LLM Mart Basic

Apply MCAF non-functional-requirements guidance to capture or refine explicit quality attributes such as accessibility, reliability, scalability, maintainability, performance, and compliance. Use when a feature or architecture change needs explicit quality attributes and trade-of

Apply MCAF source-control guidance for repository structure, branch naming, merge strategy, commit hygiene, and secrets-in-git discipline. Use when bootstrapping a repo, tightening PR flow, or documenting branch and release policy.

Apply MCAF UI/UX guidance for design systems, accessibility, front-end technology selection, and design-to-development collaboration. Use when bootstrapping a UI project, choosing front-end stack, or tightening design and accessibility practices.

Build or consume Model Context Protocol (MCP) servers and clients in .NET using the official MCP C# SDK, including stdio, Streamable HTTP, tools, prompts, resources, and capability negotiation.

Use Metalint in .NET repositories that ship Node-based frontend assets and want one CLI entrypoint over several underlying linters. Use when a repo wants to orchestrate ESLint, Stylelint, HTMLHint, and related frontend checks from a single checked-in `.metalint/` configuration.

Use the open-source free `Meziantou.Analyzer` package for design, usage, security, performance, and style rules in .NET. Use when a repo wants broader analyzer coverage with a single NuGet package.

Build .NET AI agents and multi-agent workflows with Microsoft Agent Framework using the right agent type, threads, tools, workflows, hosting protocols, and enterprise guardrails.

Use the Microsoft.Extensions stack correctly across Generic Host, dependency injection, configuration, logging, options, HttpClientFactory, and other shared infrastructure patterns.

Build provider-agnostic .NET AI integrations with `Microsoft.Extensions.AI`, `IChatClient`, embeddings, middleware, structured output, vector search, and evaluation.

Design and implement Minimal APIs in ASP.NET Core using handler-first endpoints, route groups, filters, and lightweight composition suited to modern .NET services.

Work on C# and .NET-adjacent mixed-reality solutions around HoloLens, MRTK, OpenXR, Azure services, and integration boundaries where .NET participates in the stack.

Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations.

Write modern, version-aware C# for .NET repositories. Use when choosing language features across C# versions, especially C# 13 and C# 14, while staying compatible with the repo's target framework and `LangVersion`.

Write, run, or repair .NET tests that use MSTest. Use when a repo uses `MSTest.Sdk`, `MSTest`, `[TestClass]`, `[TestMethod]`, `DataRow`, or Microsoft.Testing.Platform-based MSTest execution.

Implement the Model-View-ViewModel pattern in .NET applications with proper separation of concerns, data binding, commands, and testable ViewModels using MVVM Toolkit.

Use the open-source free `NetArchTest.Rules` library for architecture rules in .NET unit tests. Use when a repo wants lightweight, fluent architecture assertions for namespaces, dependencies, or layering.

Write, run, or repair .NET tests that use NUnit. Use when a repo uses `NUnit`, `[Test]`, `[TestCase]`, `[TestFixture]`, or NUnit3TestAdapter for VSTest or Microsoft.Testing.Platform execution.

Build or review distributed .NET applications with Orleans grains, silos, persistence, streaming, reminders, placement, transactions, serialization, event sourcing, testing, and cloud-native hosting.

Use the free official .NET diagnostics CLI tools for profiling and runtime investigation in .NET repositories. Use when a repo needs CPU tracing, live counters, GC and allocation investigation, exception or contention tracing, heap snapshots, or startup diagnostics without GUI-on

Create or reorganize .NET solutions with clean project boundaries, repeatable SDK settings, and a maintainable baseline for libraries, apps, tests, CI, and local development.

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.

Periodic multi-reviewer sweep of the whole codebase — surfaces a triaged checkpoint report.

Sanctioned lane for non-behavioral work — docs-only edits, dependency bumps, reverts. Type-scaled gates; no TDD demanded of prose.

