Field Service Sobject Create Configure
forcedotcom/sf-skills
Headless 360 REST API deployment step for creating sObject records.
Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API.
Install Quickgo Database
$ npx skills add google-deepmind/science-skills --skill quickgo-database -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google-deepmind/science-skills quickgo-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/quickgo_database .claude/skills/quickgo-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 "quickgo-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/quickgo_database into .claude/skills/quickgo-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quickgo-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/quickgo_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 quickgo-database -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google-deepmind/science-skills quickgo-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/quickgo_database .agents/skills/quickgo-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 "quickgo-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/quickgo_database into .agents/skills/quickgo-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quickgo-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 quickgo-database -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google-deepmind/science-skills quickgo-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/quickgo_database .cursor/skills/quickgo-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 "quickgo-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/quickgo_database into .cursor/skills/quickgo-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quickgo-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/quickgo_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 quickgo-database -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google-deepmind/science-skills quickgo-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/quickgo_database .gemini/skills/quickgo-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 "quickgo-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/quickgo_database into .gemini/skills/quickgo-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quickgo-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 quickgo-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 quickgo-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/quickgo_database .github/skills/quickgo-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 "quickgo-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/quickgo_database into .github/skills/quickgo-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quickgo-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 quickgo-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 quickgo-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/quickgo_database .opencode/skills/quickgo-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 "quickgo-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/quickgo_database into .opencode/skills/quickgo-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quickgo-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
quickgo-database- GitHub stars
- 3.2k
- Used in
- 1 other repo
- Token cost
- ~1.4k tokens
- SKILL.md length
- 529 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 40
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API.
- Works in 4 steps: Map a gene to its functions (Annotations) → Find all genes in a pathway → Explore the GO Hierarchy → …
- Querying drug targets (use OpenTargets)
- SKILL.md covers Prerequisites, Usage, Core Rules and Common Workflows
- Runs Python scripts from its folder; calls uv
What it does
Quickgo Database is an agent skill from google-deepmind/science-skills. Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this when you need to map genes to biological processes, molecular functions, or cellular components, find genes associated with a specific pathway/GO term, or explore the Gene Ontology hierarchy. Do not use for querying drug targets (use OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).
Its SKILL.md is about 1.4k 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/annotations.md`, `references/eco_terms.md` and `references/gene_products.md`).
It sits in Backend & APIs, covering REST APIs and Diagrams. 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
- Querying drug targets (use OpenTargets)
- Mechanistic signaling pathway diagrams (use KEGG)
Example prompts
- “/quickgo-database”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
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):
ebi.ac.uk
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
Quickgo Database loads about 1.4k tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 529 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). 529 words, ~1,415 tokens.
Download SKILL.mdSave it as
.claude/skills/quickgo-database/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- quickgo-database
- description
- Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this when you need to map genes to biological processes, molecular functions, or cellular components, find genes associated with a specific pathway/GO term, or explore the Gene Ontology hierarchy. Do not use for querying drug targets (use OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).
QuickGO Database Skill
GO (Gene Ontology) annotations are one of the main ways to label a gene's function. QuickGO is a fast, web-based browser for the GO and Evidence & Conclusion Ontology (ECO), maintained by the Gene Ontology Annotation (GOA) group at EMBL-EBI.
It provides a centralised resource to explore the functional attributes of gene products (proteins, RNA, and complexes). It is a primary tool for functional annotation mapping since it allows you to link a gene (e.g., USH2A) to its specific biological processes (e.g. sensory perception of light stimulus), molecular functions, and cellular components.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/quickgo_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.ebi.ac.uk/QuickGO/ and https://www.ebi.ac.uk/QuickGO/api/index.html, then (2) create the file recording the notification text and timestamp.
Usage
This skill provides a Python CLI wrapper scripts/quickgo_tool.py that queries
the QuickGO REST API. It handles formatting the requests, respecting rate
limits, and safely storing the potentially large JSON responses.
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.
- Pagination & Limits: Restrict endpoints to a maximum of 100 results per
page using
--limit 100and the--pageparameter for larger datasets. - Output Files: Always use the
--outputflag to save responses to a file incrementally or parse viajq. - Evidence Codes: Prioritize experimental evidence (
ECO:0000269) over electronic (ECO:0000501) to avoid noisy predictions. - Taxon Filtering: Use
--taxonId 9606to restrict results to Human when analysing clinical or human genomic data. - Notification: If this skill is used, ensure this is mentioned in the output.
The tool has four main subcommands:
go: For retrieving information about GO terms (e.g. definitions, ancestors, descendants, and slims). See references/go_terms.md.annotation: For finding functional annotations linking gene products to GO terms. This is your primary functional mapper. See references/annotations.md.geneproduct: For resolving gene symbols (likePROC) to their formal database identifiers. See references/gene_products.md.eco: For Evidence & Conclusion Ontology terms (used in annotations to indicate how an annotation was derived, e.g. experimental vs electronic). See references/eco_terms.md.
