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system-prompts-and-models-o.../salesflow-saas/CLAUDE.md
Claude d7d428d0a1
feat: Add gstack discipline, skill governance, Arabic ops layer 
Final integration layer (gstack + Antigravity + Mukhtasar/Mkhlab):

- gstack_discipline.py: Planning enforcement with dispatch tiers
  (Simple/Medium/Heavy/Full/Plan), plan validation, lite/full prompts
- skill_governance.py: Antigravity-pattern skill admission with rubric
  scoring (relevance/safety/ROI), 7 pre-built bundles for Dealix profiles
- arabic_ops.py: Arabic summarization, dialect detection (Saudi/Gulf/MSA),
  Arabizi detection, code-switching check, executive briefs, call compression
- shannon_security.py: Enhanced with verified findings and detailed PoC
- CLAUDE.md: Appended gstack tiers, Hermes profiles, Arabic ops guide

https://claude.ai/code/session_01LsnvBa7HwF5hs99VZbgLGj
2026-04-11 08:33:58 +00:00

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CLAUDE.md — Dealix Project Context for AI Agents

Quick Context

Dealix is an AI-powered CRM built for the Saudi market. It combines Salesforce-grade AI with WhatsApp-first communication, PDPL compliance, and Arabic-first UX.

Key Directories

  • backend/app/api/v1/ — API routes (FastAPI)
  • backend/app/models/ — SQLAlchemy models
  • backend/app/services/ — Business logic layer
  • backend/app/services/ai/ — AI engine (Arabic NLP, scoring, forecasting)
  • backend/app/services/pdpl/ — PDPL compliance engine
  • backend/app/services/cpq/ — Configure, Price, Quote
  • backend/app/services/agents/ — Multi-agent orchestration
  • backend/app/services/llm/ — LLM provider abstraction
  • backend/app/workers/ — Celery async tasks
  • backend/app/integrations/ — WhatsApp, Email, SMS adapters
  • frontend/src/app/ — Next.js pages
  • seeds/ — Industry templates (JSON)
  • memory/ — Project knowledge base

Database

  • PostgreSQL 16 with async driver (asyncpg)
  • Multi-tenant: every table has tenant_id
  • Alembic for migrations
  • Money fields use Numeric type (never Float)

AI Architecture

  • Provider abstraction: Groq → OpenAI fallback
  • Model router: task-specific model selection
  • Arabic NLP: intent, sentiment, entity extraction
  • Lead scoring: 0-100 composite score
  • Conversation intelligence: Arabic dialogue analysis
  • Sales agent: autonomous WhatsApp qualification bot

PDPL Compliance (Critical)

  • Check consent before ANY outbound message
  • Track consent purpose, channel, timestamp
  • Support data subject rights (access, correct, delete)
  • Audit trail for all consent changes
  • Auto-expire consent after 12 months
  • Penalty: up to SAR 5 million per violation

Testing

pytest -v                           # All tests
pytest tests/test_ai/ -v            # AI engine tests
pytest tests/test_pdpl/ -v          # PDPL compliance tests
pytest tests/test_api/ -v           # API endpoint tests

Common Tasks

  • Add new API endpoint: create route in api/v1/, register in main.py
  • Add new model: create in models/, add to models/__init__.py, create migration
  • Add new AI feature: create in services/ai/, wire to relevant API/worker
  • Add industry template: create JSON in seeds/, match existing schema

gstack Planning Discipline

Before writing code, classify your task:

Tier When What to do
SIMPLE 1 file, obvious change Just do it
MEDIUM Multi-file, needs thought Read files → 5-line plan → resolve ambiguity → self-review → report
HEAVY Complex, needs specific skill Load skill → execute workflow → verify → report
FULL End-to-end feature/release Plan → review → implement → test → ship → report
PLAN Research/architecture only Plan only, save to memory/, no implementation

RULE: Append to this file, never replace existing instructions.

Hermes Profiles

Profile Mission Scope
growth Customer acquisition leads, messaging, analytics, content
sales Deal closing deals, proposals, sequences, WhatsApp
security Platform protection compliance, audit, Shannon scans
ops Deployment & reliability workers, monitoring, releases
knowledge Wiki & memory management brain, wiki, indexes
founder Strategic decisions everything (highest permissions)
arabic-ops Arabic content & dialect summarization, dialect detection, RTL

Arabic Operations

  • Use arabic_ops.py for: call notes compression, market research digests, executive briefs
  • Always detect dialect before processing (saudi/gulf/msa)
  • Check for Arabizi and suggest Arabic conversion
  • Check code-switching (Arabic+English mixed) for readability