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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
89 lines
3.7 KiB
Markdown
89 lines
3.7 KiB
Markdown
# CLAUDE.md — Dealix Project Context for AI Agents
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## Quick Context
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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.
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## Key Directories
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- `backend/app/api/v1/` — API routes (FastAPI)
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- `backend/app/models/` — SQLAlchemy models
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- `backend/app/services/` — Business logic layer
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- `backend/app/services/ai/` — AI engine (Arabic NLP, scoring, forecasting)
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- `backend/app/services/pdpl/` — PDPL compliance engine
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- `backend/app/services/cpq/` — Configure, Price, Quote
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- `backend/app/services/agents/` — Multi-agent orchestration
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- `backend/app/services/llm/` — LLM provider abstraction
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- `backend/app/workers/` — Celery async tasks
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- `backend/app/integrations/` — WhatsApp, Email, SMS adapters
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- `frontend/src/app/` — Next.js pages
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- `seeds/` — Industry templates (JSON)
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- `memory/` — Project knowledge base
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## Database
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- PostgreSQL 16 with async driver (asyncpg)
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- Multi-tenant: every table has `tenant_id`
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- Alembic for migrations
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- Money fields use `Numeric` type (never Float)
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## AI Architecture
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- Provider abstraction: Groq → OpenAI fallback
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- Model router: task-specific model selection
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- Arabic NLP: intent, sentiment, entity extraction
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- Lead scoring: 0-100 composite score
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- Conversation intelligence: Arabic dialogue analysis
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- Sales agent: autonomous WhatsApp qualification bot
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## PDPL Compliance (Critical)
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- Check consent before ANY outbound message
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- Track consent purpose, channel, timestamp
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- Support data subject rights (access, correct, delete)
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- Audit trail for all consent changes
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- Auto-expire consent after 12 months
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- Penalty: up to SAR 5 million per violation
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## Testing
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```bash
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pytest -v # All tests
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pytest tests/test_ai/ -v # AI engine tests
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pytest tests/test_pdpl/ -v # PDPL compliance tests
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pytest tests/test_api/ -v # API endpoint tests
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```
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## Common Tasks
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- Add new API endpoint: create route in `api/v1/`, register in `main.py`
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- Add new model: create in `models/`, add to `models/__init__.py`, create migration
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- Add new AI feature: create in `services/ai/`, wire to relevant API/worker
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- Add industry template: create JSON in `seeds/`, match existing schema
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## gstack Planning Discipline
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Before writing code, classify your task:
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| Tier | When | What to do |
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|------|------|-----------|
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| **SIMPLE** | 1 file, obvious change | Just do it |
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| **MEDIUM** | Multi-file, needs thought | Read files → 5-line plan → resolve ambiguity → self-review → report |
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| **HEAVY** | Complex, needs specific skill | Load skill → execute workflow → verify → report |
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| **FULL** | End-to-end feature/release | Plan → review → implement → test → ship → report |
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| **PLAN** | Research/architecture only | Plan only, save to `memory/`, no implementation |
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**RULE**: Append to this file, never replace existing instructions.
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## Hermes Profiles
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| Profile | Mission | Scope |
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|---------|---------|-------|
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| `growth` | Customer acquisition | leads, messaging, analytics, content |
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| `sales` | Deal closing | deals, proposals, sequences, WhatsApp |
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| `security` | Platform protection | compliance, audit, Shannon scans |
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| `ops` | Deployment & reliability | workers, monitoring, releases |
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| `knowledge` | Wiki & memory management | brain, wiki, indexes |
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| `founder` | Strategic decisions | everything (highest permissions) |
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| `arabic-ops` | Arabic content & dialect | summarization, dialect detection, RTL |
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## Arabic Operations
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- Use `arabic_ops.py` for: call notes compression, market research digests, executive briefs
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- Always detect dialect before processing (saudi/gulf/msa)
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- Check for Arabizi and suggest Arabic conversion
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- Check code-switching (Arabic+English mixed) for readability
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