Show full SKILL.md (152 more words)Show less
Common Workflows
1. Map a gene to its functions (Annotations)
To find out what a gene does, you must first resolve its symbol to a UniProtKB
ID, and then query its annotations. Often it is best to filter for experimental
evidence (e.g. ECO:0000269 for EXP, or others like IDA, IMP) to avoid noisy
electronic predictions.
# Step 1: Find the UniProtKB ID for human (9606) gene PROC
uv run scripts/quickgo_tool.py geneproduct search --query "PROC" --taxonId 9606 --limit 5 --output proc_id.json
# (Look at proc_id.json, observe the ID is e.g., UniProtKB:P04070)
# Step 2: Find experimental GO annotations for that ID
uv run scripts/quickgo_tool.py annotation search --geneProductId "UniProtKB:P04070" --taxonId 9606 --evidenceCode "ECO:0000269" --limit 50 --output proc_annotations.json2. Find all genes in a pathway
To find all genes annotated to a specific GO term (e.g., GO:0003700 for "transcription factor activity"):
# Find human genes with this specific molecular function
uv run scripts/quickgo_tool.py annotation search --goId "GO:0003700" --taxonId 9606 --limit 50 --output tf_genes.json3. Explore the GO Hierarchy
To check if a specific GO term is a descendant of a broader category, or to fetch its definition:
# Fetch term details (definitions, synonyms)
uv run scripts/quickgo_tool.py go terms --ids "GO:0003150" --output term_details.json
# Check ancestry (e.g., is GO:0001917 a child of something?)
uv run scripts/quickgo_tool.py go terms --ids "GO:0001917" --relation ancestors --output term_ancestors.json4. Create a GO Slim Summary
If you have a list of candidate genes and want a high-level functional summary, you can map them up to a predefined GO Slim. First, fetch the annotations for the genes to extract their GO IDs, then pass those IDs to the slim endpoint:
# Step 1: Find GO IDs for candidate genes (e.g., via their UniProt IDs, fetching their annotations)
# ... (output yields e.g., GO:0006915,GO:0008219)
# Step 2: Create a slim summary from those specific GO IDs
uv run scripts/quickgo_tool.py go slim --slimsToIds "GO:0005575,GO:0008150,GO:0003674" --slimsFromIds "GO:0006915,GO:0008219" --output my_slim.json© 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/quickgo_database of google-deepmind/science-skills.
- SKILL.md
- references/annotations.md
- references/citation.bib
- references/eco_terms.md
- references/gene_products.md
- references/go_terms.md
- scripts/quickgo_tool.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
Quickgo 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 |
|---|---|---|---|---|---|---|
| Quickgo Database this skillgoogle-deepmind/science-skills | 3.2k | 1 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Field Service Sobject Create Configureforcedotcom/sf-skills | 1.1k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Tldraw APIhoangnb24/skills | 230 | — | ~1.5k | Automated safety check: Pass | None | |
| API DesignerJeffallan/claude-skills | 12k | 2 repos | ~2k | Automated safety check: Pass | MIT | |
| Paperclippaperclipai/paperclip | 98k | — | ~9.6k | Automated safety check: Pass | MIT | |
| Nodejs Backend Patternsever-works/ever-works | 158 | 17 repos | ~4k | Automated safety check: Pass | AGPL-3.0 |
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Related
Categories
Questions about Quickgo Database
What does Quickgo Database do?
Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Quickgo Database is an agent skill from google-deepmind/science-skills. Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API.
When should I use Quickgo Database?
Quickgo Database fits situations like: querying drug targets (use OpenTargets); mechanistic signaling pathway diagrams (use KEGG).
How do I install Quickgo Database in Claude Code?
Run `npx skills add google-deepmind/science-skills --skill quickgo-database -a claude-code`. Or copy the skill folder (skills/quickgo_database in google-deepmind/science-skills) into .claude/skills/quickgo-database in your project. Claude Code loads it when a task matches its description.
How do I install Quickgo Database in Codex?
Run `npx skills add google-deepmind/science-skills --skill quickgo-database -a codex`. Or copy the skill folder (skills/quickgo_database in google-deepmind/science-skills) into .agents/skills/quickgo-database in your project. Codex loads it when a task matches its description.
Can I use Quickgo 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 quickgo-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/quickgo-database, .gemini/skills/quickgo-database, .github/skills/quickgo-database and .opencode/skills/quickgo-database in your project.
What does Quickgo Database need to run?
Going by SKILL.md and its folder, Quickgo Database needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Quickgo Database access the network?
SKILL.md names 1 domain. As links in the text: ebi.ac.uk. This is read from the text; nothing was executed.
Is Quickgo 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 Quickgo Database use?
Quickgo 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 Quickgo Database use?
About 1.4k tokens (SKILL.md is roughly 5.7k 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 2k tokens, read only when the agent opens those files.
What are the alternatives to Quickgo Database?
Skills that share tags, products or a category with Quickgo Database: Field Service Sobject Create Configure (forcedotcom/sf-skills, 1.1k stars), Tldraw API (hoangnb24/skills, 230 stars), API Designer (Jeffallan/claude-skills, 12k stars) and Paperclip (paperclipai/paperclip, 98k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Quickgo 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.