AI & ML

pjt222 Skill

Launch all available agents in parallel waves for open-ended hypothesis generation on problems where the correct domain is unknown. Use when facing a cross-domain problem with no clear starting point, when single-agent approaches have stalled, or when diverse perspectives are more valuable than deep expertise. Produces a ranked hypothesis set with convergence analysis and adversarial refinement.

leoyeai-openclaw-master-skills Bundle

Scan AI agent skills for security vulnerabilities, dangerous code patterns, and undeclared permissions. Three-layer analysis: dependency CVE scanning, static code analysis, and permission auditing. Returns structured JSON risk report. Use when the user asks to scan a skill for security issues, check for vulnerabilities, audit permissions, or assess skill safety.

leoyeai-openclaw-master-skills Bundle

TMR Land business agent for an AI business marketplace. Use when: (1) registering as AI service business, (2) managing agent cards and capabilities, (3) fulfilling personal orders, (4) answering Grand Apparatus questions, (5) building reputation, (6) configuring A2A endpoints.

leoyeai-openclaw-master-skills Bundle

Search, scan, and interact with the BasedAgents.ai agent registry — the public identity and reputation layer for AI agents. Look up agents, check reputation scores, scan npm/GitHub/PyPI packages for security issues, probe MCP endpoints, browse tasks, and send agent-to-agent messages.

pjt222 Skill

Create a Dockerfile for R projects using rocker base images. Covers system dependency installation, R package installation, renv integration, and optimized layer ordering for fast rebuilds. Use when containerizing an R application or analysis, creating reproducible R environments, deploying R-based services (Shiny, Plumber, MCP server), or setting up consistent development environments across machines.

pjt222 Bundle

Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.

pjt222 Skill

Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

pjt222 Bundle

Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

pjt222 Skill

Design monochrome ornamental patterns grounded in Alexander Speltz's classical ornament taxonomy. Covers historical period selection, motif structural analysis, prompt construction for line art and silhouette rendering, and AI-assisted image generation via Z-Image. Use when creating decorative borders, medallions, or friezes in a single color, exploring historical ornament styles through generative AI, producing line art or pen-and-ink renderings of classical motifs, or generating reference imagery for design or educational materials.

pjt222 Skill

Export and optimize 3D models for FDM/SLA printing including STL/3MF export, mesh integrity verification, wall thickness checking, support generation, and slicing. Use when exporting from CAD or modeling software for 3D printing, verifying STL/3MF files are printable before slicing, troubleshooting models that fail to slice correctly, optimizing part orientation for strength or surface finish, or converting between model formats while preserving printability.

pjt222 Skill

Scaffold a new MCP server from tool specifications using the official SDK (TypeScript or Python), including transport configuration, tool handlers, and test harness. Use when you have a tool specification and need a working server, when starting a new MCP server project and want correct structure from the start, when migrating an existing tool integration to the MCP protocol, or when prototyping a tool surface to test with Claude Code before full implementation.

leoyeai-openclaw-master-skills Skill

Diagnose issues, check prerequisites, and validate configurations. Use when: "doctor", "debug", "why failing", "validate config", "check prerequisites", "something is broken", "db status", "fix my setup".

leoyeai-openclaw-master-skills Bundle

Complete OpenClaw Agent Security Hardening - Protects against data leaks (storage security) and prompt injection (runtime security). Use for initial setup, security audits, and ongoing maintenance. Covers file permissions, sensitive data isolation, Git protection, and command execution safety.

leoyeai-openclaw-master-skills Bundle

Generate an editable PowerPoint (PPTX)/Slide deck from a prompt with AnyGen. Choose styles, page count, ratio, and auto-download the PPTX file. Triggers: make PPT, create slides, slide deck, presentation, quarterly review deck.

leoyeai-openclaw-master-skills Bundle

Vote and submit AI verdicts on ethical, cultural, and content cases alongside human crowds. Includes an autonomous heartbeat orchestrator (heartbeat.mjs) that can optionally call local LLM CLIs (claude, codex) or Anthropic/OpenAI SDKs to evaluate cases and submit verdicts automatically on a schedule. Writes persistent state to ~/.judgehuman/state.json.

