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Agent skill

Encode Ccres Database

by google-deepmind in google-deepmind/science-skills

Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.).

Apache-2.0Auto-check passedBackend & APIs

Install Encode Ccres Database

skills CLI
$ npx skills add google-deepmind/science-skills --skill encode-ccres-database -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google-deepmind/science-skills encode-ccres-database --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/encode_ccres_database .claude/skills/encode-ccres-database && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
encode-ccres-database
GitHub stars
3.2k
Used in
1 other repo
Token cost
~1.7k tokens
SKILL.md length
712 words
Files
6 (incl. scripts, references)
Skills in repo
40
Repo updated
First seen
Licence
Apache-2.0

At a glance

Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.).

  • Works in 2 steps: uv: Read the uv skill and follow its… → User Notification: If…
  • You want to query regulatory annotations
  • SKILL.md covers Prerequisites, Core Rules, ENCODE Portal REST API (Direct… and Custom Queries (SCREEN GraphQL)
  • Runs Python scripts from its folder; calls uv, jq and python3

What it does

Encode Ccres Database is an agent skill from google-deepmind/science-skills. Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). Use when you want to query regulatory annotations or raw experimental data across human cell types.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/graphql_schema.md`, `references/json_output_structure.md` and `scripts/encode_portal_api.py`).

It sits in Backend & APIs, covering GraphQL, REST APIs and Bioinformatics. It works with GraphQL. The repository describes itself as: GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other… The licence is Apache-2.0.

When your agent uses it

  • You want to query regulatory annotations
  • Raw experimental data across human cell types

Example prompts

  • “/encode-ccres-database”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. uv: Read the uv skill and follow its Setup instructions to ensure
  2. User Notification: If .licenses/encode_ccres_database_LICENSE.txt does

What it can do on your machine

Read from SKILL.md and the folder at commit 6883275. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv
    • jq
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • encodeproject.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Encode Ccres Database loads about 1.7k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 712 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from google-deepmind/science-skills at commit 6883275, republished under its Apache-2.0 licence (© google-deepmind). 712 words, ~1,712 tokens.

Download SKILL.mdSave it as .claude/skills/encode-ccres-database/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
encode-ccres-database
description
Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). Use when you want to query regulatory annotations or raw experimental data across human cell types.

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

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. 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 cat to read the entire JSON output file into context, as it can be extremely large. You MUST use jq to efficiently parse and extract relevant fields.
  • Notification: If this skill is used, ensure this is mentioned in the output.
Quick Start
bash
# 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.

Example: Finding high-confidence cell types

bash
uv run scripts/screen_api.py biosamples --output /tmp/biosamples.json
# Use jq to filter for Type A biosamples
jq '.data.ccREBiosampleQuery.biosamples[] | select(.is_type_a == true) | .displayname' /tmp/biosamples.json
Parsing Output (CRITICAL)

Do NOT use cat to read the entire JSON output file into context, as it can be extremely large. Instead, you MUST use jq to efficiently parse and extract the relevant fields from the JSON file saved by the script. If jq is not available on the system, write your own Python filtering code (e.g., python3 -c "import json...") to extract the necessary data.

For a complete reference of the JSON structure returned by eachmcommand (so you know which fields to query with jq), read references/json_output_structure.md.

Show full SKILL.md (373 more words)Show less
Available Commands

  • search: Search cCREs by coordinates, accessions, or epigenetic signals.

    bash
    uv run scripts/screen_api.py search \
    --chromosome chr11 --start 5205263 --end 5207263 \
    --output /tmp/search.json

  • nearby-genes: Find nearby genes for given cCRE accessions.

    bash
    uv run scripts/screen_api.py nearby-genes \
    EH38E1516972 --output /tmp/nearby.json

  • details: Get detailed information and biosample-specific max Z-scores for a specific cCRE.

    bash
    uv run scripts/screen_api.py details EH38E2941922 \
    --output /tmp/details.json

  • biosamples: Get biosample metadata for an assembly.

    bash
    uv run scripts/screen_api.py biosamples \
    --output /tmp/biosamples.json

  • orthologs: Get orthologous cCREs in another assembly.

    bash
    uv run scripts/screen_api.py orthologs EH38E2941922 \
    --output /tmp/orthologs.json

  • linked-genes: Find linked genes via methods like HiC or eQTLs.

    bash
    uv run scripts/screen_api.py linked-genes \
    EH38E1516972 --output /tmp/linked.json

  • gene-expression: Get gene expression (TPM) across all biosamples for a named gene. Internally resolves the gene symbol to an Ensembl gene ID, then queries per-biosample RNA-seq quantifications.

    bash
    uv run scripts/screen_api.py gene-expression GAPDH \
    --output /tmp/gene_expr.json

