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 AndyZhuang/Opentest --skill tooluniverse-expression-data-retrievalgit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/tooluniverse-expression-data-retrieval)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-expression-data-retrieval"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-expression-data-retrieval/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/andyzhuang/opentest/tooluniverse-expression-data-retrieval"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-expression-data-retrieval.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 | $0.00088 | $0.02543 |
| Opus 5 | $0.00044 | $0.01272 |
| Sonnet 5 | $0.00018 | $0.00509 |
| Haiku 4.5 | $0.00009 | $0.00254 |
Grade A, and why
tooluniverse-expression-data-retrieval 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gene Expression & Omics Data Retrieval
Retrieve gene expression experiments and multi-omics datasets with proper disambiguation and quality assessment.
IMPORTANT: Always use English terms in tool calls (gene names, tissue names, condition descriptions), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
Workflow Overview
Phase 0: Clarify Query (if ambiguous)
↓
Phase 1: Disambiguate Gene/Condition
↓
Phase 2: Search & Retrieve (Internal)
↓
Phase 3: Report Dataset Profile
Phase 0: Clarification (When Needed)
Ask the user ONLY if:
- Gene name is ambiguous (e.g., "p53" → TP53 or MDM2 studies?)
- Tissue/condition unclear for comparative studies
- Organism not specified for non-human research
Skip clarification for:
- Specific accession numbers (E-MTAB-, E-GEOD-, S-BSST*)
- Clear disease/tissue + organism combinations
- Explicit platform requests (RNA-seq, microarray)
Phase 1: Query Disambiguation
1.1 Gene Name Resolution
If searching by gene, first resolve official identifiers:
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
# For gene-focused searches, resolve official symbol first
# This helps construct better search queries
# Example: "p53" → "TP53" (official HGNC symbol)
Gene Disambiguation Checklist:
- Official gene symbol identified (HGNC for human, MGI for mouse)
- Common aliases noted for search expansion
- Species confirmed
1.2 Construct Search Strategy
| User Query Type | Search Strategy |
|---|---|
| Specific accession | Direct retrieval |
| Gene + condition | "[gene] [condition]" + species filter |
| Disease only | "[disease]" + species filter |
| Technology-specific | Add platform keywords (RNA-seq, microarray) |
Phase 2: Data Retrieval (Internal)
Search silently. Do NOT narrate the process.
2.1 Search Experiments
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.
- 11d ago First seen · 390 lines · 88 tokens per session scan A 1c4e6841b9cd
tooluniverse-expression-data-retrieval is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 88 tokens to every session and 2,543 once invoked, about $0.0004 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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