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-alternative-splicing-differential-splicinggit 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-alternative-splicing-differential-splicing)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-alternative-splicing-differential-splicing"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-alternative-splicing-differential-splicing/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-alternative-splicing-differential-splicing"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-alternative-splicing-differential-splicing.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 | $0.00146 | $0.06823 |
| Opus 5 | $0.00073 | $0.03411 |
| Sonnet 5 | $0.00029 | $0.01365 |
| Haiku 4.5 | $0.00015 | $0.00682 |
Grade A, and why
bio-differential-splicing 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 13d 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
94% identical to bio-differential-splicing — 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 — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, Shiba 0.5+, STAR 2.7.11+, regtools 1.0+, pandas 2.2+, R 4.4+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Differential Splicing
Detect splicing changes between conditions. Tool choice is a decision about statistical model, annotation dependence, and calibration regime under the specific experimental design — not a preference. Wrong tool for the design produces uncalibrated FDR or systematic effect-size bias.
Statistical Model Taxonomy
| Tool | Model | Test statistic | Min reps per group | Calibration regime | Fails when |
|---|---|---|---|---|---|
| rMATS-turbo | Binomial counts with hierarchical PSI variance | LRT on |ΔPSI| > cutoff (default 0.0001) |
n>=3 | Well-calibrated at n>=3 with adequate junction reads | Junction read imbalance; very low coverage; uncorrected for confounders |
| leafcutter | Dirichlet-multinomial GLM at cluster level | LRT on group factor | n>=2 (n>=3 preferred) | Strong at n>=3; novel-junction-friendly | Undersampled clusters (DM dispersion unstable); cluster topology arbitrariness |
| MAJIQ deltapsi | Beta-binomial bootstrap -> posterior over PSI per LSV | P(|ΔPSI| > T) threshold (T=0.2) | n>=3 | Replicate-structured n=3 vs n=3 | Cohorts where between-sample variability dominates between-group |
| MAJIQ HET | Same model, heterogeneity-aware | Per-LSV permutation-based test | n>=10 | n>=10 vs n>=10 cohort designs | Tightly-controlled small replicate experiments |
| SUPPA2 (empirical) | Empirical null from between-replicate ΔPSI | ECDF on |ΔPSI| conditioned on TPM | n>=4 | n>=4 vs n>=4 with paired-end deep sequencing | n<=3 vs n<=3 (sparse null collapses) |
| SUPPA2 (classical) | Wilcoxon rank-sum on PSI distributions | Wilcoxon p-value | n>=2 | Small samples; non-parametric backup | Cassette events with tight PSI distributions |
| Shiba (2025) | Beta-binomial with explicit junction-imbalance correction | LRT | n>=2 | n=2-3 vs n=2-3 | Established benchmarks limited (new tool) |
| LeafcutterMD | Dirichlet-multinomial outlier mode | Per-sample p-value | n=1 vs cohort >=20 | Single-patient vs cohort | Too few controls (<20) |
| FRASER 2.0 | Beta-binomial autoencoder on Intron Jaccard Index | Per-sample p-value with delta cutoff | n=1 vs cohort >=20 | n>=20 control cohort, single-patient query | See outlier-splicing-detection for this regime |
What ships with it
3 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.
- 13d ago First seen · 429 lines · 146 tokens per session scan A 60cb9590f3bc
bio-differential-splicing is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 146 tokens to every session and 6,823 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to bio-differential-splicing, differing in 12 lines, and is treated as a copy.
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