Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology.
Install String Database
$ npx skills add google-deepmind/science-skills --skill string-database -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google-deepmind/science-skills string-database --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/string_database .claude/skills/string-database && rm -rf skills-srcUse ~/.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/
Install the "string-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/string_database into .claude/skills/string-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "string-database", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google-deepmind/science-skills/tree/main/skills/string_databaseType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google-deepmind/science-skills --skill string-database -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google-deepmind/science-skills string-database --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/string_database .agents/skills/string-database && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "string-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/string_database into .agents/skills/string-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "string-database", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google-deepmind/science-skills --skill string-database -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google-deepmind/science-skills string-database --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/string_database .cursor/skills/string-database && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "string-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/string_database into .cursor/skills/string-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "string-database", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google-deepmind/science-skills.git --path skills/string_database--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google-deepmind/science-skills --skill string-database -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google-deepmind/science-skills string-database --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/string_database .gemini/skills/string-database && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "string-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/string_database into .gemini/skills/string-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "string-database", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google-deepmind/science-skills string-databaseInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google-deepmind/science-skills --skill string-database -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/string_database .github/skills/string-database && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "string-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/string_database into .github/skills/string-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "string-database", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google-deepmind/science-skills --skill string-database -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google-deepmind/science-skills string-database --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/string_database .opencode/skills/string-database && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "string-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/string_database into .opencode/skills/string-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "string-database", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
string-database- GitHub stars
- 3.2k
- Used in
- 1 other repo
- Token cost
- ~731 tokens
- SKILL.md length
- 339 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 40
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology.
- Works in 2 steps: uv: Read the uv skill and follow its… → User Notification: If…
- The user asks about interactions between specific proteins
- SKILL.md covers Prerequisites, Core Rules, Tool Execution and Feature Domains (Progressive…
- Runs Python scripts from its folder; calls uv
What it does
String Database is an agent skill from google-deepmind/science-skills. Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
Its SKILL.md is about 730 tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/enrichment.md`, `references/interactions.md` and `references/mapping.md`).
It sits in Research & Science. 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
- The user asks about interactions between specific proteins
- Interaction evidence
- Confidence scores
- Protein interaction partners
Example prompts
- “/string-database”
Requirements
- Python 3
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- uv: Read the uv skill and follow its Setup instructions to ensure
- User Notification: If .licenses/string_database_LICENSE.txt does not
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 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uv
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):
string-db.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
String Database loads about 731 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 339 words of instructions outside code blocks.
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). 339 words, ~731 tokens.
Download SKILL.mdSave it as
.claude/skills/string-database/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- string-database
- description
- Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
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.tsvFeature 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.
To begin, read the reference file most appropriate to the current task to discover the correct CLI command.
© 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 6 other files (scripts, references) in skills/string_database of google-deepmind/science-skills.
- SKILL.md
- references/citation.bib
- references/enrichment.md
- references/interactions.md
- references/mapping.md
- references/valuesranks.md
- scripts/string_cli.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
String 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| String Database this skillgoogle-deepmind/science-skills | 3.2k | 1 repos | ~731 | Automated safety check: Pass | Apache-2.0 | |
| Hypothesis Generationspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 83k | 5 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 46k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Read arXiv Paperkarpathy/nanochat | 58k | 2 repos | ~494 | Automated safety check: Pass | MIT | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT |
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GitHub Deep Research
bytedance/deer-flow
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Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
Read arXiv Paper
karpathy/nanochat
Fetches the TeX source of an arXiv paper from its URL, reads it and writes a markdown summary tied to the nanochat project.
Content Research Writer
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
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Related
Categories
Questions about String Database
What does String Database do?
Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. String Database is an agent skill from google-deepmind/science-skills. Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology.
When should I use String Database?
String Database fits situations like: the user asks about interactions between specific proteins; interaction evidence; confidence scores; protein interaction partners.
How do I install String Database in Claude Code?
Run `npx skills add google-deepmind/science-skills --skill string-database -a claude-code`. Or copy the skill folder (skills/string_database in google-deepmind/science-skills) into .claude/skills/string-database in your project. Claude Code loads it when a task matches its description.
How do I install String Database in Codex?
Run `npx skills add google-deepmind/science-skills --skill string-database -a codex`. Or copy the skill folder (skills/string_database in google-deepmind/science-skills) into .agents/skills/string-database in your project. Codex loads it when a task matches its description.
Can I use String 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 string-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/string-database, .gemini/skills/string-database, .github/skills/string-database and .opencode/skills/string-database in your project.
What does String Database need to run?
Going by SKILL.md and its folder, String Database needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does String Database access the network?
SKILL.md names 1 domain. As links in the text: string-db.org. This is read from the text; nothing was executed.
Is String 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 String Database use?
String 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 String Database use?
About 731 tokens (SKILL.md is roughly 2.9k 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 1.7k tokens, read only when the agent opens those files.
What are the alternatives to String Database?
Skills that share tags, products or a category with String Database: Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Read arXiv Paper (karpathy/nanochat, 58k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains String 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.