Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AndyZhuang/Opentestnpx agentmods add skills/andyzhuang/opentest/cosmic-databaseWrote 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/andyzhuang/opentest/cosmic-database)<a href="https://agentmods.dev/skills/andyzhuang/opentest/cosmic-database"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/cosmic-database.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 | $0.00039 | $0.02565 |
| Opus 5 | $0.00019 | $0.01282 |
| Sonnet 5 | $0.00008 | $0.00513 |
| Haiku 4.5 | $0.00004 | $0.00257 |
Grade A, and why
cosmic-database 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.
This is a copy
86% identical to cosmic-database — 6 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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
COSMIC Database
Overview
COSMIC (Catalogue of Somatic Mutations in Cancer) is the world's largest and most comprehensive database for exploring somatic mutations in human cancer. Access COSMIC's extensive collection of cancer genomics data, including millions of mutations across thousands of cancer types, curated gene lists, mutational signatures, and clinical annotations programmatically.
When to Use This Skill
This skill should be used when:
- Downloading cancer mutation data from COSMIC
- Accessing the Cancer Gene Census for curated cancer gene lists
- Retrieving mutational signature profiles
- Querying structural variants, copy number alterations, or gene fusions
- Analyzing drug resistance mutations
- Working with cancer cell line genomics data
- Integrating cancer mutation data into bioinformatics pipelines
- Researching specific genes or mutations in cancer contexts
Prerequisites
Account Registration
COSMIC requires authentication for data downloads:
- Academic users: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register
- Commercial users: License required (contact QIAGEN)
Python Requirements
uv pip install requests pandas
Quick Start
1. Basic File Download
Use the scripts/download_cosmic.py script to download COSMIC data files:
from scripts.download_cosmic import download_cosmic_file
# Download mutation data
download_cosmic_file(
email="[email protected]",
password="your_password",
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz",
output_filename="cosmic_mutations.tsv.gz"
)
2. Command-Line Usage
# Download using shorthand data type
python scripts/download_cosmic.py [email protected] --data-type mutations
# Download specific file
python scripts/download_cosmic.py [email protected] \
--filepath GRCh38/cosmic/latest/cancer_gene_census.csv
# Download for specific genome assembly
python scripts/download_cosmic.py [email protected] \
--data-type gene_census --assembly GRCh37 -o cancer_genes.csv
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 · 336 lines · 39 tokens per session scan A dde1be118e32
cosmic-database is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 2,565 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to cosmic-database, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
lamindb
Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.
tiledbvcf
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
defining-cohort-phenotypes
Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build…
benchling-integration
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
chembl-database
Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
nvalchemi-data-storage
How to write, read, compose, and load atomic data using nvalchemi's composable Zarr-backed storage pipeline (Writer, Reader, Dataset, MultiDataset, DataLoader). Use when saving simulation outputs or trajectories to disk, converting structures (e.g. ASE / extxyz) into Zarr stores, assembling datasets for training or…