Agent Skills
› openai/plugins
› opentargets-skill
opentargets-skill
GitHub封装Open Targets Platform的GraphQL查询工具，支持靶点、疾病、药物等数据检索及关联疾病证据矩阵生成。通过脚本执行查询，返回精简摘要或原始JSON，适用于需要快速生物医学证据上下文和汇总的场景。
Trigger Scenarios
用户请求获取特定靶点或疾病的证据上下文
用户需要查看疾病与数据源的关联热力图或气泡网格
用户要求提供简洁的生物医学数据摘要
Install
npx skills add openai/plugins --skill opentargets-skill -g -y
SKILL.md
Frontmatter
{
"name": "opentargets-skill",
"description": "Submit compact Open Targets Platform GraphQL requests for target, disease, drug, variant, study, and search data, including associated-disease datasource heatmap matrices. Use when a user wants concise Open Targets summaries or per-datasource evidence context"
}
Operating rules
- Use
scripts/opentargets_graphql.pyfor all Open Targets GraphQL work. - Use
scripts/opentargets_disease_heatmap.pywhen the user wants the associated-disease bubble grid or a disease-by-datasource evidence matrix. - The script accepts
max_items; for nested GraphQL results, start withmax_items=3to5. - Keep GraphQL selection sets narrow and page connection-style fields conservatively.
- Use
query_pathfor long GraphQL documents instead of pasting large inline query strings. - Re-run requests in long conversations instead of relying on earlier tool output.
- Treat displayed
...in tool previews as UI truncation, not part of the real query.
Execution behavior
- Return concise markdown summaries from the script JSON by default.
- Return the JSON verbatim only if the user explicitly asks for machine-readable output.
- Prefer targeted GraphQL queries that select only the fields needed for the user task.
- Use schema introspection only when necessary; do not dump large schema payloads into chat.
- For the associated-disease heatmap, treat
datasourceScoresas evidence-source breadth/context. Do not treat heatmap breadth alone as proof of causal target assignment, mechanism, or direction of effect.
Input
- Read one JSON object from stdin.
- Required field:
queryorquery_path - Optional fields:
variables,max_items,max_depth,timeout_sec,save_raw,raw_output_path - Common Open Targets patterns:
{"query":"query { __typename }"}{"query":"query searchAny($q: String!) { search(queryString: $q) { total hits { entity score object { ... on Target { id approvedSymbol } } } } }","variables":{"q":"MST1"},"max_items":3}
Output
- Success returns
ok,source,top_keys, a compactsummary, andraw_output_pathwhensave_raw=true. - Failure returns
ok=falsewitherror.codesuch asinvalid_json,invalid_input,network_error,invalid_response, orgraphql_error.
Execution
echo '{"query":"query { __typename }"}' | python scripts/opentargets_graphql.py
Associated-disease heatmap helper:
echo '{
"ensembl_id":"ENSG00000186868",
"page_size":50,
"max_pages":4,
"disease_name_filter":"alzh"
}' | python scripts/opentargets_disease_heatmap.py
The helper paginates associatedDiseases, collects datasourceScores, and returns:
matrix.columns: datasource IDs plus display labelsmatrix.rows: diseases withdatasource_scoressummary.rows_preview: top datasource signals per disease
Use the disease-name filter as a client-side substring filter similar to the UI. If you later need the overall association score column, inspect the GraphQL row type first before adding candidate fields such as score or associationScore.
References
- No additional runtime references are required; keep the import package limited to this file and the bundled scripts in
scripts/.
Version History
- 11c74d6 Current 2026-07-19 09:39
