Opentargets Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The Open Targets Database skill provides a structured interface to the Open Targets Platform GraphQL API. It enables researchers to retrieve aggregated evidence from genetics, pathways, animal models, and clinical trials. By using the provided Python wrapper, users can perform complex queries to rank target-disease associations, identify druggable genes, and explore clinical trial data while adhering to API best practices and fair-use policies.
What it does
- Fetches target-disease associations and ranks them based on multi-modal evidence.
- Retrieves druggability, tractability, and clinical safety data for specific gene targets.
- Identifies drugs and clinical candidates associated with specific diseases.
- Performs Locus-to-Gene (L2G) predictions to identify causal genes at genomic loci.
- Searches for disease metadata using standard identifiers like MONDO or EFO.
- Executes custom GraphQL queries for advanced data retrieval beyond built-in commands.
When to use it
- When identifying potential therapeutic targets for a specific disease.
- When exploring the druggability of a gene or protein.
- When investigating the genetic evidence supporting a target-disease association.
- When looking for clinical trial information for drugs related to a specific condition.
- When performing fine-mapping analysis using credible sets from GWAS studies.
Advantages
- Simplifies complex GraphQL API interactions into easy-to-use command-line subcommands.
- Includes built-in retry logic and fair-use rate limiting.
- Automatically handles response truncation to manage context window usage.
- Provides standardized tools for genomic range searches and L2G analysis.
- Ensures consistent output formatting via JSON files.
Usage examples
- “Find all target genes associated with Rheumatoid Arthritis.”
- The agent uses the get-associated-targets command with the appropriate disease ID to return a list of associated genes and their evidence scores.
- “What is the druggability status of the gene ENSG00000169083?”
- The agent executes the get-target-druggability command to retrieve tractability data and clinical trial safety information for the specified Ensembl ID.
- “Find all drugs in Phase III or higher for a specific disease.”
- The agent uses the get-disease-drugs command with the --min-stage PHASE_3 flag to filter and return relevant clinical candidates.
- “Identify the most likely causal gene for a specific variant.”
- The agent runs the get-l2g command for the given variant ID to retrieve Locus-to-Gene predictions.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google-deepmind/opentargets-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip opentargets-database.zip -d .agents/skills
Documentation by Google DeepMind
Open Targets Database Skill
Overview
This skill provides access to the Open Targets Platform GraphQL API. It aggregates multi-modal evidence from genetics (GWAS/eQTL), pathways, animal models, and clinical trials to rank target-disease associations and identify druggable genes.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.User Notification: If .licenses/opentargets_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://platform-docs.opentargets.org/licence, 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 fair use and implement retry logic.
Output Flag: The
--outputflag is always required as output can be very large. Usejqor write your own code to process this JSON file.Notification: If this skill is used, ensure this is mentioned in the output.
Quick Reference
Always use the provided Python script scripts/query_opentargets.py to quickly
query the database. It handles API communication, retries, formatting, and
automatically truncates overly large responses. NEVER write your own curl or
similar requests.
Usage:
uv run scripts/query_opentargets.py --output /tmp/opentargets_results.json [OPTIONS] COMMAND [ARGS]...Common Options:
--output PATH: Required. Path to write the JSON output file.--limit N: Limit the number of items returned in arrays (default is 50). Use a smaller number like 10 when doing preliminary exploration.--page-size N: Set the API pagination size (default is 200). Increase if you need more results (e.g., a study with many credible sets).
