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Reviews code changes using CodeRabbit AI. Use when user asks for code review, PR feedback, code quality checks, security issues, or wants autonomous fix-review cycles.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Entry-point live-market scan. Returns current quote, H1/H4 regime, session state, and watch levels for a default watchlist (gold, US30, US500, US100, EURUSD, GBPUSD, BTCUSD, ETHUSD) — or any custom list. Uses the free yfinance path by default, MT5 if available. USE FOR - scan markets, what's moving, market overview, current prices, what's on the watchlist, show me the markets.
Entry-point ranked-setup feed. Runs the full pair-analyze pipeline against a watchlist and returns the top N highest-grade trade setups right now, sorted by confidence × R:R, with one-line rationale and a copy-pasteable trade plan. USE FOR - what should I trade, recommend trades, best setups, top picks, ranked opportunities, where's the edge.
Entry-point skill discovery. Given a keyword or task description, returns the shortest list of Tradecraft skills that match, with direct invocation recipes. No keyword = show the 5 main entry points. USE FOR - find skill, which skill, how do I, search skills, list skills, what can this do, find command.
Entry-point strategy selector. Lists the available trading strategies with a one-line when-to-use for each, plus a deep-dive drill-down by name. Routes to the right specialist skill (ICT/SMC, breakout, scalping, swing, trend-following, mean-reversion). USE FOR - which strategy, what strategies, strategy selector, strategy list, how should I trade, pick a strategy, ICT or SMC, breakout or trend.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Agentic storage architecture — persistent memory for AI agents using MCP, immutable versioning, sandboxing, and intent validation. Covers the stateless problem in LLM agents, RAG limitations (read-only), MCP protocol (JSON-RPC, resources, tools), storage safety layers, and agent file system design. Source: IBM Technology (Martin Keen), March 2026. Use this skill for "agentic storage", "agent memory", "persistent agent state", "MCP storage", "agent file system", "agent hard drive", "stateless agent problem", "agent work product", "immutable versioning", "agent sandboxing", "intent validation", "agent safety layers", "agent context window limits", "storage for AI agents", "agent persistence", "autonomous agent storage", "agent audit trail". Works with mcp-integration, trading-brain, agents, trade-psychology-coach.
| name | code-review |
| description | Reviews code changes using CodeRabbit AI. Use when user asks for code review, PR feedback, code quality checks, security issues, or wants autonomous fix-review cycles. |
| kind | workflow |
| category | dev/quality |
| status | active |
| tags | ["code","dev","quality","review"] |
AI-powered code review using CodeRabbit. Enables autonomous development workflows where you can implement features, review code, and fix issues without manual intervention.
When user asks to:
# Check CLI
coderabbit --version 2>/dev/null
# Check auth
coderabbit auth status 2>&1
If CLI not installed, tell user:
Please install CodeRabbit CLI first:
curl -fsSL https://cli.coderabbit.ai/install.sh | sh
If not authenticated, tell user:
Please authenticate first by running in your terminal:
coderabbit auth login
coderabbit review --plain
Options:
-t all - All changes (default)-t committed - Committed changes only-t uncommitted - Uncommitted changes only--base main - Compare against specific branchGroup findings by severity and create a task list for issues found.
When user requests implementation + review:
coderabbit review --plainFor more details: https://docs.coderabbit.ai/cli/claude-code-integration