LLM Mart Basic

Delegate a coding task to the Kimi Code CLI (`kimi`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Kimi - phrasings like "have Kimi implement X", "delegate this to Kimi", "run it through Kim

Delegate a coding task to Oh My Pi (`omp`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Oh My Pi / omp - phrasings like "have omp implement X", "delegate this to oh my pi", "run it thro

Delegate a coding task to the OpenCode CLI as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to OpenCode — phrasings like "have OpenCode do X", "delegate this to OpenCode", "run it through OpenCode

Delegate a coding task to the Pi coding agent CLI (`pi`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Pi - phrasings like "have Pi implement X", "delegate this to pi", "run it through P

Delegate a coding task to the Qoder CLI (`qodercli`) as a background implementer, then review its diff and land it yourself. Use this whenever the user asks to have Qoder implement, fix, refactor, or run a queue of coding tasks while the orchestrator remains the reviewer. DO NOT

Delegate a coding task to the Mistral Vibe CLI (`vibe`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Vibe — phrasings like "have Vibe implement X", "delegate this to Vibe", "run it through

Delegate a coding task to the Warp Agent CLI (`oz`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Warp - phrasings like "have Warp implement X", "delegate this to the Warp CLI", "run it thro

Delegate a coding task to the Z.AI ZCode CLI as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to ZCode — phrasings like "have ZCode do X", "delegate this to ZCode", "run it through ZCode", or "use

Fetch brand SVG logos and cloud architecture icons (AWS, Azure, GCP) from theSVG. Use when the user asks for a brand logo, company icon, framework mark, service icon, or any "icon/logo for X" where X is a real brand or cloud service. Returns ready-to-use CDN URLs or raw SVG marku

Imported from ypares/rigup.nix/riglets/agent-rig-system.

Imported from ypares/rigup.nix/riglets/code-search.

Imported from ypares/rigup.nix/riglets/nix-module-system.

Imported from ypares/rigup.nix/riglets/riglet-creator.

Review AI agent skills before installation using NVIDIA SkillSpector and source-aware semantic review. Use when asked whether a skill or downloaded skill folder is safe, trustworthy, installable, over-permissioned, or malicious.

写一整本多章节教材或成体系的课程讲义（含习题）时用它：按 UbD 逆向设计调度五个阶段——教学定位 → UbD 预期结果（gate）→ 章节树（gate）→ 逐章写作 → 审核定稿；进度落盘 .progress.json，中断后可从断点续写。触发语如"写教材"、"写一本教材"、"编写课程讲义"、"系统教材"、"textbook"。只写单篇教程、只写一章（用 textbook-chapter）、只出习题（用 textbook-exercises）时不要用它。需与 textbook-outline、textbook-chapter、textbook-exer

按大纲切片与 UbD 锚点写一章教材正文时用它：固定四段式结构——概念讲解 → 示范例题 → 引导练习 → 独立习题。触发语如"写第三章"、"按大纲写这一章"。通常由 textbook 调度，也可单独运行，用来写一篇带完整例题的深度技术文章。不负责设计大纲，也不负责整本书的调度；只是问一个知识点、要一段解释而不是成篇教材内容时，也不要用它。需与 textbook 系列其余 skill 装在同一 skills 目录下。

生成带 Bloom 层级标注的成套习题时用它：示范例题（完整解题过程）、引导练习（带提示）、独立习题（附参考答案）三类，每道计算题的答案都真算核对过再输出。触发语如"出几道矩阵乘法的练习题"、"给这一章生成例题"。通常由 textbook-chapter 调用，也可单独运行。不适用于文科论述题（stem 学科档案未为其定义验证手段），也不负责组卷。需与 textbook 系列其余 skill 装在同一 skills 目录下。

动笔写教材前要先规划并创建工作目录时用它：规划单本教材项目目录或多教材工作区，可选 git 版本管理与 README 工作说明，建好后把产出交接给 textbook。触发语如"初始化教材项目"、"创建教材工作目录"、"规划教材工作区"、"建一个写教材的目录"。只搭目录骨架，不创建流水线文件（.progress.json、00-教材设计.md、术语表.md）。写正文用 textbook（不经 init 它也会自建默认目录），续写已有项目直接用 textbook。需与 textbook 系列其余 skill 装在同一 skills 目录下。

教材项目要做教学设计时用它：产出教学定位、UbD 预期结果五件套（大概念 / 持久理解 / 核心问题 / 迁移目标 / 学习目标）、章节树与例题 Bloom 梯度规划，写入 00-教材设计.md。触发语如"设计教材大纲"、"教材规划"、"课程体系设计"。通常由 textbook 调度，也可单独运行。不负责写章节正文，也不负责生成习题。需与 textbook 系列其余 skill 装在同一 skills 目录下。

Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications

Fourteen posts of being wrong in production, compressed to checkboxes

Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds

Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.

Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.

A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.

A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.

None of the safety came from the model. It came from six boring habits.

Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.

Six hours in, one step left, everything green, and the incident that didn't happen

CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.

Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it

An API request routing a model's tool call through an approval gate to a remote MCP server

31 config keys, two audits, and why the first one was wrong in both directions

The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.

Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.

Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.

"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks

stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.

The most important rule wasn't about what I could change. It was about what I was allowed to display.

Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.

Run a UI code review on the current snippet — Before / After / Why table per review-format, scoped to review-checklist.

Scan UI or marketing copy for AI-default tells and content-authenticity misses — deletion list, not a redesign brief.

Decide which moments earn a sound, design one material family, generate the files (ElevenLabs if keyed, synth or CC0 if not), and wire them in — returns a sound map table.

Animate an SVG — icon, logo reveal, stroke draw, morph, mascot loop — or turn a frame sequence / flat clip into one editable animated SVG, with the engine chosen for where the file lives.

Author or clean up an SVG asset — icon, illustration, mascot pose, logo mark — so it scales from viewBox, recolors from tokens, animates without a rewrite, is optimized, and has an accessible name.

Close a Context OS session through its review-gated workflow

Set up Context OS through its review-gated lifecycle workflow

Start a read-only Context OS continuity review

Save a review-gated Context OS checkpoint

Route broad repository discovery to a cheaper worker model using each client's native subagents, keeping the main agent for decisions and targeted verification. Use when asked to "reduce token usage", "delegate bulk reading", "set up a cheap reader agent", or "why is my context filling up".

Generate LEARNINGS.md from skill execution logs over a configurable time window.

Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading.

Generate context optimization report for skill directories

Create slash commands with brainstorming and best practices

Create hooks with brainstorming and security-first design

Scaffold new Claude Code skills with brainstorming, TDD methodology, and proper frontmatter and module structure.

Manually evaluate a recent skill execution to record qualitative feedback.

Evaluate all hooks in a plugin for quality and compliance

Identify and implement skill improvements from execution logs and user evaluations.

Analyze and enhance Makefiles for complete functionality coverage with auto-generation capability

Conduit — native SwiftUI iOS client for Hermes Agent

一个现代化的可视化规则引擎平台，用于编排复杂的 AI 工作流。在无限画布上设计、测试和部署 AI 流程——无需编写代码。结合 flowgram.ai 的能力与 Java 服务端，提供生产级工作流管理。在此之上提供AI Agent / Copilot的助手编排能力。

Job application tracker and AI-powered job search assistant. Helps job seekers manage their search journey with AI resume review, job matching, task logging, an…

📝 Markdown and HTML renderer for Svelte 5 — built for streaming AI agent output from Claude Code, ChatGPT, and agentic workflows. XSS-safe defaults, token cach…

ServiceNow MCP server: 500+ tools and 26 AI capabilities for any AI (Claude, ChatGPT, Gemini, Cursor, Copilot). Multi-transport (stdio, SSE, HTTP), A2A, dynamic…

Free crypto news API - real-time aggregator for Bitcoin, Ethereum, DeFi, Solana & altcoins. No API key required. RSS/Atom feeds, JSON REST API, historical archi…

Build production-ready AI agents in both Python and Typescript.

From agent user to agent builder: build a Claude Code-style coding agent from scratch in Python: 8 articles, 4 videos, one codebase

🪁 A lightweight, modern Kubernetes dashboard that unifies multi-cluster and resource management, enterprise-grade user governance (OAuth, RBAC, and audit logs)…

Stateful runtime management for LLM agents—inject, manipulate, and retrieve Python objects across turns.

AgentCall lets AI Agents join meetings with voice, video & screen-share to build together. Supports Google Meet, Teams, Zoom (Beta)

Kition brings Markdown, DataTable, WhiteBoard, a tool-using AI agent, browser research, and visual workflows into one desktop workspace.

IntentKit is an open-source, self-hosted cloud agent cluster that manages a collaborative team of AI agents for you.

A powerful Model Context Protocol (MCP) server providing comprehensive Google Maps API integration with LLM processing capabilities.

Turn your Claude Pro/Max subscription into an OpenAI-compatible API for your IDEs and devices — LAN auth, per-key quotas, response cache, disciplined cli.js ali…

Paw Work - selection-first web agent for Chrome: select on the live page, describe the outcome, take away an editable office file. BYOK, sandboxed, no server.

基于 AI Agent + MCP 工具链 + 渗透 Skill 编排， 配合大语言模型， 自然语言输入 → 自动完成「信息收集 → 漏洞发现 → 漏洞利用 → 报告生成」全流程。

Lexora — Personal AI workspace built around Desktop / 以 Desktop 为核心的个人 AI 工作台

First AI Journey for DevOps - with comprehensive learning paths, practical tips, and enterprise guidelines

AI coding agent with one Python core and three front-ends — headless CLI, Textual TUI, and an Electron desktop. Works with any OpenAI-compatible API, with risk-…