Available Commands:
get-gwas-studiesdisease_id: Fetches all GWAS studies associated with a specific disease ID (e.g.MONDO_0008383for Rheumatoid Arthritis).get-study-credible-setsstudy_id: Fetches all credible sets for a given study ID (e.g.FINNGEN_R12_RX_CROHN_2NDLINE). Returns confidence, finemapping method, variant, and p-value info.get-qtl-credible-setsvariant_id: Retrieves QTL credible sets for a specific variant ID (e.g.19_44908822_C_T).get-l2gvariant_id [--study-id ID]: Returns Locus-to-Gene (L2G) predictions/scores for a locus to identify the most likely causal gene. Onlyvariant_idis required; use--study-idto filter to a specific study. Acceptschrprefix (e.g.chr1_113834946_A_G).get-target-druggabilityensembl_id: Provides tractability data (small molecule, antibody, etc.) and clinical trial safety info for a gene/target.get-associated-targetsdisease_id: Find all target genes associated with a specific disease ID (EFO or MONDO).get-disease-drugsdisease_id [--min-stage STAGE]: Find all drugs and clinical candidates associated with a disease. Use--min-stageto filter (e.g.,PHASE_3for Phase III or Approved).get-associated-diseasesensembl_id: Find all diseases associated with a specific target Ensembl ID.search-diseasequery_string: Search for a disease by name to find its ID and other metadata.get-credible-sets-near-targetensembl_id [--window N]: Fetches credible sets for a target and filters them to those within a genomic window around the target. Useful for finding variants "nearby" a gene.custom-queryquery [--variables '{}']: Run a raw GraphQL query for any other Open Targets data.
L2G Query Usage
The get-l2g command has two modes:
Variant only (
get-l2g <variant_id>): Returns L2G predictions from all credible sets across all studies where that variant is the lead variant. This can return a large number of results (e.g., hundreds). Use this when the user wants a broad view of which gene is most likely causal at a locus, or when no specific study is mentioned.Variant + study (
get-l2g <variant_id> --study-id <study_id>): Returns L2G predictions only for credible sets from that specific study. Use this when the user asks about a specific GWAS study or when you need to narrow down the results.
Incomplete results warning: The variant-only mode can return hundreds of credible sets. The default
--page-sizeis 200, so if the API reports acounthigher than the number ofrowsreturned, you are seeing incomplete results. Always comparecountto the actual number of rows. If they differ, either increase--page-sizeor inform the user that only a subset was retrieved.
Querying by Region
To find studies with variants "nearby" a gene, use
get-credible-sets-near-target, which improves upon the base API by performing
a flexible search based on genomic position: uv run scripts/query_opentargets.py --output /tmp/results.json get-credible-sets-near-target ENSG00000156515 --window 500000
Note that the Open Targets GraphQL schema includes a regions parameter for
credibleSets, however it performs an exact match against pre-computed region
strings (e.g., chr10:68769984-69903496) and there is some missing data. Use
get-credible-sets-near-target as it allows a genomic range overlap search.
This fetches credible sets associated with the target and filters them in Python based on the variant's genomic position.
Advanced GraphQL Queries
If you need to query endpoints or fields not exposed by the built-in
subcommands, use the custom-query subcommand.
Before writing a custom query: Read the reference documentation to understand the API schema, types, and see example queries. See references/OpenTargets_GraphQL_Guide.md for full schema details, endpoints, and examples.
Example: Finding drugs for a disease
uv run scripts/query_opentargets.py custom-query \
query drugsForDisease($id: String!) {
disease(efoId: $id) {
name
drugAndClinicalCandidates {
count
rows {
maxClinicalStage
drug {
id
name
}
}
}
}
}' \
--variables '{"id": "EFO_1001006"}'
--output '/tmp/opentargets_result.json'Confidence Star Ratings
The Open Targets Platform assigns a confidence level to each credible set based on the fine-mapping method and quality checks. These correspond to star ratings displayed in the platform UI:
| Stars | Confidence String (API value) |
|---|---|
| ★★★★ (4 stars) | SuSiE fine-mapped credible set with in-sample LD |
| ★★★ (3 stars) | SuSiE fine-mapped credible set with out-of-sample LD |
| ★★ (2 stars) | `PICS fine-mapped credible set extracted from summary |
| : : statistics` : | |
| ★ (1 star) | PICS fine-mapped credible set based on reported top hit |
| None | Unknown confidence |
When users ask about "N-star confidence", match their request to the
corresponding string in the confidence field of the API response.
Tips and Common Mistakes
ID Formats:
Disease IDs are typically MONDO IDs (e.g.
MONDO_0008383for Rheumatoid Arthritis) or EFO IDs (e.g.EFO_0009460). Use thesearch-diseasecommand to find the correct ID.Target IDs must be Ensembl IDs (e.g.
ENSG00000169083), not HGNC symbols. If you only have a gene symbol, you may need to map it first using a custom GraphQLsearchquery.Variant IDs are formatted as
chromosome_position_ref_alt(e.g.,1_154426264_C_T). Achrprefix (e.g.chr1_154426264_C_T) is automatically stripped by the tool.Study IDs can be GWAS Catalog IDs (e.g.
