Claude Skills by majiayu000

Generate a complete OAF-compliant agent package from an Agent Case (requirements) and Agent Design (architecture). Use when asked to "package an agent", "generate an agent package", "create an agent from a design", or when you have both case/ and design/ folders and need to create the package/ folder with AGENTS.md, skills, and scripts.

Guide for using pagent - a PRD-to-code orchestration tool. Use when users ask how to use pagent, run agents, create PRDs, or transform requirements into code.

Guide for using pagent - a PRD-to-code orchestration tool. Use when users ask how to use pagent, run agents, create PRDs, or transform requirements into code.

Guidance for working with the PolicyEngine GitHub agent bot

Become an autonomous prediction market trader on Polymarket with AI-powered analysis and a performance-backed token on Base. Trade real markets, build a track record, and let the buyback flywheel run.

This skill should be used when users want to create powerful AI agents comparable to Claude Code or sonph-code. It provides battle-tested system prompts, masterfully-crafted tool implementations, and the simple but powerful agent loop pattern. Use this skill when users ask to build coding agents, AI assistants with tools, or any autonomous agent that needs file operations, code execution, search, and task management capabilities. The key insight is that customization requires only ONE HumanMe...

Anticipates and pre-loads optimal skills before task execution based on pattern matching and historical success rates

Create and manage agentic wallets with Privy. Use for autonomous onchain transactions, wallet creation, policy management, and transaction execution on Ethereum, Solana, and other chains. Triggers on requests involving crypto wallets for AI agents, server-side wallet operations, or autonomous transaction execution.

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Includes memory architecture with pre-compaction flush (so context survives when the window fills), reverse prompting (surfaces ideas you didn't know to ask for), security hardening, self-healing patterns (diagnoses and fixes its own issues), and alignment systems (stays on mission, remembers who it serves). Battle-tested patterns for agents that learn from every interaction and cre...

Patterns and workflows for proactive agent behavior.

Operate an AI agent inside OpenClaw World over HTTP IPC. Use when tasks involve registering or reconnecting an agent, moving, chatting, forming alliances, running turn-based combat, checking world phase/state, or handling survival and betting commands.

Python Agent 开发规范（Windows wxauto v4），包括项目结构、模块化、wxauto 使用、IPC 集成、错误处理、测试和部署。

Show AgentOps next action.

持久化执行-验证-修复循环，直到任务完成

Specification-first AI development powered by Ouroboros. Socratic questioning exposes hidden assumptions before writing code. Evolutionary loop (Interview → Seed → Execute → Evaluate → Evolve) runs until ontology converges. Ralph mode persists until verification passes — the boulder never stops. Use when user says \"ralph\", \"ooo\", \"don't stop\", \"must complete\", \"until it works\", \"keep going\", \"interview me\", or \"stop prompting\".

Enables autonomous development loops that run until all tasks pass. Use when the user says "until done", "keep going", "finish this", "終わるまでやれ", or requests long-running autonomous iteration with fix_plan.md tracking.

Use this skill when Ralph is working autonomously through Brain Dump backlogs. Covers ticket selection, implementation patterns, and autonomous workflow management.

Run Ralph autonomous build loop. Use when user asks to "ralph build", "run build loop", or needs to process subtasks autonomously. Executes iterations against a subtasks.json queue.

Create and run Ralph loops for structured AI-driven development. Triggered by "create a ralph loop for X" or "ralph plan for X". Uses interview to clarify requirements, expert review via Ralph wrappers, creates phased task plans, and executes in YOLO mode.

Self-referential loop until task completion with architect verification

Persistent completion mode. Use when the user explicitly says `/ralph` or clearly wants you to keep iterating until the task is actually finished, repeating implement-verify-fix loops instead of stopping at partial progress.

Run RALPH autonomous development loop. Converts PRD markdown to prd.json and runs autonomous implementation.

Run RALPH autonomous development loop. Converts PRD markdown to prd.json and runs autonomous implementation.

Autonomous execution loop that processes a Beads epic task-by-task with fresh subagents, two-stage review, and circuit breaker safety. Use after plan-to-epic creates the epic.

Execute iterative development loop through user stories. Implements one story at a time with quality gates, commits, and learning persistence. Ships features autonomously.

