LLM Mart Basic

AWS Secrets Manager for secure secret storage and rotation. Use when storing credentials, configuring automatic rotation, managing secret versions, retrieving secrets in applications, or integrating with RDS.

AWS EventBridge serverless event bus for event-driven architectures. Use when creating rules, configuring event patterns, setting up scheduled events, integrating with SaaS, or building cross-account event routing.

AI DevKit · Exchange information with active Codex, Claude Code, and other AI agents using ai-devkit agent list, detail, and send. Use when an agent needs to find another active agent, read its recent context, send it information, or request information back.

AI DevKit · Manage running AI agents with ai-devkit agent commands. Use when an agent needs to identify itself, list agents, start workers, inspect agent detail, assign work, group agents, resume sessions, stop agents, or delegate work to other agents.

AI DevKit · Supervise multi-agent workflows over repeated passes: poll progress, unblock waiting agents, coordinate dependencies, relay outputs, resolve conflicts, and verify completion. Use only for ongoing multi-agent coordination, not one-off list/detail/send/start/kill action

AI DevKit · Use when the user asks to brainstorm, ideate, generate ideas, expand options, challenge ideas, pressure-test ideas, compare concepts, narrow choices, name something, plan content angles, explore strategy, evaluate product ideas, technical approaches, experiments, or d

AI DevKit · Update CHANGELOG.md Unreleased items from git commits since the latest release. Use when users ask to update changelog/release notes from recent commits, with one concise line per commit and commit/PR links.

AI DevKit · Safe git commit workflow for AI coding agents. Use when the user asks to commit, prepare a commit, stage changes, create a PR-ready checkpoint, or finish work with a conventional commit while avoiding unrelated user changes.

AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.

AI DevKit · Orchestrator for structured SDLC phase skills. Use when the user wants to run the full lifecycle or choose the next phase across requirements, design, planning, implementation, testing, and review.

AI DevKit · Planning phase guidance for creating and reconciling feature task plans. Use when the user wants to create an implementation plan, update planning docs, mark task progress, capture blockers or new tasks, or run dev-lifecycle planning work.

AI DevKit · Publish a ready feature branch for review. Use when the user wants to sync, push, and open or update a code review request on GitHub, GitLab, or another Git host.

AI DevKit · Requirements phase guidance for starting features and reviewing requirements. Use when the user wants to capture a new requirement, clarify product scope, initialize feature docs, review requirements, or run dev-lifecycle phases 1-2.

AI DevKit · Final code review phase guidance for holistic pre-push review. Use when the user wants code review, final lifecycle review, design alignment checks, integration risk review, or dev-lifecycle phase 9.

AI DevKit · Testing phase guidance for adding and validating feature test coverage. Use when the user wants to write tests, update testing docs, run coverage, close coverage gaps, or run dev-lifecycle phase 8.

AI DevKit · Worktree setup and resume guidance for isolated feature work. Use when starting, resuming, switching, or verifying a feature branch/worktree for lifecycle, debugging, implementation, review, or multi-agent workflows.

AI DevKit · Document a code entry point with structured analysis, dependency mapping, and saved knowledge docs. Use when users ask to document, understand, or map code for a module, file, folder, function, or API.

AI DevKit · Use the memory CLI as a durable knowledge layer. Search before non-trivial work, store verified reusable knowledge, update stale entries, and avoid saving transcripts, secrets, or one-off task progress.

AI DevKit · Systematic structural or multi-file refactors across any stack while preserving behavior and public contracts. Use for reorganizing modules, boundaries, naming, APIs/contracts, staged refactor plans, or refactor risk review.

AI DevKit · Review code, skills, and prompts for security vulnerabilities — OWASP Top 10, prompt injection, business logic flaws, and insecure defaults. Use when reviewing PRs, auditing modules, reviewing AI skills/prompts, or preparing for release.

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.