pjt222 Skill

Validate deliverables and build evidence trails when work passes between agents. Covers expected outcome specification before execution, structured evidence generation during execution, deliverable validation against external anchors after execution, fidelity checks for compressed or summarized outputs, trust boundary classification, and structured disagreement reporting on verification failure. Use when coordinating multi-agent workflows, reviewing cross-agent handoffs, producing external-facing outputs, or auditing whether an agent's summary faithfully represents its source material.

pjt222 Skill

Configure MCP (Model Context Protocol) servers for Claude Code and Claude Desktop. Covers mcptools setup, Hugging Face integration, WSL path handling, and multi-client configuration. Use when setting up Claude Code to connect to R via mcptools, configuring Claude Desktop with MCP servers, adding Hugging Face or other remote MCP servers, or troubleshooting MCP connectivity between clients and servers.

pjt222 Skill

Configure the putior MCP server to expose 16 workflow visualization tools to AI assistants. Covers Claude Code and Claude Desktop setup, dependency installation (mcptools, ellmer), tool verification, and optional ACP server configuration for agent-to-agent communication. Use when enabling AI assistants to annotate and visualize workflows interactively, setting up a new development environment with putior MCP integration, or configuring agent-to-agent communication via ACP for automated pipelines.

pjt222 Skill

Conduct a structured cross-project code review between two Claude Code instances via the cross-review-mcp broker. Each agent reads its own codebase, reviews the peer's code, and engages in evidence-backed dialogue — with QSG scaling laws enforcing review quality through minimum bandwidth constraints and phase-gated progression.

leoyeai-openclaw-master-skills Bundle

When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," "Dataset," or "GEO."

leoyeai-openclaw-master-skills Bundle

Teaches OpenClaw agents to participate in authentic text-based Krump battles. Use when the agent is invited to a Krump battle, needs to respond with Krump vocabulary, or competes on KrumpKlaw. Includes judging criteria, battle formats, and cultural vocabulary from Free-DOM Foundation research. Enriched with ClawHub krump, KrumpClaw, and Asura lineage knowledge.

leoyeai-openclaw-master-skills Bundle

Multi-queue task orchestration system. Tasks are routed to queues by model source, with support for task dependencies, context passing, and failure handling. Each model source has its own FIFO queue, executing one task at a time.

leoyeai-openclaw-master-skills Bundle

知乎 CLI (pyzhihu-cli)：搜索、热榜、问题/回答/评论、推荐 Feed、用户资料、发想法/提问/文章、删自己的内容、点赞关注、收藏与通知。Agent 代执行 zhihu 命令，Cookie 仅存本地。

pjt222 Skill

Implement a JSON-RPC 2.0 A2A server with full task lifecycle management (submitted/working/completed/failed/canceled/input-required), SSE streaming, and push notifications. Use when implementing an agent that participates in multi-agent A2A workflows, building a backend for an Agent Card, adding A2A protocol support to an existing agent or service, or deploying an agent that must interoperate with other A2A-compliant agents.

leoyeai-openclaw-master-skills Bundle

Multi-agent orchestration patterns for production deployments. Covers sub-agent QC workflow, model staggering across 5+ models, cross-validation patterns, fallback chains, task routing by model strength, ACPX configuration, and cost optimization. Use when coordinating multiple agents or models for complex workflows. Do NOT use for single-agent prompting, prompt engineering, or fine-tuning — those are separate skills.

leoyeai-openclaw-master-skills Bundle

Transform AI agents from task-followers into proactive partners. Implements WAL Protocol, Working Buffer, Compaction Recovery, Unified Search, Security Hardening, and Relentless Resourcefulness. Use when: building proactive behaviors, implementing memory systems, setting up heartbeats, creating self-improving agents, or deploying the complete Hal Stack agent architecture. Part of the Hal Stack 🦞

leoyeai-openclaw-master-skills Bundle

Generate high-quality, customizable AI prompts for various use cases including creative writing, problem-solving, education, business, programming, Flutter development, and game development. Use when a user asks for help creating prompts, wants to improve their AI interactions, or needs structured prompt templates for specific tasks.

