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-prime-editing-screensgit 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-prime-editing-screens)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-crispr-screens-prime-editing-screens"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-prime-editing-screens/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-prime-editing-screens"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-prime-editing-screens.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 | $0.00213 | $0.04404 |
| Opus 5 | $0.00106 | $0.02202 |
| Sonnet 5 | $0.00043 | $0.00881 |
| Haiku 4.5 | $0.00021 | $0.00440 |
Grade A, and why
bio-crispr-screens-prime-editing-screens 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-prime-editing-screens — 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: PRIDICT2 v1.0+ (https://github.com/uzh-dqbm-cmi/PRIDICT2), CRISPResso2 2.2.14+, pandas 2.2+, biopython 1.83+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
python pridict2_pegRNA_design.py single --help;python pridict2_pegRNA_design.py batch --help - Web: PRIDICT2 web interface at https://pridict.it/
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Prime-Editing Screen Analysis
"Design or analyze a pooled prime-editor screen" -> Design pegRNAs (spacer + scaffold + PBS + RTT) for intended edits, predict efficiency with PRIDICT2, filter pre-synthesis to efficient candidates, install variants in the screen, quantify intended-edit vs scaffold-incorporation vs indel via CRISPResso2, and aggregate to per-variant fitness scores.
- Python:
PRIDICT2for pegRNA efficiency prediction - Python:
ePRIDICTfor chromatin-context prediction; pair with PRIDICT2 rather than replacing it - CLI:
CRISPResso --prime_editing_pegRNA_*for amplicon-level analysis - Workflow: pegRNA library design -> PRIDICT2 filtering -> screen execution -> CRISPResso2 quantification -> per-variant scoring
Prime Editor Chemistry Comparison
| Editor | Year | Mechanism | Indel rate | Use when |
|---|---|---|---|---|
| PE2 (Anzalone 2019) | 2019 | nCas9-RT fusion + pegRNA | 1-3% | Standard PE; lowest indel rate |
| PE3 | 2019 | PE2 + nick of opposite strand by additional sgRNA | 2-5% | Higher editing efficiency, slightly more indels |
| PE3b | 2019 | PE3 with edit-blocking ssgRNA | 1-3% | When PE3's added nick risks unwanted indels |
| PEmax (Chen 2021) | 2021 | Engineered RT + nCas9 | 1-2% | Higher editing rate per pegRNA |
| PE5max (Chen 2021) | 2021 | PE3 plus MMR inhibition (MLH1dn) on the PEmax architecture | 1% | Highest efficiency at favorable sites |
| PE6 / dual-pegRNA (2023) | 2023 | Engineered compact PE; twin-pegRNA systems | Variable | Specific applications |
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 · 319 lines · 213 tokens per session scan A d170846b9c10
bio-crispr-screens-prime-editing-screens is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 213 tokens to every session and 4,404 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-crispr-screens-prime-editing-screens, differing in 12 lines, and is treated as a copy.
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