LLM Mart Basic

Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches,

File-upload bridge for Claude Code on the Web. CCotw has no native file mount; this skill creates a throwaway GitHub branch the user can drop files onto via the github.com web UI, then fetches them locally on the next turn. Use when the user wants to upload, share, or send files

Close a GitHub issue with a synthesis comment as a flowing graph — validate the synthesis, post the closing comment, close, then run a pluggable callback (e.g. memory store) detached. Use when closing an issue should also capture the LEARNING (not just the diff log) and when the

Installs and authenticates the GitHub CLI (gh) in a Claude.ai container, then runs gh commands against it. Use for "install gh", "gh auth", "create an issue or PR from the CLI", "gh is not authenticated", or any GitHub operation you want to issue as a gh command rather than a raw

Enables GitHub repository operations (read/write/commit/PR) for Claude.ai chat environments. Use when users request GitHub commits, repository updates, DEVLOG persistence, or cross-session state management via GitHub branches. Not needed in Claude Code (has native git access).

窗帘选购指南专用 Skill。覆盖窗帘款式、遮光防晒、布艺与纱帘、罗马杆与轨道、飘窗异形窗、电动智能窗帘、安装测量、搭配选购等全流程，整合西安装修课堂装修知识库精华，帮用户一次选对窗帘，遮光隐私好看又好用。

厨房装修避坑指南专用 Skill。覆盖厨房空间从格局（开放/半开放/砸墙/进门即厨房）、动线布局、水电防水地漏、橱柜柜体柜门、台面挡水下挂、水槽选购安装、收纳（高柜/超薄柜/切菜位）、砖与墙面、灯光采光、门（推拉门/联动门）、去家务化、安全（夺命操作/毒物/重金属锅）、智商税鸡肋等全主题，倒序整合西安装修课堂 24 篇原创干货，帮用户装修前一次规划对、入住少后悔。

橱柜定制避坑指南专用 Skill。覆盖台面材质、柜门与板材、五金与配件、衣柜鞋柜与餐边柜、橱柜布局与尺寸、全屋定制避坑等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的橱柜定制避坑。

瓷砖木地板选购指南专用 Skill。覆盖木纹砖与通铺、铺贴工艺、木地板选购、踢脚线与过门石、厨卫砖、瓷砖选购等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的瓷砖木地板选购。

灯光照明避坑指南专用 Skill。覆盖无主灯设计、灯带与氛围光、主灯与吸顶灯、色温显色与选灯、分区照明、灯光综合等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的灯光照明避坑。

吊顶装修指南专用 Skill。覆盖厨卫吊顶、不吊顶与石膏线、材料与工艺、层高与尺寸、客厅吊顶造型等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的吊顶装修。

防水避坑指南专用 Skill。覆盖卫生间防水、阳台防水、厨房防水、屋面外墙防水、地下室防潮、闭水试验、防水涂料与卷材选择、管根阴角细部处理、渗漏维修、防水层验收等全流程，倒序整合西安装修课堂知识库精华，帮用户一次做对防水、入住不返潮不漏。由西安装修课堂出品

复式·跃层·LOFT装修指南专用 Skill。覆盖楼梯设计与安全、挑空与夹层、跃层与顶楼阁楼、空间规划与功能、公寓与复式综合等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的复式·跃层·LOFT装修。

家电避坑指南专用 Skill。覆盖冰箱与厨房电器、空调地暖与新风、热水器与净水、洗衣机与清洁电器、电视投影与智能、卫浴电器、安装加价与售后避坑等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的家电避坑。

家具软装选购与搭配指南专用 Skill。覆盖沙发、床与床垫、衣柜与柜类、窗帘与布艺、地毯、灯具与配饰、装饰画、配色与风格搭配、成品家具选购与避坑、绿植软装等全流程，倒序整合西安装修课堂知识库精华，帮用户把钱花在能提升居住质感的家具软装上，不踩材质和搭配的坑。由西安装修课堂出品

精装修改造避坑指南专用 Skill。覆盖老房旧房改造、厨房卫生间改造、墙地面翻新、拆改项目与顺序、预算报价与维权、精装修微改等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的精装修改造避坑。

客厅装修避坑指南专用 Skill。覆盖客厅布局、沙发茶几、电视墙、吊顶、地砖、背景墙、无主灯、灯光插座、窗帘、阳台、风格搭配、收纳、投影仪、采光动线等全流程，倒序整合西安装修课堂知识库精华，帮用户装出实用又有品质的客厅。

买房购房避坑指南专用 Skill。覆盖高层与老破小取舍、户型选择、楼层与朝向、看房验房、中介与开发商避坑、贷款与税费、收房交付、二手房与期房、小区配套与学区、老破小改造前评估等全流程，倒序整合西安装修课堂知识库精华，帮用户买房不踩坑、不花冤枉钱。由西安装修课堂出品

门窗封窗避坑指南专用 Skill。覆盖窗户选型、入户门与室内门、封窗与落地窗、窗帘与遮挡、安装密封与五金、门窗选型综合等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的门窗封窗避坑。

飘窗窗户装修指南专用 Skill。覆盖飘窗窗帘、窗套与窗台、落地窗与封窗、安全与护栏、飘窗利用与改造等全流程，倒序整合西安装修课堂会员版知识库精华，帮用户装出更省心、不踩坑的飘窗窗户装修。

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.