GCST90204201) or project-specific IDs (e.g.FINNGEN_R12_RX_CROHN_2NDLINE).
Truncation: The tool truncates arrays longer than
--limitto protect the context window. If you see"_truncated", you can run the query again with a higher limit if you specifically need more data, but be cautious with large limit values. Always use the--outputflag to save the result to a file and avoid terminal output truncation.Pagination and incomplete results: The
--page-sizeoption (default: 200) controls how many items are fetched from the API. Always check thecountfield in the response and compare it to the number ofrowsactually returned. Ifcount> number of rows, you have incomplete data — either increase--page-sizeto fetch more, or inform the user that only a partial result set was returned. This is especially important forget-l2gwithout--study-id, which can return hundreds of credible sets.
Files
- ATTRIBUTION.mdAdded by AutoGineer922 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md9.3 KiB
- references/OpenTargets_GraphQL_Guide.md7.7 KiB
- references/citation.bib2.4 KiB
- scripts/query_opentargets.pyScript18.2 KiB
scripts/query_opentargets.py
# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Queries the Open Targets Platform GraphQL API.
This script provides a command-line interface to query various endpoints of the
Open Targets GraphQL API, including GWAS studies, QTL credible sets, L2G
predictions, target druggability, and disease/target associations.
"""
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "polite-http",
# ]
# ///
import argparse
import json
import re
import sys
from typing import Any
from polite_http import http_client
BASE_URL = "https://api.platform.opentargets.org/api/v4/graphql"
_CLIENT = http_client.HttpClient(BASE_URL, qps=1.0)
CLINICAL_STAGES = ["PHASE_0", "PHASE_1", "PHASE_2", "PHASE_3", "APPROVAL"]
STAGE_ORDER = {stage: i for i, stage in enumerate(CLINICAL_STAGES)}
def normalize_variant_id(variant_id: str) -> str:
"""Normalize variant ID by stripping 'chr' prefix if present."""
return re.sub(r"^chr", "", variant_id, flags=re.IGNORECASE)
def truncate_data(data: Any, limit: int = 50) -> Any:
"""Recursively truncates lists within a dictionary or list to a given limit.
This function is designed to limit the size of potentially large lists within
the JSON response from the OpenTargets API, making the output more manageable.
Specifically handles the "rows" field within dictionaries that also have a
"count" field, common in OpenTargets GraphQL responses.
Args:
data: The data structure (dict or list) to potentially truncate.
limit: The maximum number of items to keep in any list.
Returns:
The truncated data structure.
"""
if isinstance(data, list):
if len(data) > limit:
result = [truncate_data(item, limit) for item in data[:limit]]
result.append({"_truncated": f"(Showing {limit} of {len(data)} items)"})
return result
return [truncate_data(item, limit) for item in data]
elif isinstance(data, dict):
result = {}
for k, v in data.items():
if k == "rows" and isinstance(v, list) and "count" in data:
# specific to Open Targets GraphQL
count = data.get("count", len(v))
if len(v) > limit:
result[k] = [truncate_data(item, limit) for item in v[:limit]]
result[k].append({"_truncated": f"(Showing {limit} of {count} rows)"})
else:
result[k] = [truncate_data(item, limit) for item in v]
else:
result[k] = truncate_data(v, limit)
return result
else:
return data
def execute_query(
query: str,
variables: dict[str, Any],
output_file: str,
limit: int = 50,
page_size: int = 200,
):
"""Executes a GraphQL query against the Open Targets API.
This function sends a POST request to the Open Targets GraphQL API. It
automatically injects pagination variables if the query supports it and
pagination is not already specified. The response is checked for errors,
truncated to a manageable size, and then written to the specified output file
as JSON.
Args:
query: The GraphQL query string.
variables: A dictionary of variables to pass with the query.
output_file: The path to the file where the JSON output will be written.
limit: The maximum number of items to keep in lists within the response
(used by truncate_data). Defaults to 50.
page_size: The size of each page for API pagination. If provided and the
query supports "$page", pagination variables will be added. Defaults to
200.
