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.

Improve an existing agent through performance baselines, prompt engineering, A/B testing, and staged rollout

Optimize multi-agent system performance through profiling, context window tuning, coordination efficiency, and cost and latency tradeoffs

Debug issues using competing hypotheses with parallel investigation by multiple agents

Task delegation dashboard for managing team workload, assignments, and rebalancing

Develop features in parallel with multiple agents using file ownership boundaries and dependency management

Launch a multi-reviewer parallel code review with specialized review dimensions

Gracefully shut down an agent team, collect final results, and clean up resources

Spawn an agent team using presets (review, debug, feature, fullstack, research, security, migration) or custom composition

Display team members, task status, and progress for an active agent team

Build realistic API mock servers with request stubbing, dynamic data, test scenarios, and contract testing

Orchestrate end-to-end application performance optimization from profiling to monitoring

Orchestrate end-to-end feature development from requirements to deployment

Set up PreToolUse hook to block --no-verify and other git bypass flags in Claude Code projects

Generate comprehensive C4 architecture documentation (Context, Container, Component, Code) for a codebase using bottom-up analysis and four coordinated C4 agents.

Automate CI/CD pipelines, releases, and development workflows with GitHub Actions, pre-commit hooks, and infrastructure automation

Explain complex code, algorithms, and design patterns with step-by-step breakdowns, visual diagrams, and interactive examples

Generate API, architecture, code, and user documentation from a codebase and automate keeping it current

Restore saved project context and decisions to resume a session

Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.

Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.

Persistent session memory for AI coding agents — local-first, with on-device inference, associative recall, and drift detection. Works with Claude Code, Cursor,…

Run Hermes Agent and OpenClaw on the same WeChat account

An AI co-scientist running on your desktop. Claude Science but better.

Emotion Ball 是一套面向 AI 助手的表情引擎:32 种状态表情全部由纯 SVG 与原生 JavaScript 实时驱动,零框架、零图片资源。AI 侧只需输出一个 emotionId,小球即可切换到对应表情,可直接用作聊天机器人、桌面宠物、悬浮助手的情绪表达层。

Agent communication SDK. The open-source agent communication layer for AI agents — email, WhatsApp, Slack, Discord, Telegram, SMS. Python & TypeScript.

The micro-VM for AI agents — light enough to embed on your laptop, elastic enough to power an agentic cloud.

Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.

Webhook integration skills for AI coding agents (Claude Code, Cursor, Copilot). Step-by-step guidance for setting up webhook receivers, signature verification,…

Token burn reducer and focus keeper for Claude Code, Codex, Copilot, Gemini CLI, and more: surgical read hints, PDF/Office/CSV/markdown file interception, 160+…

RuvNet Brain — a downloadable, source-grounded brain for Claude Code over Reuven Cohen's (rUv's) RuvNet stack: RuVector/RVF, Ruflo, AgentDB, RuLake, SPARC + 21…

💼 One MCP server to search job boards and company career sites

C++ MCP SDK - build Model Context Protocol (MCP) servers and clients in C++ / CPP. Enterprise-grade security, observability, connectivity. Stdio, HTTP+SSE, Stre…

Open-Source AI Presentation Generator and API (Gamma, Canva, Beautiful AI, Decktopus, Presentations AI Alternative)

Secure, Fast, and Extensible Sandbox runtime for AI agents.

Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.

Browser Harness | Self-healing harness that enables LLMs to complete any task.

The World's First Agentic IDE. Visual dashboard: live sessions, task management, code editor, terminal. Epic Swarm parallel workflows. Auto-proceed rules. Autom…

Agenta is a workspace where you and your team build agents and automations.

Terminal Director. One lightweight app, eight features, your whole dev workflow in a single window.

AI turns documents or topics into real, native PowerPoint decks—with native shapes, transitions and animations, data-backed charts and tables on demand, audio n…