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 splicing-qcgit 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/splicing-qc)<a href="https://agentmods.dev/skills/gptomics/bioskills/splicing-qc"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/splicing-qc.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 | $0.00165 | $0.06571 |
| Opus 5 | $0.00082 | $0.03285 |
| Sonnet 5 | $0.00033 | $0.01314 |
| Haiku 4.5 | $0.00016 | $0.00657 |
Grade A, and why
bio-splicing-qc scanned grade A with 1 finding 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 8d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run([ Copies of this mod
1 near-identical copy found in the catalogue:
- bio-splicing-qc — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: RSeQC 5.0+, STAR 2.7.11+, samtools 1.19+, pysam 0.22+, regtools 1.0+, maxentpy 0.0.1+, spliceai 1.3+, matplotlib 3.8+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - 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.
Splicing-Specific Quality Control
Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice. Failures in any of these silently bias PSI estimates and inflate novel-junction false positives. The decision sequence is: experimental design -> library prep -> alignment strategy -> annotation -> diagnostic metrics. Each layer's failure mode is distinct.
QC Layer Taxonomy
| Layer | Target | Tool | Fails when |
|---|---|---|---|
| Experimental design | Read length, depth, replicates, library type | Pre-sequencing review | <PE 75nt; n<3 vs n<3; <30M reads/sample |
| Library prep | poly(A) vs rRNA depletion | Pre-sequencing review | poly(A) library used for IR analysis |
| Alignment | STAR 2-pass cohort-style | STAR | 1-pass loses 14% novel junctions; per-sample 2-pass introduces inconsistency |
| Junction discovery | Saturation, novelty | RSeQC junction_saturation, junction_annotation |
Curve still rising = under-sequenced; novel% >40% suggests biology or artifact |
| Strand specificity | Library protocol consistency | RSeQC infer_experiment |
Wrong --libType halves usable junctions |
| Splice site strength | Cryptic vs canonical | MaxEntScan, SpliceAI | Weak splice sites (MaxEnt<5) may indicate cryptic, regulated, or annotation error |
| Junction overhang | Read-junction support quality | pysam CIGAR parsing | Overhang <8nt = high false-positive rate |
| Contamination | rRNA, adapters | fastq_screen | >20% rRNA in "depleted" library = failed depletion |
| Annotation | GENCODE basic vs comprehensive | Annotation choice | Basic for canonical events; comprehensive for DTU |
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.
- 8d ago First seen · 481 lines · 165 tokens per session scan A 7762f998e5cb
bio-splicing-qc is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 23d ago), licensed MIT. It adds 165 tokens to every session and 6,571 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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