CC — Capability SKILL Sheet

Defines the scope of the Monkeytype API, including its resources, representations, and ORM models.

Updated Feb 23, 2026
One-click install
npx skills add https://github.com/cheafi/Trading-bot-CC --skill cc-capability-skill-sheet
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: CC — Capability SKILL Sheet
Source: https://github.com/cheafi/Trading-bot-CC/tree/main/docs
Command: npx skills add https://github.com/cheafi/Trading-bot-CC --skill cc-capability-skill-sheet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill sheet prevents confusion by clearly defining CC’s capabilities, data sources, constraints, and the kinds of outputs users should expect versus avoid.

Core Features & Use Cases

  • Capability Transparency: Lists what CC can deliver across regime, signals, recommendations, performance, portfolio briefs, compare/strategy labs, options screens, macro intel, and research artifacts.
  • Data & Architecture Clarity: Documents which internal services and providers are used (MarketDataService, AutoTradingEngine singleton, ExpressionEngine, synthetic options provider fallback, yfinance upstream via MarketDataService).
  • Safety & Non-Goals: Explicitly states what CC does not do (e.g., no financial advice, no live trading by default, no real-time options chain guarantees, no tick-data backtesting).
  • External Contract Reference: Points to the stable read-only /api/market-intel/* contract for assistant/bot consumption.

Quick Start

Use the CC — Capability SKILL Sheet to decide whether a specific task you want (like “market regime intel” or “performance lab auditable metrics”) is within scope before you request it from the platform.

Frequently Asked Questions about CC — Capability SKILL Sheet

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What capabilities does the CC trading bot platform support for market regime detection?

The CC trading bot platform supports market regime detection, signal and recommendation discovery, auditable performance briefs, portfolio analysis, and research artifact usage. It clarifies supported data sources and explicit constraints, ensuring outputs are research and informational rather than financial advice.

Can I use the CC trading bot platform for live trading execution and real-time options chains?

The CC trading bot platform does not support live trading by default, real-time options chain guarantees, or tick-data backtesting. It uses a synthetic options provider fallback and explicitly bounds trading execution guarantees to ensure users understand these safety constraints.

How do I consume external market intelligence data from the CC platform?

You consume external market intelligence data via the stable read-only `/api/market-intel/*` API contract. This external contract reference is designed for assistant and bot consumption, providing structured market intel outputs derived from internal services like MarketDataService and yfinance upstream.

What data sources and internal services power the CC trading bot platform?

The CC trading bot platform uses MarketDataService, AutoTradingEngine singleton, ExpressionEngine, and a synthetic options provider fallback, with yfinance serving as the upstream data provider. This architecture clarity helps users understand exactly which services generate the platform's research artifacts and performance briefs.

Does the CC trading bot platform provide financial advice or trading signals?

The CC trading bot platform does not provide financial advice; it enforces transparency constraints labeling all outputs as research and informational. Signal and recommendation discovery features are bounded by explicit non-goals to prevent users from interpreting analytical outputs as direct financial guidance.

When should I check the CC capability scope before requesting performance lab metrics?

You should check the CC capability scope before requesting performance lab metrics to verify whether auditable performance briefs, compare and strategy labs, or macro intel are within the supported platform boundaries. This scope clarification prevents confusion by defining expected outputs versus explicit non-goals.