py-review-orchestrator

name: py-review-orchestrator description: Execute BioETL hierarchical code review orchestration (L1/L2/L3) across sectors S1-S8 with delegated sub-reviews, scoring, and consolidated reporting in reports/{LLM}/review_py-review-orchestrator_{YYYYMMDD}_{HHMM}_FINAL.md. Use when a full-project audit or broad multi-layer review is requested.

py-review-orchestrator

Objective

Run the role-specific workflow as defined in the py-review-orchestrator profile.

Source Of Truth

• Canonical runtime entrypoint: this SKILL.md
• Team orchestration: ../../../.codex/agents/ORCHESTRATION.md
• Memory policy: ../../../docs/00-project/ai/agents/guides/MEMORY_USAGE.md
• Shared project context: ../../../docs/00-project/ai/memory/agent-memory.md
• Role-specific memory: ../../../docs/00-project/ai/memory/memory-py-review-orchestrator.md

Workflow

1. Start with the canonical memory loop from ../../../src/memory/DAILY_WORKFLOW.md and run python -m memory.tooling.workflow pre-task ... for the review task.
2. Read MEMORY_USAGE.md, agent-memory.md, and memory-py-review-orchestrator.md; then add delegated role-memory sheets for sector-specific reviewers.
3. Treat this skill file as the canonical Codex runtime profile for the workflow.
4. Execute hierarchical review orchestration (Wave 1, then Wave 2) and respect sector dependencies.
5. Aggregate sector reports into reports/{LLM}/review_py-review-orchestrator_{YYYYMMDD}_{HHMM}_FINAL.md (LLM = caller) with complete critical/high issue rollup.
6. Keep scoring and status thresholds aligned with the profile and BioETL rules.
7. After the review, run python -m memory.tooling.workflow post-task ... and promote only durable review knowledge.