LLM Mart Basic

Use when implementing a multi-file change, building a feature from a breakdown, or writing a large amount of code. Not for a single settled ticket: use work.

Use when writing or restructuring code, before adding a helper, wrapper, config key, or dependency, or when the user asks for minimal or DRY code. Not for performance tuning: use optimize.

Use when asked to audit comments in code files and propose structural replacements or deletions with per-candidate approval. Not for deterministic commented-out-code removal: use deslop.

Use when a human says "overhaul", "rebuild this subsystem", or "rewrite it from scratch". Not for thin-slice features: use incremental-implementation. Not for root-cause repair: use strike-the-root.

Use when asked to optimize code, speed up a path, reduce allocations, repair a regression, or profile a target. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a request names a working principle (subtract before you add, idempotent operations, never block on the human) or asks which principle applies. Not for running a repair: use strike-the-root.

Use when modernizing APIs, removing compat shims, killing feature flags, or rewriting a subsystem cleanly. Not for additive refactors that must preserve the old path.

Use when a trusted bug or performance report needs reproduction and fix. Not for untrusted reports or scope beyond the named feature.

Use when an implementation has more workarounds than structure and another patch will not pay. Not for in-place re-derivation: use breaking-driven. Not for one-artifact rewrites: use rewrite-clean-v0.

Use when the user says "simplify this diff" or asks for a compression pass over a change-set. Not for dead-code sweeps: use deslop.

Use when an exact symbol, path, entrypoint, or line range can bound a focused code question or patch proposal under a fixed source budget. Not for source changes or broad repository exploration.

Use when writing or verifying framework-specific code, boilerplate, or a documented, correct implementation. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a feature begins or specs are checked in: author or update behavioral specs and keep them current with what ships. Not for producing the initial approved spec and plan: use spec-driven.

Use when a bug, failure, flake, regression, review finding, or ticket needs the core fixed so it cannot recur. Not for greenfield features: use tdd. Not for style-only review or typo-class one-liners.

Use when the user says greenfield this or rescue this codebase, names a field (dark, red, blue, or brown), or diagnoses a subsystem. Not for specs: use to-spec. Not for remote or irreversible changes.

Use when an abstraction leak must be sealed as a module seam, configuration option, or explicit override, or exposed as a named boundary. Not for detecting concealment patterns: use no-hide.

Use when the user says "audit my code", "find all the bugs", "review until clean", or "grill my changes". Not for remote, credential, or irreversible changes.

Use when asked to audit an agent or AI feature for agentic-experience quality (AX review, agent-native critique, trust question). Not for source or remote-system changes.

Use when asked to determine what a change could break before it ships. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a user wants to identify the true sources of complexity qualitatively before counting metrics. Not for source or remote mutation.

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.

SonarQube 정적분석 실행 및 결과 조회. 코드 품질·보안 핫스팟·커버리지 확인 시 사용.

GitHub 이슈 #$ARGUMENTS 를 처리한다(이슈 우선 워크플로):

이슈/기획서 기준 SDLC 한 사이클(이슈→개발→테스트→검증→PR)을 사람 개입 없이 자동 실행.

GitLab 이슈 #$ARGUMENTS 를 처리한다(이슈 우선 워크플로):

이슈/기획서 기준 SDLC 한 사이클(이슈→개발→테스트→검증→MR)을 사람 개입 없이 자동 실행.

Invoke a repeated task with `/name`. File name = command name (`fix-issue.md` → `/fix-issue`).

Handle issue #$ARGUMENTS (issue-first workflow):

Renders the connection structure of the AGENTS.md ecosystem

Automatically runs one SDLC cycle (issue → development → testing → verification → PR/MR) based on an issue or spec, without human intervention.

Run SonarQube static analysis and check the results. Use to check code quality, security hotspots, or coverage.

Handle GitHub issue #$ARGUMENTS (issue-first workflow):

Automatically runs one SDLC cycle (issue → development → testing → verification → PR) based on an issue or spec, without human intervention.

Handle GitLab issue #$ARGUMENTS (issue-first workflow):

Automatically runs one SDLC cycle (issue → development → testing → verification → MR) based on an issue or spec, without human intervention.

Show Hermes planning-with-files status for the current project.

Start Hermes planning-with-files workflow in the current project.

بدء تخطيط الملفات بنمط Manus. إنشاء task_plan.md و findings.md و progress.md للمهام المعقدة.

Lock the current task_plan.md content with a SHA-256 attestation. Hooks then refuse to inject plan content if the file diverges from the attested hash, blocking silent tampering. Use --show to print the stored hash, --clear to remove the attestation. Available since v2.37.0.

Starte Manus-artige Dateiplanung. Erstelle task_plan.md, findings.md, progress.md für komplexe Aufgaben.

Self-check for the planning-with-files mechanisms that fail silently: plan resolution, hook injection, canonicalizer path shape, attestation state, install surfaces, and per-fire hook latency. Run it whenever hooks seem quiet or after installing on a new machine. Available since v3.6.0.

Build AI Agents like playing LEGOs. Everything is a Plugin.

The coding agent that answers to you, your model, your machine, your rules.

CI-native agent CLI tool for deterministic pipeline gating.

Local-first cross-platform desktop workspace for Claude Code / agents: multi-agent, Git worktrees, code diffs, skill marketplace, multi-model, Computer Use, tas…

Local-first visual generation runtime and studio for people and coding agents, with reproducible image and video workflows across multiple providers.

A token-efficient MCP server for safe, line-oriented text file editing with partial reads, hash-based conflict detection, and atomic multi-file operations.

Open-source, evidence-driven MCP server for RTL simulation debugging: correlate VCS/Xcelium logs, VCD/FSDB waveforms, SystemVerilog/UVM source, hierarchy, and c…

runs anywhere. uses anything

One macOS app for Claude Code, Codex, and every agent runtime you use — scheduled runs, global hotkey launcher, per-run git worktrees, one review board.

Soul-driven AI agent with permission-hardened tools, token budgets, and multi-channel access. Runs 24/7 from CLI or Telegram.

Email, SMS & phone-call infrastructure for AI agents — send and receive real email and text messages, and place agent-driven outbound voice calls, all programma…

55 MCP tools to drive the Unity Editor from any MCP-compatible AI agent — scenes, GameObjects, prefabs, assets, tests, and builds. OpenClaw gateway included for…

Google Ads MCP + Meta Ads MCP + GA4 in one server for Claude, ChatGPT, Cursor & n8n — 250+ tools, hosted remote MCP, OAuth login, no API keys. By Ryze AI.

Claude Code & Codex 本地 token 追踪 — 状态栏（Codex 业界首创伪 statusline）、GitHub 风格热力图、多模型成本分析。 | Local token tracker for Claude Code & Codex — status line (industry-first…

Empower the Shell to think. Evolve Operations.

🤖 A full-stack Agent Chat Runtime for pre-production validation, built with act, TypeScript, and LangGraph JS combining streaming, multimodal, HITL, and tools…

Cerul — video memory infrastructure for AI products. SDKs, CLI, MCP, and developer integrations.

Stealth headless browser for AI agents — bypass Cloudflare, bot detection, and anti-scraping. Drop-in Puppeteer/Playwright replacement.

Web research for your agents with smart and safe tooling + knowledge store

FuXi is a fast, self-contained AI coding agent that lives in your terminal — edit code, run commands, and drive tools, with cost-aware routing across LLM provid…