Health-check your meeting knowledge for contradictions, stale commitments, and decision conflicts. Use when the user asks "any conflicts in my meetings", "check for stale action items", "lint my meetings", "consistency check", "are there contradictions", or wants to audit their decision history.

List recent meetings and voice memos. Use when the user asks "what meetings did I have", "show my recent recordings", "any meetings today", "list my voice memos", or wants an overview of their meeting history. Also use when they need to find a specific meeting by browsing rather than searching.

Act as the user's live meeting sidekick inside the current terminal agent session. Use when the user explicitly asks you, the terminal agent, to watch a meeting, follow the live transcript, answer during the call, offer strategist thoughts, silently watch for risks, or track decisions. Do not use this skill to start or control the separate Minutes Coach HUD; explicit Coach or HUD lifecycle requests belong to minutes-copilot, and an ambiguous request such as "coach me live" requires one short surface clarification.

Self-coaching analysis of your own behavior across meetings — talk-time ratio, filler words, hedging language, monologue length, energy patterns, and (when meetings are tagged via /minutes-tag) what your behavior in winning meetings looks like vs losing ones. Use this whenever the user says "how did I do", "review my last meeting", "mirror", "self-review", "show my patterns", "coach me", "where am I weak", "talk time", "am I improving", "what do I do in meetings I win", "feedback on me", or asks for any kind of personal feedback on their own meeting behavior. This is the rare skill that gives the user a mirror to their own habits — surface it whenever they show curiosity about their own performance, even if they don't use the word "mirror".

Add a note to the current recording or annotate a past meeting. Use whenever the user says "note that", "remember this", "mark this as important", "add a note about", "annotate the meeting", or wants to capture a thought during or after a recording. Plain text input — no markdown needed.

Interactive meeting preparation — builds a relationship brief and talking points before a call. Use when the user says "prep me for my call with", "I'm meeting with X", "prepare me for", "what should I bring up with", "meeting prep", "get ready for my call", or wants to review history with someone before a meeting.

Generate a daily digest of today's policy-authorized meetings and voice memos — key decisions, action items, and themes across available recordings. Use when the user asks "recap my day", "what happened in my meetings today", "daily summary", "what did I discuss today", "any action items from today", or wants a consolidated view of the day's conversations.

Start or stop recording a meeting, call, or voice memo. Use this whenever the user says "record", "start recording", "capture this meeting", "stop recording", "I'm in a meeting", "take notes on this call", or wants to transcribe live audio. Also use when they ask about recording status or want to know if something is being recorded.

Draft user-facing Minutes release notes for a version from the commit range, recent GitHub releases, and the repository release checks. Use when the user asks to write, generate, prepare, revise, or review release notes or a changelog for a Minutes version.

Search past meeting transcripts and voice memos for specific topics, people, decisions, or ideas. Use this whenever the user asks "what did we discuss about X", "find that meeting where we talked about Y", "what did Alex say", "did we decide on", "what was that idea about", or any question that could be answered by searching their meeting history. Also use for "do I have any notes about" or "check my meetings for".

Guided first-time setup for Minutes — download whisper model, create directories, configure audio input. Use when the user says "set up minutes", "install minutes", "first time setup", "configure minutes", "get started with minutes", "how do I start using minutes", or when verify shows missing components.

Lightweight outcome tagging for meetings — won, lost, stalled, great, or noise. Use whenever the user says "tag this meeting", "mark that as a win", "that one was a loss", "tag yesterday's call as stalled", "mark this great", "that meeting was noise", "label that meeting", or any time they describe a meeting outcome in passing. Tagging takes 5 seconds and unlocks /minutes-mirror correlation analysis — the more meetings get tagged, the smarter mirror gets at telling the user what behavior patterns lead to wins. Surface this skill any time the user mentions a meeting result, win, loss, or wasted time.

Verify that Minutes is properly set up and working — model downloaded, mic accessible, directories exist, no stale state. Use when the user says "is minutes working", "check my setup", "verify minutes", "test recording setup", "why isn't minutes working", "minutes health check", or after running setup for the first time.

Analyze a product walkthrough, bug report video, Loom, or ScreenPal using Minutes transcription plus visual review. Use when the user wants a recorded demo or bug clip turned into a durable brief with transcript, key frames, issues, and next steps.

Weekly meeting synthesis — themes, decision arcs, stale commitments, and what deserves your attention next week. Use when the user says "weekly review", "what happened this week", "weekly summary", "recap my week", "any outstanding items", "week in review", or at the end of a work week.

Search agent history or trace code to its original agent session

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Generate a custom checklist for the current feature based on user requirements.

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.