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-base-editing-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-base-editing-analysis)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-crispr-screens-base-editing-analysis"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-base-editing-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-base-editing-analysis"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-base-editing-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 | $0.00218 | $0.06152 |
| Opus 5 | $0.00109 | $0.03076 |
| Sonnet 5 | $0.00044 | $0.01230 |
| Haiku 4.5 | $0.00022 | $0.00615 |
Grade A, and why
bio-crispr-screens-base-editing-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
97% identical to bio-crispr-screens-base-editing-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 — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: CRISPResso2 2.2.14+, BE-Hive 1.0+ (BE prediction), pandas 2.2+, biopython 1.83+, numpy 1.26+, scipy 1.12+, scikit-learn 1.4+; Broad be-validation-pipeline notebooks (repo HEAD).
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
CRISPResso --version - Python:
pip show CRISPResso2; BE-Hive is a GitHub clone (maxwshen/be_predict_bystander), not a PyPI package
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Base Editing Screen Analysis
"Analyze my base-editor variant-function screen" -> Quantify per-sgRNA target-base conversion, bystander rate, and indel byproducts from amplicon sequencing; filter on editing efficiency; map each sgRNA to its intended SNV (target + bystander pattern); compute per-variant fitness from the screen log-fold change; reconcile target vs bystander variant attribution; annotate against ClinVar / COSMIC.
- CLI:
CRISPResso --base_editor_outputfor per-amplicon BE quantification - CLI: Broad
be-validation-pipelinefor end-to-end pooled-screen analysis with editing-efficiency filtering - Python:
BE-Hive(Arbab 2020) for editing-efficiency prediction; clone maxwshen/be_predict_bystander and import via sys.path - Web:
BE-Designer(Hwang 2018, RGEN Tools) for variant-encoding sgRNA design
Base Editor Chemistry Selection
| Editor | Reaction | Editing window | Indel byproduct rate | When to use |
|---|---|---|---|---|
| BE3 (Komor 2016) | C->T (also G->A on opposite strand) | Pos 4-8 from PAM-distal end | 5-10% | Original; superseded |
| BE4 / BE4max (Koblan 2018) | C->T | Pos 4-8 | <5% | CBE standard |
| eA3A-BE3 | C->T narrow specificity | Pos 5-7 | <5% | Specifically TC contexts (eA3A prefers TC) |
| ABE7.10 (Gaudelli 2017) | A->G (T->C opposite strand) | Pos 4-7 | <2% | First ABE; slow at non-TA contexts |
| ABE8.20 (Gaudelli 2020) | A->G | Pos 4-8 | <2% | Modern ABE; high activity |
| ABE8e (Richter 2020) | A->G | Pos 4-8 | <2% | Highest editing activity; more processive than ABE7.10 |
| evoCDA-BE | C->T (broader) | Pos 1-9 | 5-10% | Larger editing window; more bystander |
| CGBE1 (Kurt 2021) | C->G | Pos 5-7 | 5-10% | C-to-G transversion; rare use |
| GBE (Zhao 2021) | C->G or C->A | Pos 4-7 | 5-10% | Transversions; less mature |
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 · 365 lines · 218 tokens per session scan A dcffe0aa4a88
bio-crispr-screens-base-editing-analysis is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 218 tokens to every session and 6,152 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-crispr-screens-base-editing-analysis, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
cellxgene-census
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.