"""
# Inject pagination into variables the query supports it and not already set
if "page" not in variables and page_size and "$page" in query:
variables["page"] = {"index": 0, "size": page_size}
data = _CLIENT.fetch_json(
BASE_URL,
method="POST",
json_body={"query": query, "variables": variables},
)
if "errors" in data:
print(
f"GraphQL Errors: {json.dumps(data['errors'], indent=2)}",
file=sys.stderr,
)
sys.exit(1)
truncated_data = truncate_data(data.get("data", {}), limit)
with open(output_file, "w", encoding="utf-8") as f:
f.write(json.dumps(truncated_data, indent=2))
def main():
parser = argparse.ArgumentParser(
description="Query Open Targets Platform GraphQL API"
)
parser.add_argument(
"--limit",
type=int,
default=50,
help="Limit list sizes in response (default: 50)",
)
parser.add_argument(
"--page-size",
type=int,
default=200,
help=(
"API pagination size (default: 200). Set higher if results are"
" truncated."
),
)
parser.add_argument(
"--output",
required=True,
help="Path to write the JSON output file",
)
subparsers = parser.add_subparsers(dest="command", required=True)
# get-gwas-studies
p_gwas = subparsers.add_parser(
"get-gwas-studies", help="Get GWAS studies for a specific disease EFO ID"
)
p_gwas.add_argument(
"disease_id",
help="Disease ID (EFO or MONDO, e.g., MONDO_0005011)",
)
# get-qtl-credible-sets
p_qtl = subparsers.add_parser(
"get-qtl-credible-sets",
help="Get QTL credible sets for a specific variant",
)
p_qtl.add_argument("variant_id", help="Variant ID (e.g., 19_44908822_C_T)")
# get-l2g
p_l2g = subparsers.add_parser(
"get-l2g",
help=(
"Get L2G prioritisation scores for a variant, optionally filtered by"
" study"
),
)
p_l2g.add_argument(
"variant_id",
help="Lead variant ID (e.g., 1_113834946_A_G or chr1_113834946_A_G)",
)
p_l2g.add_argument(
"--study-id",
help="Optional study ID to filter results (e.g., GCST90204201)",
default=None,
)
# get-target-druggability
p_drug = subparsers.add_parser(
"get-target-druggability",
help="Get druggability and safety info for a target",
)
p_drug.add_argument(
"ensembl_id", help="Target Ensembl ID (e.g., ENSG00000169083)"
)
# get-associated-targets
p_assoc_tgt = subparsers.add_parser(
"get-associated-targets",
help="Get targets associated with a disease EFO ID",
)
p_assoc_tgt.add_argument(
"disease_id",
help="Disease ID (EFO or MONDO, e.g., MONDO_0005005)",
)
# get-associated-diseases
p_assoc_dis = subparsers.add_parser(
"get-associated-diseases",
help="Get diseases associated with a target Ensembl ID",
)
p_assoc_dis.add_argument(
"ensembl_id", help="Target Ensembl ID (e.g., ENSG00000127318)"
)
# get-study-credible-sets
p_study_cs = subparsers.add_parser(
"get-study-credible-sets",
help="Get credible sets for a GWAS or other study by study ID",
)
p_study_cs.add_argument(
"study_id",
help="Study ID (e.g., FINNGEN_R12_RX_CROHN_2NDLINE or GCST90204201)",
)
# search-disease
p_search_dis = subparsers.add_parser(
"search-disease",
help="Search for a disease by name to find its EFO ID and other metadata",
)
p_search_dis.add_argument(
"query_string",
help="Disease name or phenotype string to search for (e.g., 'asthma')",
)
# get-credible-sets-near-target
p_near = subparsers.add_parser(
"get-credible-sets-near-target",
help="Get credible sets near target by filtering coordinates",
)
p_near.add_argument(
"ensembl_id", help="Target Ensembl ID (e.g., ENSG00000156515)"
)
p_near.add_argument(
"--window",
type=int,
default=500_000,
help="Window size in bp around the target (default: 500000)",
)
# get-disease-drugs
p_dis_drugs = subparsers.add_parser(
"get-disease-drugs",
help="Get drugs and clinical candidates for a disease",
)
p_dis_drugs.add_argument(
"disease_id",
help="Disease ID (EFO or MONDO, e.g., MONDO_0006823)",
)
p_dis_drugs.add_argument(
"--min-stage",
choices=CLINICAL_STAGES,
help="Filter to drugs at or above this clinical stage for the disease",
