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 skills add google-deepmind/science-skills --skill string_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/string_database)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/string_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/string_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/google-deepmind/science-skills/string_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/string_database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00050 | $0.00687 |
| Opus 5.5 | $0.00020 | $0.00275 |
| Sonnet 5.5 | $0.00010 | $0.00137 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
string-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 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.
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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STRING Database Skill
This skill allows you to query the STRING database programmatically using a bundled Python CLI wrapper.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/string_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://string-db.org/cgi/access, then (2) create the file recording the notification text and timestamp.
Core Rules
- MANDATORY: Ask for Species First: The STRING API requires NCBI Taxon IDs. You MUST NOT guess or assume a species. If the user does not explicitly state a species or Taxon ID, you MUST stop and ask: "Which species are you interested in? I need the NCBI Taxon ID to proceed." Even for well-known proteins like TP53, BRCA1, or MDM2 that are commonly associated with human studies, you MUST still ask — do not default to Human.
- Never print output to stdout: The
--output <file.tsv>is required. Never read large outputs into context. Instead use jq, python or file operations (grep,head) to process large output. - Map Identifiers first: If you only have common gene names (e.g.,
'TP53'), map them to STRING IDs first as this guarantees much faster server
responses. Use the
mapcommand for this. - Notification: If this skill is used, ensure this is mentioned in the output.
Tool Execution
The CLI is at scripts/string_cli.py and should be run using uv run:
uv run scripts/string_cli.py <command> [options] --output /tmp/out.tsv
Feature Domains (Progressive Disclosure)
Read the following reference files based on the user's request:
- Mapping Identifiers - Map common protein names to STRING IDs.
- Interactions & Network - Find interacting proteins, network topologies, mediators, homology, and visual network images.
- Enrichment & Functional Annotations - Analyze pathway enrichment (GO, KEGG, Pfam), PPI significance, or find all proteins associated with a specific term (e.g. Melanoma).
- Values/Ranks Enrichment - Submit full experimental datasets (e.g., logFC, p-values) for rank-based enrichment analysis using the async background API.
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.
- 1mo ago First seen · 67 lines · 50 tokens per session scan A 6de0f5994327
string-database is a skill published in the GitHub repository google-deepmind/science-skills (3,315 stars, last pushed 7d ago), licensed Apache-2.0. It adds 50 tokens to every session and 687 once invoked, about $0.0002 per session on Opus 5.5. 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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