LLM Mart Basic

사용자가 "녹음시작" / "녹음 시작" / "강의 녹음" / "회의 녹음" / "전사 시작" 이라고 하거나, 실시간 강의·회의·교육 세션의 녹음과 요약을 요청할 때 사용한다. 실시간 전사와 함께 오디오 원본(m4a)도 저장한다. 실행을 멈추는 "녹음종료" / "녹음 끝" 도 이 스킬이 처리한다.

피드백 기반으로 SKILL.md를 자동 개선하는 메타 스킬

SQLite 파일을 직접 읽고 쓰거나, 읽기 전용 조회·WAL·잠금·마이그레이션·동적 테이블명 주입 같은 SQLite 고유 문제를 다룰 때 사용한다.

用户明确说“规格已确认，开始实现”或要求按已审批 CM specs 开发时使用。新任务默认由 JS workflow 驱动 N1-N8，完成开发、独立审查、QA 与文档同步；模糊点子、未审规格和单独一句“继续”不能触发编码批准。

后端 API 工程师 Skill，执行服务端 API 层开发（路由、业务逻辑、鉴权中间件、缓存、队列），自动适配语言和框架

用户说“检查工作流是否安装正确”“为什么找不到 cm 命令”时使用。默认查询 npm 稳定版，有新版自动升级已管理的 CM 安装，再检查插件、核心 Skills、兼容包装与模板引用；不测试或修改业务代码。

智能合约工程师 Skill，执行合约开发、测试、部署，自动适配 EVM/Solana/Move 等链和开发框架

数据库工程师 Skill，执行数据模型设计、migration、查询优化，自动适配 ORM 和数据库类型

发布/运维工程师 Skill，执行 staging 部署、冒烟验证、发布记录与生产发布待决清单编制，自动适配部署栈；生产发布与基础设施变更强制人工确认

文档同步 Skill，开发完成后自动更新 README、.claude/ 配置、specs CHANGELOG，保持文档与代码一致

金融专家 Skill，覆盖 Web3 与证券/资产/交易领域的正确性审核、营销合规红线识别、合规问题清单生成、业务验收协同；把关型角色，只举旗不定性

用户说“修复这个可复现 bug”或要求根据失败报告修代码时使用。执行红灯测试、根因定位、最小修复、独立审查和回归；尚未确认的问题先用 cm-test，新功能和架构重设计转交 cm-prd。

前端工程师 Skill，执行前端开发任务，自动适配项目技术栈（React/Vue/Svelte/Next.js 等），支持 Figma/Stitch 设计稿还原

用户说“我有个点子”“帮我梳理产品”或需要先聊清目标时使用。通过逐题访谈整理为可交给 cm-prd 的 PRD；已有明确需求文档时改用 cm-prd，不写代码、不拆开发任务。

用户说“第一次接管这个项目”“分析仓库并生成项目规则”时使用。分析已有代码并生成 Codex AGENTS.md 与 CM/Claude 兼容规则；仅适用于非空存量项目，不创建脚手架、不承接普通代码修改。

微信小程序开发工程师 Skill，执行小程序开发任务，自动适配项目技术栈（原生小程序/Taro/uni-app 等），支持 Figma/Stitch 设计稿还原与云开发

用户说“把需求拆成可开发规格”“变更现有功能需求”或要求整理方案、任务和验收时使用。支持新项目、存量二开与需求变更；完成后停在人审规格，不直接编码。

产品经理 Skill，负责需求分析、用户故事与验收标准编写、歧义清单生成、变更影响分析、业务验收走查；把关型角色，不做技术设计与技术测试

QA 工程师 Skill，执行功能测试、E2E 测试、可视化回归、验收标准核验，自动适配项目测试框架

用户明确要求“只整理结构，不改变行为”时使用。执行边界分流、行为判官、分批重构和独立审查；缺陷修复转交 cm-fix，新增或变化的业务行为转交 cm-prd。

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.

A green PR, a controller reporting success, and not one line of the new code running

Audit agents spawned in the current/last run against the agent-selection taxonomy

Run the Agent Diversity Review gate and emit the result table

Meta-analyze the effectiveness of a /loop arc — per-iteration metrics, LOC delta trend, saturation detection, and a keep/lengthen/delete recommendation.

Audit the rules/ directory for missing foundational principles; output gap list with priority and justification

Validate ~/.claude/settings.json hooks block — event names, file existence, executability, matcher syntax; --fix repairs common issues

Catch Resend-class bug (isError: false on HTTP 4xx/5xx) across all MCP server tool handlers

Healthcheck + drift detect + rotation-reminder across all MCP servers in ~/.claude/mcp-registry.json

Catch mock/live divergence in MCP eval golden tests (anti-pattern #2 from eval-mock-mode-discipline)

Catch the github-mcp class of bug — CallTools handlers that exist but are invisible to Claude because ListTools never advertises them (orphaned), or ListTools entries that have no handler (zombies)

Validate _router.md — check every referenced skill file exists; surface stale entries + orphan files; --fix prunes or stubs

List every active tool across all MCP servers and flag cross-MCP duplicates and semantic overlaps

Scaffold a new spawnable specialist agent def and register it in the agent taxonomy

Audit whether user-visible changes in the current session have matching CHANGELOG.md entries; report MISSING with suggested lines; --fix auto-appends

Repeatable pass upgrading an Angular admin dashboard into a compact black-and-cyan developer-cockpit PWA

Deploy an MCP server generated by forge-from-openapi --target=mcp-server; detect transport, deploy, smoke-test, print .claude.json snippet

Run the drift-detection checklist (incl. agent-drift signals); report + fix in-turn

Execute a PRP — TodoWrite breakdown, parallel implementation, validate every gate, deploy, prove on prod

Orchestrate the final review fan-out (integration + diversity + risk + release readiness)

Auto-generate a Claude Code skill (commands + types + client) from any OpenAPI 3.x spec URL or file path

Scaffold a complete Claude Code skill (commands + types + client) from a GraphQL endpoint or local schema file

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.