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Pivot YouTube RAG scaffold into Amana — T&S triage copilot (Phases 1–3.6)
Repurposes the YouTube-transcript RAG app into a Trust & Safety campaign-review triage copilot: an AI agent screens a fundraising campaign against policy and recommends APPROVE / REJECT / ESCALATE with cited rule IDs; a human moderator decides. The AI never auto-decides. Data layer: - data/policy.md (26 citable rule IDs: ELIG/PROH/COMP/CONT/DEC) - data/campaigns/*.json (18 synthetic cases w/ private _design_note/_expected, stripped before the agent sees them), data/sanctions.json, data/past_cases.json Agent + tools (Pydantic AI, Claude; Ollama toggle for free local dev): - src/schemas.py, src/campaigns.py, src/policy.py, src/tools.py, src/agent.py - four tools: policy_search, similar_cases, check_sanctions (mock), scan_risk_signals - retired src/ingest.py, src/chunk.py, src/rag.py Deterministic policy gate (the "not-a-wrapper" layer): - src/gate.py reconciles the model's decision against policy invariants in code; may only route toward the human (-> ESCALATE), never auto-approve…250666a