Agent skill

Quickgo API

by wentorai in wentorai/research-plugins

Browse and search Gene Ontology annotations via the QuickGO API

MITAuto-check passedResearch & Science

Install Quickgo API

skills CLI
$ npx skills add wentorai/research-plugins --skill quickgo-api -a claude-code

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

GitHub CLI
$ gh skill install wentorai/research-plugins quickgo-api --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/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/domains/biomedical/quickgo-api .claude/skills/quickgo-api && 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
quickgo-api
GitHub stars
298
Used in
1 other repo
Token cost
~1.4k tokens
SKILL.md length
153 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Browse and search Gene Ontology annotations via the QuickGO API

  • Research & Science work in your project
  • SKILL.md covers Overview, API Endpoints, Python Usage and References
  • Calls curl; reaches ebi.ac.uk

What it does

Quickgo API is an agent skill from wentorai/research-plugins. Browse and search Gene Ontology annotations via the QuickGO API

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “/quickgo-api”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. 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

    Shell commands in SKILL.md call:

    • curl

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • ebi.ac.uk

    Also links to:

    • geneontology.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

Quickgo API loads about 1.4k tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 153 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~19
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 153 words, ~1,386 tokens.

Download SKILL.mdSave it as .claude/skills/quickgo-api/SKILL.md (or your agent's skills folder).
name
quickgo-api
description
Browse and search Gene Ontology annotations via the QuickGO API

QuickGO API

Overview

QuickGO is the EBI's fast browser and API for Gene Ontology (GO) annotations — the standard framework for describing gene/protein functions across all organisms. It provides access to 800M+ GO annotations covering biological processes, molecular functions, and cellular components. Essential for functional genomics, pathway analysis, and gene set enrichment. Free, no authentication.

API Endpoints

Base URL
https://www.ebi.ac.uk/QuickGO/services
Search GO Terms
bash
# Search terms by keyword
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/search?query=apoptosis&limit=20"

# Get term details
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915"

# Get term ancestors/descendants
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/ancestors"
curl "https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/GO:0006915/descendants"
Query Annotations
bash
# Get annotations for a protein (UniProt ID)
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?geneProductId=P04637&limit=50"

# Annotations for a GO term
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?goId=GO:0006915&taxonId=9606&limit=50"

# Filter by evidence code
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\
goId=GO:0006915&taxonId=9606&evidence=EXP,IDA,IMP&limit=50"

# Filter by aspect (ontology branch)
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/search?\
geneProductId=P04637&aspect=biological_process"
Download Annotations
bash
# Download as TSV
curl "https://www.ebi.ac.uk/QuickGO/services/annotation/downloadSearch?\
goId=GO:0006915&taxonId=9606&downloadLimit=10000" -o annotations.tsv
GO Aspects
AspectCodeDescription
Biological Processbiological_processWhat the gene does
Molecular Functionmolecular_functionBiochemical activity
Cellular Componentcellular_componentWhere in the cell
Evidence Codes
CodeMeaningReliability
EXPInferred from ExperimentHigh
IDAInferred from Direct AssayHigh
IMPInferred from Mutant PhenotypeHigh
IPIInferred from Physical InteractionMedium
ISSInferred from Sequence SimilarityMedium
IEAInferred from Electronic AnnotationLower

Python Usage

python
import requests

BASE_URL = "https://www.ebi.ac.uk/QuickGO/services"


def search_go_terms(query: str, limit: int = 20) -> list:
    """Search Gene Ontology terms."""
    resp = requests.get(
        f"{BASE_URL}/ontology/go/search",
        params={"query": query, "limit": limit},
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for term in data.get("results", []):
        results.append({
            "id": term.get("id"),
            "name": term.get("name"),
            "aspect": term.get("aspect"),
            "definition": term.get("definition", {}).get("text", ""),
        })
    return results


def get_protein_annotations(uniprot_id: str,
                            aspect: str = None,
                            experimental_only: bool = False) -> list:
    """Get GO annotations for a protein."""
    params = {"geneProductId": uniprot_id, "limit": 100}
    if aspect:
        params["aspect"] = aspect
    if experimental_only:
        params["evidence"] = "EXP,IDA,IMP,IPI,IGI,IEP"

    resp = requests.get(
        f"{BASE_URL}/annotation/search",
        params=params,
    )
    resp.raise_for_status()
    data = resp.json()

    annotations = []
    for ann in data.get("results", []):
        annotations.append({
            "go_id": ann.get("goId"),
            "go_name": ann.get("goName"),
            "aspect": ann.get("goAspect"),
            "evidence": ann.get("goEvidence"),
            "reference": ann.get("reference"),
        })
    return annotations


def get_term_genes(go_id: str, taxon_id: int = 9606,
                   limit: int = 100) -> list:
    """Get genes annotated with a GO term."""
    params = {
        "goId": go_id,
        "taxonId": taxon_id,
        "limit": limit,
    }
    resp = requests.get(
        f"{BASE_URL}/annotation/search",
        params=params,
    )
    resp.raise_for_status()
    data = resp.json()

    genes = set()
    for ann in data.get("results", []):
        genes.add(ann.get("geneProductId", ""))
    return sorted(genes)


# Example: search for apoptosis-related GO terms
terms = search_go_terms("programmed cell death")
for t in terms[:5]:
    print(f"{t['id']}: {t['name']} ({t['aspect']})")

# Example: get p53 protein annotations
annotations = get_protein_annotations("P04637",
                                      experimental_only=True)
for a in annotations[:10]:
    print(f"  {a['go_id']} {a['go_name']} [{a['evidence']}]")

References

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/domains/biomedical/quickgo-api of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

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 wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

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Questions about Quickgo API

What does Quickgo API do?

Browse and search Gene Ontology annotations via the QuickGO API. Quickgo API is an agent skill from wentorai/research-plugins.

When should I use Quickgo API?

Quickgo API fits situations like: research & Science work in your project.

How do I install Quickgo API in Claude Code?

Run `npx skills add wentorai/research-plugins --skill quickgo-api -a claude-code`. Or copy the skill folder (skills/domains/biomedical/quickgo-api in wentorai/research-plugins) into .claude/skills/quickgo-api in your project. Claude Code loads it when a task matches its description.

How do I install Quickgo API in Codex?

Run `npx skills add wentorai/research-plugins --skill quickgo-api -a codex`. Or copy the skill folder (skills/domains/biomedical/quickgo-api in wentorai/research-plugins) into .agents/skills/quickgo-api in your project. Codex loads it when a task matches its description.

Can I use Quickgo API 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 wentorai/research-plugins --skill quickgo-api -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-api, .gemini/skills/quickgo-api, .github/skills/quickgo-api and .opencode/skills/quickgo-api in your project.

What does Quickgo API need to run?

Going by SKILL.md and its folder, Quickgo API needs the command-line tools its instructions call (curl). Our summary lists: Python 3.

Does Quickgo API access the network?

SKILL.md names 2 domains. In commands or code: ebi.ac.uk; the agent is likely to contact it when it follows the instructions. As links in the text: geneontology.org. This is read from the text; nothing was executed.

Is Quickgo API 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. Review the folder before installing.

What licence does Quickgo API use?

Quickgo API is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Quickgo API use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Quickgo API?

Skills that share tags, products or a category with Quickgo API: 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 Quickgo API?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.