{{SKILL_ENTRY:post-merge-cleanup}}

Show the codeArbiter command catalog — the public command list and what each routes to.

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Stop everything and surface a rule conflict — persona vs. docs vs. code. Present both sides and the conflict-hierarchy level; the user resolves. No silent reconciliation.

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Verify the active host install, package, command ownership, enforcement{{IF:pi}}, wrapper self-test, and active-dispatch coverage gap{{ELSE}}, and harmless live-fire probe{{END}}. Read-only.

Start a feature: brainstorm a spec, get it approved, then drive it test-first through the pipeline. The one entry to implementation.

Fix a confirmed bug: a failing regression test first, then a minimal fix, then the rest of the tdd gates.

Opt this repo into codeArbiter — scaffold the root-level .codearbiter/ state store.

Read-only 3-metric governance glance — override rate, small-lane rate, sprint low-confidence ratio — each with a trend arrow vs. the prior 20-commit window.

Sanctioned, logged bypass of a gate or hard rule — one audit line, then proceed.

{{SKILL_ENTRY:finishing-a-development-branch}}

Zero-onboarding, read-only dry-run of the reviewer fleet against the current uncommitted diff. Predicts reviewers, runs the state-free secret scan, writes nothing.

Trim transcript clutter to extend session lifetime — analyze, prune a copy, or toggle the after-each-turn service. Dry-run by default; gains land at resume/compaction, not the current turn.

{{SKILL_ENTRY:decision-variance}}

Your efficient agentic AI coding CLI assistant

AI coding with Aegis governance built into execution: baseline-aware changes, evidence-backed delivery. Free desktop client, your choice of model. 将哲科思维融入 AI 开发…

Android in docker solution with noVNC supported, video recording, mcp server and AI-agent

Your AI assistant, built for the agentic era. Open source and local: talk to it, and it runs your agents, your coding CLIs, your browser and your apps. Windows,…

Verifiable program-level self-evolution for AI-for-Science agents: Skills and typed Operators grow from replay-verified executions.

Let Claude Code and other coding agents drive and debug Chrome from the shell. DOM, network, console and raw CDP as short commands, no screenshots and no MCP ne…

Unofficial, agent-friendly Mercadona shopping CLI (Go) — search products, read prices, build a cart and prepare checkout from the terminal. BYO credentials.

Local-first AI canvas for visual workflows, AI short drama, image/video generation, storyboarding and asset management. ComfyUI, AI agents, MCP & Blender. 本地优先…

Let any AI coding tool — Claude Code, Cursor, Codex — drive your real Chrome. One-prompt setup, muscle memory, local-first.

runs anywhere. uses anything

Open-source dots for the web: an AI agent with its own browser, one that does not get blocked.

Free MIT AI coding agent — no API key needed. Built-in free model, or bring Claude, GPT, Gemini, DeepSeek, Ollama +50 more. Swarm mode, 40+ tools, browser & des…

Open-source AI customer support system. AI-first support, human-ready operations.

A durable, multiplayer, mobile-first web app for Pi agents, built on Pi Durable

Embedded Cypher knowledge graph for Python and Rust. Bundled MCP server, describe() schema, and code-graph parser for LLM agents.

Turn PDFs, books and papers into interactive learning webpages｜将复杂材料转化为可追溯、可测验、可做笔记的学习网页

PhysicsOS 是一个面向初高中物理学习的公益可视化智能体，通过 AI 理解题目并结合物理引擎，将抽象物理过程转化为可交互、可观察、可计算、可验证的真实物理场景。

Open-source, self-hosted alternative to OpenAI Dots, Meta Muse, Grok Bot, Manus Cue and Claude Cowork. AI agents, each with a virtual machine on your PC with a…

Quant infrastructure for AI agents — a free, open-source desktop workspace for macOS and Windows where your Claude Code or Codex turns a trading idea into a bac…

Build stateful agent workflows with typed outputs, reusable tools, session forks, and ordinary TypeScript.