Execute Ralph loops. Use when: Running the autonomous loop, managing tasks, or handling backpressure. Not for: Designing new loops or defining high-level strategy.

Convert PRDs to prd.json format and run the Ralph autonomous agent system. Use when converting PRDs to Ralph format OR running Ralph to execute user stories. Triggers on: convert this prd, turn this into ralph format, create prd.json, ralph json, run ralph, start ralph, execute ralph.

Runs autonomous loop fetching stories from GitHub Issues. Implements and closes issues as done. Triggers on "loop through my PRDs", "work on my issues", "start the autonomous loop", "implement my PRDs", or requests to work through GitHub issues autonomously.

Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.

Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert to ralph, convert this prd, turn this into ralph format, create prd.json, ralph json, run ralph on this.

Converts a PRD or plan markdown file into prd.json format for ralph-json-start-loop to execute autonomously. Use when user wants to convert a PRD or plan to JSON stories.

Runs the Ralph autonomous loop. Executes stories from prds/*.json using git worktrees.

Activate autonomous Ralph Wiggum loop mode for iterative task completion. Use when you have a well-defined task with clear completion criteria that benefits from persistent, autonomous execution.

Use after first code change. Autonomous iteration until all quality gates pass (max 7 iterations).

Setup the Ralph autonomous AI coding loop - ships features while you sleep

Autonomous feature development loop. Executes complete 9-phase cycle (Interview -> Think Critically -> Plan -> Branch -> Implement -> Verify -> PR -> Merge -> Wrap-Up) with minimal human intervention. Triggers on "/ralph", "start autonomous loop", "run ralph loop".

Start a Ralph Wiggum autonomous task loop that keeps Claude working until a task is complete. Uses the Stop hook pattern to re-inject prompts when Claude tries to exit before finishing. Supports two completion strategies: promise-based (Claude outputs a completion token) and file-movement-based (task file moves to /Done). Use when a multi-step task requires Claude to iterate until completion, such as processing all items in /Needs_Action or generating a complete audit.

Ralph Wiggum-inspired automation loop for specification-driven development. Orchestrates task implementation, review, cleanup, and synchronization using a Python script. Use when: user runs /loop command, user asks to automate task implementation, user wants to iterate through spec tasks step-by-step, or user wants to run development workflow automation with context window management. One step per invocation. State machine: init → choose_task → implementation → review → fix → cleanup → sync →...

Iterative development loop methodology for autonomous AI work. Configure self-correcting coding loops that iterate until completion criteria are met, integrate with Archon for task tracking, and support multiple execution modes. Use when running autonomous coding sessions, implementing self-correcting workflows, or building iterative development pipelines.

Execute an autonomous development loop that picks one task per iteration, implements it, verifies it, and commits the result — each iteration in a fresh context window. Use when user runs /ralph, mentions "ralph loop", "autonomous loop", "builder verifier", "run tasks automatically", "iterate on tasks", "develop autonomously", or wants an automated build-verify-commit cycle with task tracking.

Ralph Loop plugin manager. Provides start, cancel, status, and help commands for autonomous task loops. Enforces safety guardrails (sandbox, deny rules, PR-only, max-iterations). Use /ralph-loop:start to begin, /ralph-loop:cancel to stop.

Autonomous agent loop for completing features. Use when asked to 'use ralph', 'ralph this', or to autonomously implement a feature end-to-end. Creates prd.json with user stories, then executes them one by one until complete.

Ralph autonomous coding loop with MiniMax subagent delegation. Managed loop (not recursive) with progress tracking, completion detection, and circuit breakers.

Run RALPH autonomous development loop to implement features from the PRD.

Run RALPH autonomous development loop to implement features from the PRD.

Run RALPH autonomous development loop with multi-agent pipeline

Run long-running autonomous AI agents using PRD-based task scoping.

Set up automated agent-driven development with Ralph. Run AI agents in a loop to implement features from user stories, verify acceptance criteria, and log progress for the next agent.

Use only when the user explicitly invokes `$ralph-specum`, requests Ralph Specum in Codex, asks Ralph Specum to handle a named phase, or explicitly requests autonomous or quick mode or continuation without pauses.

Execute iterative development loop through user stories. Implements one story at a time with quality gates, commits, and learning persistence. Ships features autonomously.