Intelligent issue resolution with multi-agent debugging, root cause analysis, and verified fix implementation

Scaffold a TypeScript project (Next.js, React with Vite, Node.js API, or library) with pnpm, testing, and dev tooling

Build AI assistant application with NLU, dialog management, and integrations

Create LangGraph-based agent with modern patterns

Optimize prompts for production with CoT, few-shot, and constitutional AI patterns

Run the eval-gated fine-tuning lifecycle end to end — eval harness, method selection, data, environment, training, checkpoint gate, export

Re-gate an existing fine-tuned checkpoint against the current eval harness and export it on PROMOTE

Orchestrate specialized agents to build a production ML pipeline from data analysis through training, deployment, and monitoring

Quick gallery search. Use when user runs /meigen-ai-design:find with keywords to browse inspiration.

Quick image generation. Use when user runs /meigen-ai-design:gen with a prompt. Skips intent assessment, generates directly.

Orchestrate cross-platform feature development across web, mobile, and desktop with API-first architecture

Set up monitoring and observability with Prometheus metrics, Grafana dashboards, distributed tracing, log aggregation, and alerting

Implement SLOs with SLI selection, error budgets, burn-rate alerting, dashboards, and reporting

Run an AI-assisted code review that combines static analysis tools with AI review of security, performance, and architecture

Coordinate specialized review agents in parallel or in sequence and synthesize their findings into one code review

Full quality certification with badge

Compare two skills head-to-head

Evaluate a plugin or skill for quality

Verify every receipt in ./receipts/receipts.jsonl against the signer's public key. Detects tampered or malformed receipts across the audit trail.

Verify a single Ed25519-signed receipt file against the signer's public key. Returns exit 0 if valid, 1 if tampered, 2 if malformed or the key is missing.

Open-source super AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-model, multi-channel. Lightwei…

Open-source AI browser agent for web automation: a web browsing agent and computer-use agent in plain English. Browser MCP for Claude Code and Gemini CLI.

Agent-native trading terminal built on DeepSeek Harness. Crypto, US, CN and HK in one three-column GUI, 19+ hot-swappable connectors, dry-run by default with hu…

Open-source desktop and web coding agent with a first-party host and harness, durable sessions, model connections, streaming chat, and workspace tools.

OrcaReplay — Time travel for AI agents. Record, replay, fork, and debug any agent run with any model. Built by the OrcaRouter.ai team.

A complete toolkit for connecting R and LLMs

OpenSRE — the memory-first AI SRE. Self-hosted incident investigation with episodic memory, knowledge graph, web console, Slack & Teams. opensre.in

A desktop AI coding assistant that works with your local projects. Ally helps you understand code, edit files, search a workspace, manage tasks, and complete de…

【在线免费使用】 简单快速将SQL或DBML转换为美观的ER图（支持 Agent Skill）/ The best SQL to ER Diagram converter (Support Agent Skill).

Operation-bound protection for autonomous AI agents: contain and verify operations before committing changes, understand intent and risk across long-running wor…

Local-first AI coding agent desktop: Electron + Rust host core + pi Agent Harness + user-installable plugins

Local-only Go static analysis engine with a built-in MCP server. Gives AI coding agents deterministic structural awareness: call graphs, impact analysis, symbol…

Self-hosted AI agent workspace with tool calling, MCP, multi-model routing, sandboxed execution, multi-agent workflows, and LLM-authored 3D character animation…

Automated TDD enforcement for Claude Code

High-performance platform for building websites, e-commerce, and web applications—with Native AI, JavaScript development, and a marketplace for portable sites a…

Autonomous AI-agent orchestration engine for job discovery with decision traces, tool adapters, and production-grade run control.

Token efficient Claude Code full Python rebuild. AI Coding Agent in 310K LoC Python.

OPC — One Person Company. A full team in a single Claude Code skill. Adaptive agent orchestrator with 21 built-in roles, 6 flow templates, and adversarial quali…

Open-source, secure environment with real-world tools for enterprise-grade agents.

MCP server for AI-powered GitHub project management — agent orchestration, PRD-to-issues pipeline, sprint planning, and multi-agent swarm coordination