LLM Mart Basic

Choose simple, maintainable software designs by removing speculative complexity, comparing alternatives, and making explicit tradeoffs. Use for architecture, API, data-model, dependency, and scope decisions.

Implement software through small, repeatable, integrated vertical slices with clear exit criteria and honest verification. Use when a design is understood and code needs to be built or changed.

Review software with careful attention to correctness, maintainability, security, operations, and meaningful detail. Use for diffs, branches, pull requests, architecture decisions, or final quality checks.

Author a greenfield build blueprint in four gated stages — business logic, tech stack, logic-to-stack mapping, and a phase plan — each requiring explicit user approval before the next. Use when building a new project or a substantial new subsystem from scratch.

Step 0 of consequential software work under Monozukuri. Classify the task, assess its risk tier, choose execute or sensei mode, and compose the sequence of Monozukuri skills and the Definition of Done for it. Skip for trivial one-line edits, pure questions, and throwaway scripts.

Clarify software goals, constraints, stakeholders, and risks before consequential design or implementation work. Use for greenfield ideas, ambiguous requirements, architecture decisions, or changes where misunderstanding would be costly.

Prevent software mistakes through strong boundaries, safe defaults, meaningful tests, and mechanically enforced invariants. Use for TDD, validation, schemas, authorization, edge cases, regression coverage, or reliability-sensitive behavior.

Prepare software for responsible release, migration, deployment, rollback, and handoff with evidence about compatibility, health, ownership, and recovery.

Use when creating or updating AGENTS.md files, .github/copilot-instructions.md, or other AI agent rule files, onboarding AI agents to a project, standardizing agent documentation, or when anyone mentions AGENTS.md, agent rules, project onboarding, or codebase documentation for AI

Record a decision, document existing code, or file a supplied research material. Modes: document decision (ADR, RFC, or rule), document code (spec, doc, guide, or scenario for existing behavior), document research (only when a finished report or one external material is already i

First-time Archcore setup. Wires the host (MCP config, hooks, CLAUDE.md/AGENTS.md managed block), measures the authored context the repo already holds, then composes a first-day seed — stack rule, run guide, data-model, integrations, config, entry points, public surface, a linked

Plan a feature or initiative through a computed route: the conductor derives the canon delta and assembles the document package — from a zero-document null route for small fixes to an umbrella PRD with one spec per capability for large initiatives. Modes, named as the first word:

The pre-merge review of a branch in a project that records its specs, decisions, and rules in .archcore/. Run this first for 'review my branch', 'review the changes before merge', or 'review before merge': it checks the changed code against the project's recorded canon and the ch

Pull a Korean brand's published DESIGN.md from the ko-design-md catalog (getdesign.kr) and apply its design language — colors, typography, spacing, radius, components, do's & don'ts — to the UI you are building in the CURRENT project. Use this skill whenever the user wants to bui

Add a new design.md catalog entry to ko-design-md. Use this skill IMMEDIATELY when the user wants to onboard a new brand into THIS project's catalog — produce services/{slug}.md (Stitch v0.1 format) plus services/{slug}.tokens.json (token-card sidecar) plus public/preview/{slug}/

프리뷰 산문(public/preview/*/preview.html — 라이트·다크 한 파일의 캡션 등 글 전체)이 services/*.md 나 상류가 뒷받침하지 않는 주장을 하는지 대조·판정할 때 쓴다. 판정 근거의 등급, 상류(Claude Design 번들 또는 브랜드 발행물) 확인 절차, 슬러그별 상류 판정표, 그 되돌리기가 남긴 판정 규칙을 담는다. 프리뷰 산문을 고치거나 "md에 없다"를 근거로 프리뷰를 정정하려 할 때 반드시 먼저 읽을 것.

Plug-in web search, X (Twitter) search, and page fetch for models without native web access. Use whenever the task needs current information, external facts, source links, posts from X, or the content of a specific URL, and the active model/harness has no native search or fetch t

添加新的前端应用

添加新的微服务

旧项目接入 PDLC

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

Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)

Triage raw inbox notes into reviewed repository destinations without deleting their sources.

Audit and rewrite content to remove AI writing patterns

Log a completed piece of content to content/log.md after the user confirms it was published.

Validate a dream artifact, review each proposal, and apply only individually accepted changes.

Run a curator pass against the validated memory directory and produce a proposal artifact.

End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work

Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.

Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.

Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.

Scan multi-session drift and offer individually reviewed fixes only after explicit approval.

Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.

Guided onboarding or import for durable workspace context

Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers

Create a morning heartbeat from repository state and update the local heartbeat log.

Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session

Maintainer-only. Find every file that would newly ship to adopters and decide, one file at a time, whether to ship it or withhold it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.

Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.

Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.

Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.

面向中文开发者的 Claude Code Skills / Agents / Plugins 精选与原创技能库｜按场景分类｜复制即装｜持续更新

原生 macOS Git 客户端，以 Agent 驱动仓库管理、审阅与协作。

A 股短线复盘看板：涨停池·连板梯队·龙虎榜·板块资金一屏看完，赚钱效应/晋级率/梯队断层/情绪周期等派生指标纯计算直出（不经过 AI），AI 只把数据串成能读的盘面研判。全本地运行，可用 Claude/Codex 订阅免 API key。| A-share short-term daily-review dashbo…

禁漫天堂 Agent Skills / AI 原生 JMComic 助手：通过 MCP 与 Skills 将 JMComic 注入你的 AI Agent. / AI-powered JMComic assistant for seamless integration with AI Agents via MCP & S…

Pairlet (formerly CC Pocket / cc-pocket) — Continue your local AI coding tasks from phone, tablet, or desktop.

Self-hosted Personal AI + agent runtime in .NET (NativeAOT-friendly)

A self-hosted knowledge platform for humans and AI agents — publish wikis, blogs, and portable Agent Skills.

Notion CLI with AI agent support. Smart queries, Obsidian sync, batch ops, backups, validation and more.

AI Coding Agent Multiplexer

Independent executor–verifier orchestration for software changes.

AI-native, local-first video editor by Ribbi — runs entirely in the browser. Rust/WASM engine, WebGPU compositing, WebCodecs export; humans and LLM agents edit…

Distill your knowledge, memories, and decisions into an open-source, inspectable AI Agent Twin.

🔬 Harness Vibe Research with Self-evolving AI Scientists

Personal AI desktop agent for Windows, macOS, Linux, Android & iOS. Set a goal, it works on its own. Teams (pair two desktops, agents + humans), Agent2Agent, Wo…

Run agents like a company. AgentOS is the native control plane for OpenClaw — manage agents, tasks, models, context, approvals, and runtime visibility from one…

Create Agentic admin panels faster on TypeScript and Vue.js with AdminForth Framework. Setup main CRUD pages within minutes, extend as you need

OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards

An ebook reader with a self-evolving agent: it remembers your reading across books, and plugins extend the reader and the agent alike.

A curated list of awesome resources for vibe coding

Agentic Voice Notes for iPhone and macOS - Rust, Dioxus, LanceDB + RIG + SQLite