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 agentmods add skills/google-deepmind/science-skills/quickgo_databasenpx skills add google-deepmind/science-skills --skill quickgo_databasegit clone --depth 1 https://github.com/google-deepmind/science-skillsWrote 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/google-deepmind/science-skills/quickgo_database)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/quickgo_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/quickgo_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 | $0.00083 | $0.01437 |
| Opus 5 | $0.00042 | $0.00718 |
| Sonnet 5 | $0.00017 | $0.00287 |
| Haiku 4.5 | $0.00008 | $0.00144 |
Grade A, and why
quickgo-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 3d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QuickGO Database Skill
GO (Gene Ontology) annotations are one of the main ways to label a gene's function. QuickGO is a fast, web-based browser for the GO and Evidence & Conclusion Ontology (ECO), maintained by the Gene Ontology Annotation (GOA) group at EMBL-EBI.
It provides a centralised resource to explore the functional attributes of gene products (proteins, RNA, and complexes). It is a primary tool for functional annotation mapping since it allows you to link a gene (e.g., USH2A) to its specific biological processes (e.g. sensory perception of light stimulus), molecular functions, and cellular components.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/quickgo_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/QuickGO/ and https://www.ebi.ac.uk/QuickGO/api/index.html, then (2) create the file recording the notification text and timestamp.
Usage
This skill provides a Python CLI wrapper scripts/quickgo_tool.py that queries
the QuickGO REST API. It handles formatting the requests, respecting rate
limits, and safely storing the potentially large JSON responses.
Core Rules
- Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
- Pagination & Limits: Restrict endpoints to a maximum of 100 results per
page using
--limit 100and the--pageparameter for larger datasets. - Output Files: Always use the
--outputflag to save responses to a file incrementally or parse viajq. - Evidence Codes: Prioritize experimental evidence (
ECO:0000269) over electronic (ECO:0000501) to avoid noisy predictions. - Taxon Filtering: Use
--taxonId 9606to restrict results to Human when analysing clinical or human genomic data. - Notification: If this skill is used, ensure this is mentioned in the output.
What ships with it
6 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.
- 3d ago First seen · 125 lines · 83 tokens per session scan A a835ba855337
quickgo-database is a skill published in the GitHub repository google-deepmind/science-skills (2,814 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,437 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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