القائمة
Project onboarding and orientation. Use when first exploring the codebase, setting up the development environment, or understanding the project architecture.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
DEEP multi-step research harness. Decomposes queries into sub-questions, executes parallel web searches via Pieces MCP, iteratively refines with gap detection, and synthesizes cited reports. Architecture inspired by GPT Researcher (27.6k stars), Vane/Perplexica (35.2k stars), and Gemini Deep Research. Use when user asks for deep research, investigation, analysis, comparison, or any task requiring exhaustive web research.
DEFAULT THINKING MODE — not a feature, but how the agent operates. Every task gets innovation-overdrive thinking: research, ground, reflect on contradictions, propose, then build. This is the agent's identity, not a command. Integrates MiMo-Code's 6-phase memory consolidation.
Chain execution manager: validates dependencies, enforces order, tracks violations. Use to verify that all required skill chains were executed and to get the correct execution order.
API design patterns, REST conventions, and endpoint standards. Use when creating or modifying API endpoints. Covers routing, request/response patterns, error handling, and versioning.
Architectural design, system patterns, and dependency management. Use when designing new modules, refactoring existing systems, or making architectural decisions. Covers layering, coupling, cohesion, and design patterns.
Trigger-driven automation definitions and runner management. Use when creating, running, or managing skill-based automation pipelines.
| name | onboarding |
| description | Project onboarding and orientation. Use when first exploring the codebase, setting up the development environment, or understanding the project architecture. |
| chains_with | ["documentation","automated-learning"] |
When entering a new session on this project:
# Read the top-level project files
head -50 README.md 2>/dev/null
head -100 AGENTS.md 2>/dev/null
# Understand the directory structure
ls -la src/ 2>/dev/null
git log --oneline -5
git status
git branch
Project-Q is a quantum-classical hybrid risk engine:
Data Pipeline → GARCH → PIT → Copula → MC Simulation → Risk Metrics → Backtesting
↓
Quantum POC (QAE/QAOA)
| File | Purpose |
|---|---|
src/project_q/dashboard/api_server.py | Flask API server (all endpoints) |
src/project_q/quantum/ | Quantum primitives |
src/project_q/copula/ | Vine Copula models |
src/project_q/volatility/ | GARCH models |
src/project_q/simulation/ | Monte Carlo engine |
frontend/src/ | React frontend |
tests/ | Test suite |
outputs/ | Generated artifacts |
docker-compose.yml | Docker setup |
# Build
pip install -e ".[dev]"
# Run API server
python -m src.project_q.dashboard.api_server
# Run tests
pytest tests/ -x -q
# Lint
ruff check src/
# Type check
mypy src/
# Docker
docker compose up -d
Use Pieces LTM for historical context:
pieces_search_memory — past decisions, discussionspieces_workstream_events_full_text_search — recent code workpieces_annotations_full_text_search — summaries, notessrc/project_q/
dashboard/ # API server + routes
api_server.py # Main Flask app (2492 lines)
risk/ # Risk computation
metrics.py # VaR/ES calculation
risk_engine.py # Scenario to risk mapping
copula/ # Vine copula
fitting.py # Copula fitting
simulation.py # Copula sampling
regimes.py # Regime-conditioned copula
volatility/ # GARCH
garch_estimator.py # GARCH model fitting
regime_detection.py # Volatility regime detection
quantum/ # Quantum POC
circuits.py # Quantum circuit definitions
models/risk/ # QAE estimators
simulation/ # Monte Carlo
monte_carlo.py # MC engine
performance_optimiser.py # Bottleneck analysis