Quickgo ApiSAFE
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Overview
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
e1ba289846fdOBSERVED · 2026-10-08Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| openclaw | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: quickgo-api
description: "Browse and search Gene Ontology annotations via the QuickGO API"
metadata:
openclaw:
emoji: "🧬"
category: "domains"
subcategory: "biomedical"
keywords: ["Gene Ontology", "GO annotations", "protein function", "QuickGO", "EBI", "functional genomics"]
source: "https://www.ebi.ac.uk/QuickGO/"
---
# 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
| Aspect | Code | Description |
|--------|------|-------------|
| Biological Process | `biological_process` | What the gene does |
| Molecular Function | `molecular_function` | Biochemical activity |
| Cellular Component | `cellular_component` | Where in the cell |
### Evidence Codes
| Code | Meaning | Reliability |
|------|---------|-------------|
| `EXP` | Inferred from Experiment | High |
| `IDA` | Inferred from Direct Assay | High |
| `IMP` | Inferred from Mutant Phenotype | High |
| `IPI` | Inferred from Physical Interaction | Medium |
| `ISS` | Inferred from Sequence Similarity | Medium |
| `IEA` | Inferred from Electronic Annotation | Lower |
## 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
- [QuickGO](https://www.ebi.ac.uk/QuickGO/)
- [QuickGO API Docs](https://www.ebi.ac.uk/QuickGO/api)
- [Gene Ontology](http://geneontology.org/)
- Binns, D. et al. (2009). "QuickGO: a web-based tool for Gene Ontology searching." *Bioinformatics* 25(22).Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
e1ba289846fdfull audit observations/trust-audit/skill/brycewang-stanford__quickgo-api.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | e1ba289846fd | SAFE | B | 89 | first audit |
Questions
What does the Quickgo Api skill do?
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Is Quickgo Api safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Quickgo Api access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Quickgo Api work with?
Its documentation mentions openclaw. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (e1ba289846fd), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.