leoyeai-openclaw-master-skills Bundle

途牛火车票助手 - 通过 exec + curl 调用 MCP 实现车次列表搜索、查询车次详情、预订下单。适用于用户查询车次列表、查询车次详情或提交火车票订单时使用。

pjt222 Skill

Design an A2A Agent Card (.well-known/agent.json) manifest describing agent capabilities, skills, authentication requirements, and supported content types. Use when building an agent that must be discoverable by other A2A-compliant agents, exposing capabilities for multi-agent orchestration, migrating an existing agent to the A2A protocol, defining the public contract for an agent before implementation, or integrating with agent registries that consume Agent Cards.

leoyeai-openclaw-master-skills Bundle

AI短剧提示词工厂 - 端到端将小说/故事创意转化为结构化提示词包（角色立绘+场景+道具+分镜图片+视频+音频）的完整流水线。一个入口启动全流程，内部自动按阶段推进：策划→设计→剧本→诊断→提示词组装→API JSON输出。支持小说改编和原创短剧两条路径。触发词：AI短剧、短剧制作、小说转短剧、短剧提示词、提示词工厂、短剧全流程、小说改编短剧、生成短剧、AI视频剧本、短剧项目、分镜提示词、视频提示词、prompt factory。

leoyeai-openclaw-master-skills Bundle

World Model - Environment understanding, causal reasoning, and prediction for AGI

leoyeai-openclaw-master-skills Bundle

Build and operate autonomous AI agents that compete in Aureus Arena, a fully on-chain Colonel Blotto game on Solana. Use when the user asks about Aureus, Colonel Blotto strategies, building competition bots on Solana, or interacting with the Aureus Arena protocol. Also use when an AI agent wants to autonomously enter the arena itself.

leoyeai-openclaw-master-skills Bundle

AutoML 自动化机器学习技能 | Automated Machine Learning Skill. 基于 PyCaret 进行低代码机器学习建模，支持分类、回归、聚类、异常检测、时间序列预测、自然语言处理和关联规则挖掘等任务。 未来将集成更多 AutoML 库（如 AutoGluon、FLAML 等）。 当用户需要快速构建机器学习模型、自动化模型选择、超参数调优、模型集成、特征工程或进行 AutoML 实验时使用此技能。 适用于数据科学家、公民数据科学家、机器学习工程师和希望快速原型开发的人员。 触发关键词：AutoML、机器学习自动化、PyCaret、分类模型、回归模型、聚类、异常检测、时间序列、文本分类、模型调优、模型比较、特征选择、统计检验、显著性检验、A/B测试。 Trigger keywords in English: AutoML, automated machine learning, PyCaret, classification, regression, clustering, anomaly detection, time series forecasting, NLP, text mining, model tuning, model comparison, feature engineering, statistical test, significance testing, A/B testing.

leoyeai-openclaw-master-skills Bundle

分析 Android 项目源码，用 LLM 从多维度生成 AI 自动化测试所需的先验知识文档，打包上报测试平台。核心价值：让 AI 测试 Agent 在运行前就知道「测什么、怎么断言、有哪些陷阱」。触发词：「分析我的 Android 项目」「生成测试画像」「理解这个 App 的业务」「提取测试先验知识」「帮我分析 Android 源码」

leoyeai-openclaw-master-skills Bundle

Client onboarding and business diagnostic framework for AI agent deployments. Covers 4-round diagnostic process, 6 constraint categories, deployment SOP with completion contracts, tiered advisory mode for new automations, and the 6-week sell narrative. Use when onboarding new clients for agent deployments or managed automation services. NOT for self-service SaaS onboarding or consumer products.

leoyeai-openclaw-master-skills Bundle

Complete operational playbook for Fractional CFO engagements. Covers client onboarding financial assessment, monthly close cadence, board-ready reporting packages, cash flow management, strategic financial advisory, and engagement scope templates. Use when setting up or running a Fractional CFO service, onboarding a new client, preparing monthly CFO deliverables, or advising on financial strategy as an outsourced finance executive. NOT for: building financial models from scratch (use startup-financial-model), bookkeeping or transaction recording (use qbo-automation), tax filing preparation (PTIN-required service), or one-time financial analysis without an ongoing advisory context.