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 x-cmd/skill --skill geo-databasegit clone --depth 1 https://github.com/x-cmd/skillWrote 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/x-cmd/skill/geo-database)<a href="https://agentmods.dev/skills/x-cmd/skill/geo-database"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/geo-database/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/x-cmd/skill/geo-database"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/geo-database.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 | $0.00047 | $0.05994 |
| Opus 5.5 | $0.00019 | $0.02398 |
| Sonnet 5.5 | $0.00009 | $0.01199 |
| Haiku 4.5 | $0.00005 | $0.00599 |
Grade A, and why
geo-database 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Using wget or curl for Downloads:** This is a copy
86% identical to geo-database — 10 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 — 815 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Database
Overview
The Gene Expression Omnibus (GEO) is NCBI's public repository for high-throughput gene expression and functional genomics data. GEO contains over 264,000 studies with more than 8 million samples from both array-based and sequence-based experiments.
When to Use This Skill
This skill should be used when searching for gene expression datasets, retrieving experimental data, downloading raw and processed files, querying expression profiles, or integrating GEO data into computational analysis workflows.
Core Capabilities
1. Understanding GEO Data Organization
GEO organizes data hierarchically using different accession types:
Series (GSE): A complete experiment with a set of related samples
- Example: GSE123456
- Contains experimental design, samples, and overall study information
- Largest organizational unit in GEO
- Current count: 264,928+ series
Sample (GSM): A single experimental sample or biological replicate
- Example: GSM987654
- Contains individual sample data, protocols, and metadata
- Linked to platforms and series
- Current count: 8,068,632+ samples
Platform (GPL): The microarray or sequencing platform used
- Example: GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array)
- Describes the technology and probe/feature annotations
- Shared across multiple experiments
- Current count: 27,739+ platforms
DataSet (GDS): Curated collections with consistent formatting
- Example: GDS5678
- Experimentally-comparable samples organized by study design
- Processed for differential analysis
- Subset of GEO data (4,348 curated datasets)
- Ideal for quick comparative analyses
Profiles: Gene-specific expression data linked to sequence features
- Queryable by gene name or annotation
- Cross-references to Entrez Gene
- Enables gene-centric searches across all studies
2. Searching GEO Data
GEO DataSets Search:
Search for studies by keywords, organism, or experimental conditions:
from Bio import Entrez
# Configure Entrez (required)
Entrez.email = "[email protected]"
# Search for datasets
def search_geo_datasets(query, retmax=20):
"""Search GEO DataSets database"""
handle = Entrez.esearch(
db="gds",
term=query,
retmax=retmax,
usehistory="y"
)
results = Entrez.read(handle)
handle.close()
return results
# Example searches
results = search_geo_datasets("breast cancer[MeSH] AND Homo sapiens[Organism]")
print(f"Found {results['Count']} datasets")
# Search by specific platform
results = search_geo_datasets("GPL570[Accession]")
# Search by study type
results = search_geo_datasets("expression profiling by array[DataSet Type]")
What ships with it
1 file 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.
- 1mo ago First seen · 815 lines · 47 tokens per session scan A 45dd249dbb16
geo-database is a skill published in the GitHub repository x-cmd/skill (26 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 5,994 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to geo-database, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
kaggle-research-compute
Use when a research or engineering task needs automatic heavy-compute routing to free Kaggle Kernels through the local broker, with exact-version CPU push, poll and fetch, explicit Internet policy, and resumable host evidence; live GPU and multi-run remain disabled.
lean-strict-verification-gate
Use when checking whether a Lean artifact can safely support a research claim.
modal-research-compute
Use when a research or engineering task needs automatic heavy-compute routing through the unified local broker, including Modal-backed remote CPU, high-memory CPU, or GPU execution.
digest-bridge
Use when the user wants to extract arXiv IDs or DOIs from research or RSS digests and turn them into getscipapers requests or manifests.
venue-ranking-evidence
Use when identifying a journal, conference, or proceedings series from a partial name, acronym, alias, ISSN, or source ID; preserving source-specific rank, quartile, metric, classification, membership, or coverage observations; or proving that the public ICORE detail page displayed one ICORE claim. Live bulk paths are…
opengauss
Use when preparing optional Math Inc. OpenGauss readiness for Lean prove/formalize workflows in the formal research lane. Inert doctor and config guidance only; never auto-installs or claims formal proof from Gauss success.