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 encode_ccres_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/encode_ccres_database)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/encode_ccres_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/encode_ccres_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/encode_ccres_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/encode_ccres_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 | $0.00070 | $0.01750 |
| Opus 5.5 | $0.00028 | $0.00700 |
| Sonnet 5.5 | $0.00014 | $0.00350 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
encode-ccres-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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ENCODE Database Skill
This skill allows you to query the ENCODE Registry of cCREs (candidate cis-Regulatory Elements) via the SCREEN GraphQL API. It helps identify functional non-coding DNA elements (like Promoters, Enhancers, and insulators) by analyzing biochemical signatures (DNase, H3K4me3, H3K27ac, CTCF).
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/encode_ccres_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.encodeproject.org/help/rest-api/, 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 the required rate limit gracefully.
- Parsing Output: Do NOT use
catto read the entire JSON output file into context, as it can be extremely large. You MUST usejqto efficiently parse and extract relevant fields. - Notification: If this skill is used, ensure this is mentioned in the output.
Quick Start
# Search cCREs by coordinates
uv run scripts/screen_api.py search --chromosome chr11 \
--start 5205263 --end 5207263 \
--output /tmp/search.json
# Get details for a specific cCRE
uv run scripts/screen_api.py details EH38E2941922 \
--output /tmp/details.json
All subcommands write JSON to disk. Always save output in a temporary location
like /tmp/.
Identifying High-Confidence ("Type A") Biosamples
Biosamples in ENCODE are often categorized by their data completeness. "Type A" (or high-confidence) biosamples are those that have experimental data for all four core epigenetic markers: DNase, H3K4me3, H3K27ac, and CTCF.
The biosamples and details commands automatically enrich their output with
an is_type_a boolean flag for each biosample.
What ships with it
5 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 · 200 lines · 70 tokens per session scan A 756f1c152088
encode-ccres-database is a skill published in the GitHub repository google-deepmind/science-skills (3,307 stars, last pushed 6d ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,750 once invoked, about $0.0003 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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