)
# custom-query
p_custom = subparsers.add_parser(
"custom-query", help="Execute a custom GraphQL query"
)
p_custom.add_argument("query", help="GraphQL query string")
p_custom.add_argument(
"--variables", help="JSON string of variables", default="{}"
)
args = parser.parse_args()
if args.command == "get-gwas-studies":
query = """
query getGWASStudies($efoId: String!) {
studies(diseaseIds: [$efoId]) {
count
rows {
id
projectId
traitFromSource
publicationFirstAuthor
publicationDate
publicationJournal
nSamples
cohorts
pubmedId
ldPopulationStructure { ldPopulation relativeSampleSize }
}
}
}
"""
variables = {"efoId": args.disease_id}
elif args.command == "get-qtl-credible-sets":
query = """
query getQTLCredibleSets($variantId: String!) {
variant(variantId: $variantId) {
id
qtlCredibleSets: credibleSets(
studyTypes: [scsqtl, sceqtl, scpqtl, sctuqtl, sqtl, eqtl, pqtl, tuqtl]
) {
count
rows {
studyLocusId pValueMantissa pValueExponent beta finemappingMethod confidence
variant { id chromosome }
study {
id studyType condition
target { id approvedSymbol }
biosample { biosampleId biosampleName }
}
locus(variantIds: [$variantId]) {
rows { posteriorProbability }
}
}
}
}
}
"""
variables = {"variantId": normalize_variant_id(args.variant_id)}
elif args.command == "get-l2g":
variant_id = normalize_variant_id(args.variant_id)
query = """
query getL2G($variantIds: [String!], $studyIds: [String!], $page: Pagination) {
credibleSets(variantIds: $variantIds, studyIds: $studyIds, page: $page) {
count
rows {
studyLocusId
confidence
study { id studyType traitFromSource }
variant { id }
l2GPredictions {
rows {
score
target { id approvedSymbol }
}
}
}
}
}
"""
variables = {"variantIds": [variant_id]}
if args.study_id:
variables["studyIds"] = [args.study_id]
elif args.command == "get-target-druggability":
query = """
query getTargetAnnotation($ensemblId: String!) {
target(ensemblId: $ensemblId) {
id
approvedSymbol
tractability { modality label value }
safetyLiabilities {
event eventId
biosamples { cellFormat cellLabel tissueLabel }
effects { dosing direction }
studies { name type description }
datasource
}
geneticConstraint { constraintType exp obs score oe }
}
}
"""
variables = {"ensemblId": args.ensembl_id}
elif args.command == "get-associated-targets":
query = """
query getAssociatedTargets($efoId: String!) {
disease(efoId: $efoId) {
id
name
associatedTargets {
count
rows {
target { id approvedSymbol }
score
}
}
}
}
"""
variables = {"efoId": args.disease_id}
elif args.command == "get-associated-diseases":
query = """
query getAssociatedDiseases($ensemblId: String!) {
target(ensemblId: $ensemblId) {
id
approvedSymbol
associatedDiseases {
count
rows {
disease { id name }
datasourceScores { id score }
}
}
}
}
"""
variables = {"ensemblId": args.ensembl_id}
elif args.command == "get-study-credible-sets":
query = """
query getStudyCredibleSets($studyIds: [String!], $page: Pagination) {
credibleSets(studyIds: $studyIds, page: $page) {
count
rows {
studyLocusId
confidence
finemappingMethod
pValueMantissa
pValueExponent
beta
credibleSetIndex
region
variant { id chromosome position }
study { id studyType traitFromSource }
}
}
}
"""
variables = {"studyIds": [args.study_id]}
elif args.command == "search-disease":
query = """
query searchDisease($queryString: String!, $page: Pagination) {
search(queryString: $queryString, entityNames: ["disease"], page: $page) {
hits {
id
name
description
entity
}
}
}
"""
variables = {"queryString": args.query_string}
elif args.command == "get-credible-sets-near-target":
query = """
query getTargetCredibleSets($ensemblId: String!, $page: Pagination) {
target(ensemblId: $ensemblId) {
approvedSymbol
genomicLocation { chromosome start end }
credibleSets(page: $page) {
count
rows {
studyLocusId
confidence
region
chromosome
position
variant { id chromosome position }
