LLM Mart Basic

Flutter Riverpod app architecture and Windows installer delivery. Use before changing a Riverpod Flutter app/package or its Windows desktop packaging/update pipeline; skip non-Riverpod stacks and pure-Dart work.

Review PR, branch, commit or working-tree changes for concrete defects and fidelity to intended behavior. Also use when explicitly asked to independently challenge a prepared plan, diagnosis or verification claim.

Design or review module ownership, public interfaces and abstractions; UI token/component ownership; or domain-driven models, bounded contexts and invariants. Use for structural decisions, not routine edits within an established owner.

Prove real user journeys across browser, mobile, desktop, API and CLI surfaces using the project's existing test tools. Use for end-to-end verification, runtime UI review or requested visual proof.

Apply Hard Eng's planning, project context, verification and gate-adaptation guidance when implementing or reviewing code in an installed project.

Implement and verify a ready, authorized plan through focused fixes and integrated checks, ending with a ready-for-ship handoff. Use for implementation, bug fixes or resuming build work; skip planning-only, review-only and shipping-only requests.

Prevent verified repeated failures and preserve lasting decisions in terse ADRs. Use when failures recur, prevention needs strengthening, or accepted decisions and steering need durable capture; skip ordinary one-off fixes and routine task updates.

Plan a change before implementation, resolve material user decisions and prepare repository-grounded UX references. Use a compact plan for small changes; route large, unclear efforts or explicit Wayfinder requests to Wayfinder. Skip explanation-only requests and execution already

Deliver a verified build through a task branch and PR, check the intended remote result, and clean up the completed task safely. Use for PR creation, shipping, merging or release/deployment requests; skip planning, implementation and review-only work.

Make a voiced product marketing or explainer video from real product screens. Script, cut walkthrough clips or stage app screenshots with a cursor, pick voice takes, render branded motion graphics and diagrams, mix music and review every second before delivery.

Investigate a codebase, compare tools or approaches, verify current external or library facts, and research failures before making a substantive technical recommendation. Use for sufficiency and gap reviews; ordinary edits with settled requirements do not need a research stage.

Review authorized application code, configuration, designs or artifacts for concrete authorization, data exposure, injection, secret, dependency and LLM/tool security risks. Use for a requested security review or a change affecting a trust boundary.

Write, revise or review an agent skill (SKILL.md package).

Use when explaining System V AMD64, ARM AAPCS, RISC-V psABI, stack frames, variadic calls, or FFI register rules. Not for the Rust FFI binding layer: use rust-ffi.

Use when the user wants to classify abstractions as useful, bad, or busy and keep one shallow level. Not for tasks requiring source or remote-system changes.

Use when configuring ADC sampling time, DMA-driven ADC, calibration, or DAC channel setup on bare-metal MCUs. Not for the DMA stream itself: use dma-baremetal.

Use when creating AF_XDP sockets, configuring UMEM and XSK rings, writing an XDP redirect program, or choosing copy versus zero-copy mode. Not for full kernel bypass: use dpdk.

Use when a completed session needs an agent-environment retrospective. Not for an engineering retrospective from telemetry: use engineering-retrospective.

Use when asked to audit or repair agent surfaces (plugins, agents, skills, CLAUDE.md/AGENTS.md, docs, prompts, commands, hooks) or improve one skill at depth. Not for agent grading: use skill-doctor.

Use when a redacted, trimmed agent transcript must be appended to a GitHub PR or issue body, with human approval and preview. Not for automated or model-initiated insertion.

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.

Scan for codebase bloat using 3-tier progressive analysis: dead code, duplication, God classes, and documentation waste.

Review the current working diff against the elegant-code decision ladder and propose deletions, honoring the negligence floor.

Suggest tier-1 filter commands for a log file before any compression or paste. Anchors on the log-debugging-hygiene module.

Analyze and optimize context window usage using MECW principles

Remove dead code, duplicate files, and unused dependencies with user approval at each step. Backs up before deleting.

The ONLY way to stop the egregore. Human-initiated graceful shutdown that saves all state.

Install the egregore watchdog daemon for automatic session relaunching

Show current egregore state and progress

Summon the egregore to autonomously process work items through the full development lifecycle. Runs indefinitely by default until dismissed.

Remove the egregore watchdog daemon and clean up files

Add or edit a knowledge annotation

Rebuild the knowledge base from the current codebase

Build, search, and query the code knowledge graph

Start or resume a guided onboarding path

Show challenge accuracy stats, weak areas, and streak

Run an ad-hoc gauntlet challenge session (5 questions, random scope)

Interactive interface to enable/disable rules

Convert Python SDK hooks to declarative rules

Display help and documentation

Create behavioral rules to prevent unwanted actions

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…