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 outlier-splicing-detectiongit 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/outlier-splicing-detection)<a href="https://agentmods.dev/skills/gptomics/bioskills/outlier-splicing-detection"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/outlier-splicing-detection/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/outlier-splicing-detection"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/outlier-splicing-detection.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 | $0.00205 | $0.05348 |
| Opus 5 | $0.00102 | $0.02674 |
| Sonnet 5 | $0.00041 | $0.01070 |
| Haiku 4.5 | $0.00020 | $0.00535 |
Grade A, and why
bio-outlier-splicing-detection 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-outlier-splicing-detection — 94% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: FRASER 2.0 (>=1.99.0), OUTRIDER 1.20+, LeafcutterMD via leafcutter 0.2.9+, DROP 1.4+, R 4.4+, BiocManager 1.30+
Before using code patterns, verify installed versions match. If versions differ:
- 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.
Outlier Splicing Detection
For clinical RNA-seq diagnostics in rare disease, the question is not "what differs between groups?" but "what is aberrant in this single patient relative to a panel of unaffected samples?". The statistical framework is single-sample-vs-cohort outlier detection, fundamentally different from two-group differential splicing. Tools in this space are designed for clinical Mendelian diagnostic settings.
Tool Taxonomy
| Tool | Statistic | Test target | Fails when |
|---|---|---|---|
| FRASER 2.0 | Beta-binomial autoencoder on Intron Jaccard Index | Splicing outliers (per-sample, per-junction) | Cohort <20 samples; tissue mismatch |
| OUTRIDER | Autoencoder-denoised expression Z-score | Gene-level expression outliers (LoF, monoallelic) | Cohort <20 samples |
| LeafcutterMD | Dirichlet-multinomial outlier mode | Annotation-free intron usage | Beta-binomial fits poorly OR few controls |
| DROP | Snakemake pipeline | All of above + monoallelic expression | Pipeline complexity for small projects |
Core reference: FRASER 2.0 for splicing outliers, OUTRIDER for expression outliers, DROP to combine. Standard tool in EU rare-disease programs (Solve-RD) and NIH UDN.
Decision Tree by Diagnostic Scenario
| Scenario | Recommended approach |
|---|---|
| Single rare-disease patient + panel of n>=50 controls | FRASER 2.0 (Intron Jaccard Index) |
| Single patient + small panel (n=20-50) | FRASER 2.0 with auxiliary GTEx controls; tune q carefully |
| Patient + cohort <20 | Insufficient for outlier detection; consider differential or recruit more samples |
| Outlier expression suspected (loss of function, monoallelic) | OUTRIDER on same cohort |
| Annotation-free outlier (cryptic exon, novel junction) | LeafcutterMD |
| Integrated diagnostic pipeline (splicing + expression + MAE) | DROP |
| TDP-43 ALS post-mortem brain (cryptic exons) | FRASER 2.0; expect UNC13A, STMN2, ATG4B |
| SF3B1-mutant cancer sample | FRASER 2.0 with cohort-matched RNA-seq; expect cryptic 3'ss |
| Familial dysautonomia (ELP1) | FRASER 2.0 in fibroblast/iPSC; CNS tissue gives strongest signal |
| Stargardt deep-intronic ABCA4 | FRASER 2.0 in retina-relevant tissue |
| Solid tumor splicing biomarker | Differential splicing (n>=10 vs cohort) — see differential-splicing skill |
| RNA validation of SpliceAI hit | FRASER 2.0 + cross-reference with predicted variant location |
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 · 387 lines · 205 tokens per session scan A 5c88bd6ff38b
bio-outlier-splicing-detection is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 24d ago), licensed MIT. It adds 205 tokens to every session and 5,348 once invoked, about $0.0010 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 2026-08-30.
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