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 msi-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/msi-detection)<a href="https://agentmods.dev/skills/gptomics/bioskills/msi-detection"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/msi-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/msi-detection"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/msi-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 | $0.00134 | $0.05557 |
| Opus 5 | $0.00067 | $0.02779 |
| Sonnet 5 | $0.00027 | $0.01111 |
| Haiku 4.5 | $0.00013 | $0.00556 |
Grade A, and why
bio-clinical-databases-msi-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 7d 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-clinical-databases-msi-detection — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MSIsensor-pro 1.2+, MSIsensor 0.6+, MANTIS 1.0.5+, samtools 1.19+, mSINGS 5.6+, pandas 2.2+, cyvcf2 0.30+. FDA pembrolizumab MSI-H / dMMR pan-tumor approval is from 2017 (Le 2015 NEJM; KEYNOTE-016/164/158); approval extended to colorectal first-line in 2020.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. MSIsensor-pro replaces MSIsensor for tumor-only assays; MSIsensor-ct is the bTMB-equivalent for ctDNA panels.
MSI Detection; The Companion ICI Biomarker to TMB
'Detect MSI status from this somatic sequencing data' -> Profile microsatellite instability across canonical loci (Bethesda 5 panel + extended NGS-derived sites); classify MSI-H / MSS / MSI-L per Bethesda / FDA / KEYNOTE convention.
- CLI (recommended tumor-only):
msisensor-pro msi -d microsatellites.list -t tumor.bam -o msi_out -b 16 - CLI (paired tumor-normal):
msisensor msi -d microsatellites.list -n normal.bam -t tumor.bam -o msi_out - CLI (ctDNA / blood MSI):
msisensor-ct ... - CLI (older WES standard):
mantis -t tumor.bam -n normal.bam -b targets.bed --threads 8
The Regulatory and Trial Landscape
| Event | Year | Threshold | Notes |
|---|---|---|---|
| Le 2015 NEJM | 2015 | MSI-H + ICI in CRC | The seminal paper: pembrolizumab in MSI-H CRC ORR 40% vs 0% MSS |
| FDA pembrolizumab MSI-H / dMMR pan-tumor | 2017 | MSI-H | First tissue-agnostic FDA approval (KEYNOTE-016/164/158) |
| FDA pembrolizumab first-line MSI-H CRC | 2020 | MSI-H + first-line CRC | KEYNOTE-177 |
| CheckMate 142 | 2017-2018 | MSI-H + nivolumab/ipilimumab | Pan-tumor MSI-H second-line |
| ESMO 2024 | 2024 | MSI-H | Maintained pan-tumor MSI-H biomarker |
| Universal Lynch screening | -- | IHC + MSI on all CRC <= 70 yr | NCCN / ACG / EGAPP guidelines |
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.
- 7d ago First seen · 348 lines · 134 tokens per session scan A 02d48a508812
bio-clinical-databases-msi-detection is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 26d ago), licensed MIT. It adds 134 tokens to every session and 5,557 once invoked, about $0.0007 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 2026-09-03.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…