study { id studyType traitFromSource }
}
}
}
}
"""
variables = {"ensemblId": args.ensembl_id}
if "page" not in variables and args.page_size and "$page" in query:
variables["page"] = {"index": 0, "size": args.page_size}
data = _CLIENT.fetch_json(
BASE_URL,
method="POST",
json_body={"query": query, "variables": variables},
)
if "errors" in data:
print(
f"GraphQL Errors: {json.dumps(data['errors'], indent=2)}",
file=sys.stderr,
)
sys.exit(1)
target_data = data.get("data", {}).get("target", {})
if not target_data:
print("Error: Target not found", file=sys.stderr)
sys.exit(1)
loc = target_data.get("genomicLocation", {})
target_chrom = loc.get("chromosome")
t_start = loc.get("start")
t_end = loc.get("end")
if t_start is None or t_end is None:
print(
"Warning: Target location not found, cannot filter by region.",
file=sys.stderr,
)
min_pos = 0
max_pos = sys.maxsize
else:
window = args.window
min_pos = max(0, t_start - window)
max_pos = t_end + window
cs_data = target_data.get("credibleSets", {})
rows = cs_data.get("rows", [])
filtered_rows = []
for row in rows:
v_pos = row.get("position") or row.get("variant", {}).get("position")
v_chrom = row.get("chromosome") or row.get("variant", {}).get(
"chromosome"
)
if (
v_pos is not None
and v_chrom == target_chrom
and min_pos <= v_pos <= max_pos
):
filtered_rows.append(row)
# Update data structure
cs_data["total_count"] = cs_data.get("count")
cs_data["rows"] = filtered_rows
cs_data["filtered_count"] = len(filtered_rows)
truncated_data = truncate_data(data.get("data", {}), args.limit)
with open(args.output, "w", encoding="utf-8") as f:
f.write(json.dumps(truncated_data, indent=2))
sys.exit(0)
elif args.command == "get-disease-drugs":
query = """
query getDiseaseDrugs($efoId: String!) {
disease(efoId: $efoId) {
id
name
drugAndClinicalCandidates {
count
rows {
maxClinicalStage
drug {
id
name
drugType
maximumClinicalStage
}
}
}
}
}
"""
variables = {"efoId": args.disease_id}
data = _CLIENT.fetch_json(
BASE_URL,
method="POST",
json_body={"query": query, "variables": variables},
)
if "errors" in data:
print(
f"GraphQL Errors: {json.dumps(data['errors'], indent=2)}",
file=sys.stderr,
)
sys.exit(1)
disease_data = data.get("data", {}).get("disease")
if disease_data and args.min_stage:
candidates = disease_data.get("drugAndClinicalCandidates", {})
rows = candidates.get("rows", [])
filtered_rows = []
min_stage_val = STAGE_ORDER.get(args.min_stage)
for row in rows:
stage = row.get("maxClinicalStage")
stage_val = STAGE_ORDER.get(stage)
if stage_val is not None and stage_val >= min_stage_val:
filtered_rows.append(row)
candidates["total_count"] = candidates.get("count")
candidates["rows"] = filtered_rows
candidates["filtered_count"] = len(filtered_rows)
candidates["count"] = len(filtered_rows)
truncated_data = truncate_data(data.get("data", {}), args.limit)
with open(args.output, "w", encoding="utf-8") as f:
f.write(json.dumps(truncated_data, indent=2))
sys.exit(0)
elif args.command == "custom-query":
query = args.query
try:
variables = json.loads(args.variables)
except json.JSONDecodeError:
print("Error: --variables must be a valid JSON string", file=sys.stderr)
sys.exit(1)
else:
available_commands = ", ".join(subparsers.choices.keys())
print(
f"Error: Unknown command: {args.command}. Available commands:"
f" {available_commands}",
file=sys.stderr,
)
sys.exit(1)
execute_query(query, variables, args.output, args.limit, args.page_size)
if __name__ == "__main__":
main()
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Low risk
- Scripts that your agent may run
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Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/opentargets_database · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Google DeepMind.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download opentargets-database.zip