored | MAGeCK MLE with dose covariate |
| Multi-screen joint, same library | JACKS | Shared efficacy; enables ~2.5x smaller screens | MAGeCK MLE; results should converge |
| Essentiality classification with reference sets | BAGEL2 | Bayes factor with CEGv2/NEGv1 calibration | MAGeCK RRA |
| Combinatorial / paired guide | MAGeCK MLE with GI scoring | Models interaction term; see [[combinatorial-screens]] | Custom GI scoring |
| Single-cell perturbation (Perturb-seq) | SCEPTRE | NB GLM + permutation; see [[perturb-seq-analysis]] | Mixscape pre-filter |
| Cancer-line copy-number screen | Chronos (preferred) or CERES | Joint CN-bias + gene-effect modeling; see [[copy-number-correction]] | CRISPRcleanR pre-hoc + MAGeCK |
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 · 293 lines · 176 tokens per session scan A c368782f6f61
bio-crispr-screens-hit-calling is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 176 tokens to every session and 5,100 once invoked, about $0.0009 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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