  • entex: Get ENTEx data for a cCRE or genomic region.

    bash
    uv run scripts/screen_api.py entex \
    --accession EH38E1310345 \
    --output /tmp/entex.json
    bash
    uv run scripts/screen_api.py entex \
    --region chr1:1000068:1000409 \
    --output /tmp/entex.json

  • gwas: Query genome-wide association studies, SNPs, or enrichment data.

    bash
    uv run scripts/screen_api.py gwas studies \
    --output /tmp/gwas.json
    bash
    uv run scripts/screen_api.py gwas snps --study \
    Ahola-Olli_AV-27989323-Eotaxin_levels \
    --output /tmp/gwas_snps.json

You can supply the --assembly mm10 or --assembly grch38 flag to explicitly request a specific assembly for most commands. By default, the script targets grch38 but will automatically fall back to mm10 if no results are found or if the query fails.

ENCODE Portal REST API (Direct Access)

For accessing raw experiments, ChIP-seq peaks, or other datasets that are not represented as cCREs in SCREEN, use the scripts/encode_portal_api.py script. It allows custom queries to the ENCODE Portal REST API.

Usage
bash
uv run scripts/encode_portal_api.py search "type=Experiment&target.label=ZNF549" --output /tmp/znf549_experiments.json
Data Analysis Tips

When analyzing .bed or .bigBed files downloaded from ENCODE, standard bioinformatics tools are highly recommended for finding overlaps (e.g., between gene promoters and peaks):

  • bedtools: For fast mathematical operations on genomic intervals.
  • bigBedToBed: For converting binary BigBed files to readable BED format.
  • pybedtools: A Python wrapper for bedtools.

Write custom logic if these tools are not pre-installed.

Custom Queries (SCREEN GraphQL)

If you need to make a complex GraphQL query that the script does not support, read references/graphql_schema.md for a reference of available queries, arguments, and return fields in the SCREEN GraphQL API.

© google-deepmind, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in skills/encode_ccres_database of google-deepmind/science-skills.

  • SKILL.md
  • references/citation.bib
  • references/graphql_schema.md
  • references/json_output_structure.md
  • scripts/encode_portal_api.py
  • scripts/screen_api.py

Open the folder on GitHubat commit 6883275

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in google-deepmind/science-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Encode Ccres Database next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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API Auditbriiirussell/cybersecurity-skills412—~2.8kAutomated safety check: NotesMIT
Data Client Schemareactive/data-client2k—~2.3kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Encode Ccres Database

What does Encode Ccres Database do?

Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). Encode Ccres Database is an agent skill from google-deepmind/science-skills.).

When should I use Encode Ccres Database?

Encode Ccres Database fits situations like: you want to query regulatory annotations; raw experimental data across human cell types.

How do I install Encode Ccres Database in Claude Code?

Run `npx skills add google-deepmind/science-skills --skill encode-ccres-database -a claude-code`. Or copy the skill folder (skills/encode_ccres_database in google-deepmind/science-skills) into .claude/skills/encode-ccres-database in your project. Claude Code loads it when a task matches its description.

How do I install Encode Ccres Database in Codex?

Run `npx skills add google-deepmind/science-skills --skill encode-ccres-database -a codex`. Or copy the skill folder (skills/encode_ccres_database in google-deepmind/science-skills) into .agents/skills/encode-ccres-database in your project. Codex loads it when a task matches its description.

Can I use Encode Ccres Database in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google-deepmind/science-skills --skill encode-ccres-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/encode-ccres-database, .gemini/skills/encode-ccres-database, .github/skills/encode-ccres-database and .opencode/skills/encode-ccres-database in your project.

What does Encode Ccres Database need to run?

Going by SKILL.md and its folder, Encode Ccres Database needs Python for the scripts in its folder and the command-line tools its instructions call (uv, jq and python3). Our summary lists: Python 3.

Does Encode Ccres Database access the network?

SKILL.md names 1 domain. As links in the text: encodeproject.org. This is read from the text; nothing was executed.

Is Encode Ccres Database safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Encode Ccres Database use?

Encode Ccres Database is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Encode Ccres Database use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.5k tokens, read only when the agent opens those files.

What are the alternatives to Encode Ccres Database?

Skills that share tags, products or a category with Encode Ccres Database: API Designer (Jeffallan/claude-skills, 12k stars), Nodejs Backend Patterns (ever-works/ever-works, 158 stars), Backend Endpoint (qf-studio/navigator, 354 stars) and API Audit (briiirussell/cybersecurity-skills, 412 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Encode Ccres Database?

google-deepmind (a GitHub organization) maintains it in google-deepmind/science-skills, which has 3,216 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on September 15, 2026.

Source: google-deepmind/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.