القائمة
Expert guide for developing the Precision Medicine Streamlit dashboard and LLM providers. Covers provider abstraction, trace visualization, and UI components.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Audits all Markdown files in the precision-medicine-mcp repo against four canonical principles: (A) server Python code is the authoritative tool count, (B) server-registry.md is the authoritative server/tool summary, (C) all three patient outcomes (PAT001/PAT002/PAT003) must be represented consistently, (D) no content block may appear in more than one MD file (DRY). Use this skill whenever a new version of the platform ships, a new server is added, a new patient use case is validated, or before any major doc update. Trigger phrases: "audit the docs", "check docs are correct", "doc cleanup", "canonical check", "are the docs up to date", "DRY check on markdown".
Ensures all development meets clinical compliance and security standards. Focuses on HIPAA-friendly logging, audit trails, risk assessment, and scientific nomenclature.
Guide for managing patient data and bioinformatics datasets (h5ad, CSV). Standardizes data loading, preprocessing, and path management.
Expert guide for project-specific documentation and clinical reporting. Maintains consistency across persona-based guides and automated reports.
Expert guide for GCP deployment, security, and hospital-specific infrastructure. Covers Cloud Run, IAM, VPC, and secrets management for bioinformatics.
Expert guide for testing bioinformatics MCP servers. Covers pytest, DRY_RUN modes, and PatientOne simulation scenarios.
| name | precision-bio-ui |
| description | Expert guide for developing the Precision Medicine Streamlit dashboard and LLM providers. Covers provider abstraction, trace visualization, and UI components. |
This skill guides the development of the frontend and LLM orchestration layer of the platform.
The UI is built with Streamlit and follows a modular provider-based architecture:
ui/streamlit-app/app.py: Main entry point.ui/streamlit-app/providers/: Abstracted LLM orchestration (Claude, Gemini).ui/streamlit-app/utils/: Visualization, GCS handling, and trace logic.When adding or modifying LLM providers:
providers/base.py.generate_response_stream for a better user experience.trace_utils.py to ensure tool calls are correctly recorded for the orchestration trace.st.dataframe for large genomic tables and st.image for GCS-hosted UMAPs/PCA plots.st.expander for advanced settings..env or the sidebar (e.g., USE_MOCK_MCP).utils/mcp_config.py.Use this skill when: