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 agentmods add skills/gptomics/bioskills/single-cell-splicingnpx skills add GPTomics/bioSkills --skill single-cell-splicinggit 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/single-cell-splicing)<a href="https://agentmods.dev/skills/gptomics/bioskills/single-cell-splicing"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/single-cell-splicing.svg" alt="Measured on agentmods" 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 | $0.00230 | $0.07042 |
| Opus 5 | $0.00115 | $0.03521 |
| Sonnet 5 | $0.00046 | $0.01408 |
| Haiku 4.5 | $0.00023 | $0.00704 |
Grade A, and why
bio-single-cell-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 6d 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-single-cell-splicing — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+
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.
Single-Cell Splicing Analysis
The fundamental decision is chemistry, not tool. Most droplet 3' scRNA-seq cannot support transcriptome-wide splicing inference because reverse transcription primes from the poly(A) tail and most reads land in the 3' UTR — far from CDS-region splicing events. Plate-based full-length methods and single-cell long-read sequencing are the chemistries that give per-cell isoform structure across the gene body.
The 10X 3' Problem (Quantified)
Three compounding mechanisms make 10X Chromium 3' (v3.1, GEM-X, v4) hostile to splicing:
- 3' enrichment: median fragment <1 kb from poly(A); >70% of unique reads fall within 3' UTR.
- Short R2 (~91 nt): each read straddles at most one junction; usually none, because R2 lands in 3' UTR.
- PCR concatemers and TSO artifacts: pollute junction detection; UMI collapse is gene-level, not isoform-level.
Quantitative estimate: Only a small fraction of cassette exons sit close enough to the polyA site to be sampled by 3' chemistry (empirical estimates from APA/3'-end atlases — see Tian & Manley 2017 Nat Rev Mol Cell Biol for the 3' UTR isoform landscape). Effective junction read yield from 10X 3' is <0.1 per cell per AS event — vs the 5-10 needed for stable per-cell PSI. Most splicing analyses on 10X 3' data report artifacts.
The 5' kit (10X 5' GEX) does not solve this — it shifts capture from 3' UTR to 5' UTR / TSS-proximal regions. Marginal improvement; not a transcriptome-wide solution. Note that V(D)J recovery requires the 10X Chromium Single Cell Immune Profiling kit (with TCR/BCR-specific enrichment), not 5' GEX alone — postdocs designing immune-repertoire experiments must use the dedicated V(D)J kit.
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.
- 6d ago First seen · 447 lines · 230 tokens per session scan A 5da5fc871d6a
bio-single-cell-splicing is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 21d ago), licensed MIT. It adds 230 tokens to every session and 7,042 once invoked, about $0.0011 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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