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Found 13,655 Skills
Documentation and commit specialist. Runs after ralph subagents complete a Priority group. Reviews RALPH_DONE signals, updates progress.md and PRD task checkboxes, and makes one atomic git commit per completed user story. Also writes an implementation summary when the full PRD is done. Use after ralph subagents finish implementing — never during active development.
Monitor running agent loops, triage failures, clean up after completion, and decide when to intervene. Use when a loop is running and needs babysitting, when a loop just finished and needs post-merge verification, when stories are skipping/failing and need diagnosis, or when stale test artifacts need cleanup. Triggers on: 'check the loop', 'what happened with the loop', 'loop finished', 'clean up after loop', 'why did that story skip', 'monitor loop', 'nanny the loop', or any post-start loop management task. Distinct from agent-loop skill (which handles starting loops).
Lightweight session memory CLI for agent workflows. Provides persistent journal, artifact, task, state, idea, and debug tracking across sessions and conversation compaction.
Play blackjack with the agent as dealer. The agent manages game state, deals cards, and sends card images.
Use when asked to trace existing codepaths or explicitly asked to run the code-explorer subagent.
Maintain /do routing tables and command references when skills or agents are added, modified, or removed. Use when skill/agent metadata changes, after skill-creator-engineer or agent-creator-engineer runs, or when routing tables need synchronization. Use for "update routes", "sync routing", "routing table", or "refresh /do". Do NOT use for creating new skills/agents, modifying skill logic, or manual /do table edits.
Use this skill when you need to operate the Creem CLI for authentication checks, products, customers, checkouts, subscriptions, transactions, configuration, monitoring, or terminal automation workflows. Prefer it for agent-driven Creem tasks that should use real CLI commands and JSON output instead of dashboard clicks or guessed API calls.
Deploy OpenClaw AI agent platform on Alibaba Cloud ECS and integrate with DingTalk bot. OpenClaw (formerly Clawdbot/Moltbot, 中文名"龙虾") is an open-source AI assistant and automation platform supporting natural language-driven task automation with multi-channel chat integration. This Skill covers the full workflow from ECS instance creation, public network configuration, base environment setup, one-click OpenClaw deployment to DingTalk bot verification. End users can chat with the AI assistant by @mentioning the bot in a DingTalk group. Triggers: "OpenClaw", "龙虾", "Clawdbot", "Moltbot", "DingTalk bot", "DingTalk AI", "deploy OpenClaw on ECS", "AI agent platform", "DingTalk integration", "openclaw dingtalk", "openclaw deploy", "DingTalk AI employee", "Alibaba Cloud OpenClaw", "Bailian + DingTalk", "DingTalk group AI", "DingTalk smart assistant", "部署龙虾", "龙虾机器人", "龙虾钉钉"
Annie Duke's Decision Quality framework applied to a business decision. Spawns a team of specialist agents — Resulting Auditor, Calibrator, Pre-Mortem Analyst, Quit Strategist, Process Architect — who each apply a distinct lens from Duke's framework to evaluate whether a decision is sound regardless of outcome. The lead synthesizes into a stacking analysis: which biases are operating, which process flaws exist, and the honest Duke verdict. Use when the user says "duke this", "is this a good bet", "should I quit", "evaluate this decision", or faces any high-stakes choice under uncertainty and wants rigorous decision-process analysis. Works as a standalone analysis or after /office-hours.
Use the unified Opper SDKs (`opperai` package for both Python and TypeScript, with built-in agent support) for AI task completion, structured output with Pydantic / Zod / JSON Schema, knowledge base semantic search, streaming, tracing, tool use, and multi-agent composition. Use this skill whenever the user is writing Python or TypeScript code that imports `opperai`, builds an Opper agent, or asks how to do anything Opper-related in code — even if they don't explicitly name the SDK. Both languages live in one repo with parallel numbered examples; agents are part of the SDK, not a separate package.
Autonomous project gardening by a coordinated team of agents. Spawns a team of gardeners that each run the `garden` skill in parallel, coordinating via a shared task list to avoid duplicate work. Use when the user wants to tend multiple small issues in one pass. Invoke with /gardeners.
Create a new Architecture Decision Record with sequential numbering and AgentDB registration