Science Skills is a collection of add-ons that give AI agents structured instructions, scripts, and references for scientific research, including genomics, structural biology, cheminformatics, and literature search. Researchers use it to guide agents through specialized scientific tasks with information from databases and tools such as AlphaGenome, AFDB, and UniProt. The catalogue entries are individual skills from this collection.
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/opentargets_databasenpx skills add google-deepmind/science-skills --skill opentargets_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/opentargets_database)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/opentargets_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/opentargets_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 | $0.00040 | $0.02404 |
| Opus 5 | $0.00020 | $0.01202 |
| Sonnet 5 | $0.00008 | $0.00481 |
| Haiku 4.5 | $0.00004 | $0.00240 |
Grade A, and why
opentargets-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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
automatically truncates overly large responses. NEVER write your own curl or How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Targets Database Skill
Overview
This skill provides access to the Open Targets Platform GraphQL API. It aggregates multi-modal evidence from genetics (GWAS/eQTL), pathways, animal models, and clinical trials to rank target-disease associations and identify druggable genes.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/opentargets_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://platform-docs.opentargets.org/licence, then (2) create the file recording the notification text and timestamp.
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 fair use and implement retry logic.
- Output Flag: The
--outputflag is always required as output can be very large. Usejqor write your own code to process this JSON file. - Notification: If this skill is used, ensure this is mentioned in the output.
Quick Reference
Always use the provided Python script scripts/query_opentargets.py to quickly
query the database. It handles API communication, retries, formatting, and
automatically truncates overly large responses. NEVER write your own curl or
similar requests.
Usage:
uv run scripts/query_opentargets.py --output /tmp/opentargets_results.json [OPTIONS] COMMAND [ARGS]...
Common Options:
--output PATH: Required. Path to write the JSON output file.--limit N: Limit the number of items returned in arrays (default is 50). Use a smaller number like 10 when doing preliminary exploration.--page-size N: Set the API pagination size (default is 200). Increase if you need more results (e.g., a study with many credible sets).
Available Commands:
What ships with it
3 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 · 204 lines · 40 tokens per session scan A 06f42275cb2a
opentargets-database is a skill published in the GitHub repository google-deepmind/science-skills (2,845 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 2,404 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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