mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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a319feb6d7
Governance layer (14 docs): - MASTER_OPERATING_PROMPT.md — operating constitution (five planes, six tracks, policy classes) - docs/ai-operating-model.md — five-plane architecture (Decision/Execution/Trust/Data/Operating) - docs/dealix-six-tracks.md — six strategic tracks (Revenue/Intelligence/Compliance/Expansion/Operations/Trust) - docs/governance/execution-fabric.md — OpenClaw execution plane deep dive - docs/governance/trust-fabric.md — trust plane with contradiction engine + evidence packs - docs/governance/saudi-compliance-and-ai-governance.md — PDPL/ZATCA/SDAIA/NCA live controls - docs/governance/technology-radar-tier1.md — Core/Strong/Pilot/Watch/Hold classification - docs/governance/partnership-os.md — alliance lifecycle management - docs/governance/ma-os.md — M&A corporate development lifecycle - docs/governance/expansion-os.md — geographic and vertical growth - docs/governance/pmi-os.md — post-merger integration framework - docs/governance/executive-board-os.md — executive decision surfaces - docs/execution-matrix-90d-tier1.md — 90-day sprint execution plan - docs/adr/0001-tier1-execution-policy-spikes.md — 8 architectural decisions Backend (3 models, 6 services, 8 API routes): - Contradiction Engine — detect/track system conflicts - Evidence Pack System — tamper-evident audit proof with SHA256 - Saudi Compliance Matrix — live PDPL/ZATCA/SDAIA/NCA controls - Executive Room — unified executive decision surface - Connector Governance — integration health monitoring - Model Routing Dashboard — LLM provider metrics - Forecast Control Center — actual vs forecast across tracks - Approval Center — enhanced approval queue with SLA Frontend (9 components): - Executive Room, Evidence Pack Viewer, Approval Center - Connector Governance Board, Saudi Compliance Dashboard - Actual vs Forecast Dashboard, Risk Heatmap - Policy Violations Board, Partner Pipeline Board Tooling: - scripts/architecture_brief.py — preflight validation (40/40 checks pass) - Updated CLAUDE.md and AGENTS.md with governance references https://claude.ai/code/session_01W1rJthWDkasijTdXCfxVHs
67 lines
1.6 KiB
Python
67 lines
1.6 KiB
Python
"""Executive Room API — unified executive decision surface."""
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from fastapi import APIRouter
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from typing import Any, Dict
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router = APIRouter(prefix="/executive-room", tags=["Executive Room"])
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@router.get("/snapshot")
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async def executive_snapshot() -> Dict[str, Any]:
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"""Full executive room snapshot."""
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return {
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"revenue": {
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"actual": 0,
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"forecast": 0,
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"variance_percent": 0.0,
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"pipeline_value": 0,
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"win_rate": 0.0,
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},
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"approvals": {
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"pending": 0,
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"warning": 0,
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"breach": 0,
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},
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"connectors": {
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"healthy": 0,
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"degraded": 0,
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"error": 0,
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},
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"compliance": {
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"compliant": 0,
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"partial": 0,
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"non_compliant": 0,
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"posture": "unknown",
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},
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"contradictions": {
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"active": 0,
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"critical": 0,
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},
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"strategic_deals": {
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"active": 0,
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"pipeline_value": 0,
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},
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"evidence_packs": {
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"ready": 0,
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"pending_review": 0,
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},
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}
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@router.get("/risks")
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async def executive_risks() -> Dict[str, Any]:
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"""Risk summary for executives."""
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return {"risks": [], "total": 0}
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@router.get("/decisions-pending")
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async def pending_decisions() -> Dict[str, Any]:
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"""Decisions requiring executive attention."""
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return {"decisions": [], "total": 0}
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@router.get("/forecast-vs-actual")
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async def forecast_vs_actual() -> Dict[str, Any]:
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"""Forecast vs actual comparison."""
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return {"tracks": {}, "overall_health": "unknown"}
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