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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
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
"""Forecast Control Center — unified actual vs forecast across all tracks."""
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from __future__ import annotations
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from typing import Any, Dict
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class ForecastControlCenter:
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"""Provides unified actual vs forecast view across revenue, partnerships, M&A, expansion."""
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def get_unified_view(self, tenant_id: str) -> Dict[str, Any]:
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return {
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"tenant_id": tenant_id,
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"tracks": {
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"revenue": {
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"actual": 0,
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"forecast": 0,
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"variance": 0,
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"variance_percent": 0.0,
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"unit": "SAR",
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},
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"partnerships": {
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"actual_count": 0,
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"target_count": 0,
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"variance": 0,
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"unit": "partners",
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},
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"ma": {
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"deals_in_progress": 0,
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"pipeline_target": 0,
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"variance": 0,
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"unit": "deals",
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},
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"expansion": {
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"markets_launched": 0,
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"markets_planned": 0,
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"variance": 0,
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"unit": "markets",
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},
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},
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"overall_health": "on_track",
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}
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def get_variance_analysis(self, tenant_id: str) -> Dict[str, Any]:
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return {
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"tenant_id": tenant_id,
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"top_variances": [],
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"root_causes": [],
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"recommendations": [],
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}
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def get_accuracy_trend(self, tenant_id: str, periods: int = 6) -> Dict[str, Any]:
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return {
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"tenant_id": tenant_id,
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"periods": periods,
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"trend": [],
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"average_accuracy_percent": 0.0,
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}
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forecast_control_center = ForecastControlCenter()
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