LLM Mart Basic

Designs interfaces that survive their consumers — resource modeling, errors, versioning, pagination, and compatibility. Use this to design a new API, review one before it ships, decide how to version or deprecate, fix an interface consumers keep misusing, or work out whether a ch

Isolates feature work in its own branch or worktree and integrates it cleanly when done. Use this when starting work that should not disturb the current workspace, when several efforts must proceed in parallel on one repository, or when implementation is finished and the change n

Owns architecture, engineering delivery, infrastructure, data platform, and internal systems. Use this for build-versus-buy calls, technology selection, architectural direction, engineering capacity and delivery risk, technical debt tradeoffs, platform and tooling decisions, or w

Designs and runs cloud infrastructure — environments, infrastructure as code, networking and isolation, scaling, and cost. Use this to design a cloud environment, control infrastructure spend, set up environment separation, plan for scale or region failure, or review infrastructu

Conducts and responds to code review — reviewing a change for correctness, design, and risk, and evaluating review feedback received on your own work. Use this before merging, when asked to review a diff or pull request, when review feedback has arrived and needs acting on, or wh

Verifies that work is actually complete before it is claimed to be — running the checks, reading the output, and confirming the original request was satisfied rather than approximated. Use this before saying something is done, fixed, or passing; before committing or opening a pul

Turns a spec or requirement into a written plan a separate session or agent can execute, then drives that plan through review checkpoints. Use this before touching code on any multi-step task, when work needs handing to someone else, when a task keeps sprawling mid-implementation

Makes systems debuggable and reliably operable — instrumentation, alerting that is worth waking for, service objectives, and learning from failure. Use this to instrument a service, fix alerting that is ignored, set error budgets or reliability targets, prepare for on-call, or ru

Splits work across multiple agents or sessions running at once, keeping their surfaces disjoint so results merge cleanly. Use this when facing several independent tasks with no shared state, when a plan has parallelizable steps, when a broad search or audit would be faster fanned

Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models. Use this when a prompt is not producing what was wanted, when output varies run to run, when writing a prompt for a repea

Ships changes safely and often — pipelines, deployment strategies, feature flags, rollback, and database changes. Use this to design a deployment pipeline, reduce release risk, roll out a risky change gradually, plan a schema migration, or work out why releases are infrequent and

Writes and revises agent skills so they trigger at the right moments and give usable instruction when they do. Use this when creating a new skill, editing an existing one, diagnosing a skill that fires too often or never fires, or reviewing a set of skills for overlap. Also use b

Designs system structure and makes architectural decisions defensible — boundaries, coupling, trade-offs, and recording why. Use this to design a new system or major component, choose between architectural options, review an existing design, decide where a boundary belongs, or do

Explores the problem and the range of possible approaches before any code is written — clarifying what is actually being asked, surfacing options with their tradeoffs, and converging on one. Use this at the start of any feature, component, or behavior change, when a request is am

Finds the root cause of a bug, test failure, or unexpected behavior before proposing any fix. Use this whenever something is broken and the cause is not yet proven — a failing test, a production error, intermittent behavior, or a symptom that appeared after a change. Also use whe

Makes technical debt visible and decidable — distinguishing real debt from mess, quantifying its cost, and arguing for remediation in business terms. Use this to assess and prioritize debt, decide whether to fix or live with something, justify remediation work to non-engineers, o

Drives implementation by writing a failing test first, then the smallest code that passes it. Use this before writing implementation code for any feature or bugfix, when a bug needs a regression test, when existing code is hard to change safely, or when someone asks whether a cha

Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.

The CIO's remit — running the technology the company works on, service quality, IT spend, and the boundary with product engineering. Use this to set IT priorities, decide what IT owns versus engineering, structure IT spend or an IT roadmap, judge whether to build, buy or outsourc

Manages laptops, desktops and mobile devices — enrollment, configuration, patching, software distribution, and lost or compromised devices. Use this to set up device management, standardize builds, roll out software or an OS upgrade, handle a lost device, or bring an unmanaged fl

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.

Require justification for new files (short for /ar:justify)

Require justification before creating new files

Block a command pattern in this session

Allow a blocked pattern in this session

Plan export status and settings [on|off|globalon|globaloff|dir <path>|pattern <template>|rejected [on|off|dir <path>]|reset]

Universal System Design Philosophy - the 17 core principles, short for /ar:philosophy

Plan export status and settings [on|off|globalon|globaloff|dir <path>|pattern <template>|rejected [on|off|dir <path>]|reset]

Create a structured plan, short for /ar:plannew

Execute an approved plan step by step, short for /ar:planprocess

Critique and improve an existing plan, short for /ar:planrefine

Start autoproc - procedural autonomous workflow

Sync an existing plan with the codebase, short for /ar:planupdate

Force-reload all integration rules from config files

Restart the daemon for the current autorun install/source tree

Start autorun - autonomous task execution

Emergency stop - immediately halt all actions (short for /ar:estop)

Show current AutoFile policy status (short for /ar:status)

Show current AutoFile policy and settings

Graceful stop - finish current task then stop autorun

Inspect tasks or configure pause, prompts, recovery, and ignore behavior

An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间，让人与智能体在此协作

Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.

The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.

Transform and optimize your markdown documentation for Large Language Models (LLMs) and RAG systems. Generate llms.txt automatically.

合乎周礼：DeepSeek-powered Zhouli-style Chinese translator, web app, and distributable Skill package.

This is a fork of the https://dockbox.dev project I made

🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly.

A Python framework for modular, self-contained skill management for machines.

ADHD — a skill for coding agents. Tree-of-thought with pruning, built on the Claude & Codex Agent SDK. Fans out parallel divergent thoughts under different cogn…

AI Agent 驱动的开源可自部署视频工作台：将小说与剧本转为角色、场景、道具资产、分镜、视频和剪映草稿，支持跨镜头一致性、多供应商与费用追踪 | Self-hosted AI video workspace for stories, storyboards and short-form video producti…

DeepSeek Harness Desktop App: a local AI desktop workspace for DSH Sessions, projects, files, web research, plugins, and Office artifacts.

Autonomous Offensive Security, Bug Bounty & Red Teaming Agent Framework powered by Hermes Agent, specialized reasoning skills, and multi-model LLM orchestration…

⌥ Coding agent with the IDE wired in

Supercharge AI Agents, Safely

X (Twitter) Scraper API and X API Alternative. You do not need an official X developer account. You do not need to connect or use an X account for supported scr…

My AI Stand. Realtime by day, rewriting itself by night. Summon my AI superpower.

Open-source coding agent for your terminal, built in Rust and on a journey of continuous community improvement. Issues and PRs welcome.

观澜 / Guanlan：AI Agent 的中文互联网研究、阅读与信源路由工具。

Mac Agent for macOS 26: the agentic AI harness for your Mac Desktop. Computer use, automation, scripting, coding, and more. Powered by 18+ providers across loca…

Semantic version control => entity-level diffs, blame, and impact analysis on top of git. 28 languages via tree-sitter. Built for coding agents.