LLM Mart Basic
@llm-mart · Joined Jun 2026
Use when implementing a multi-file change, building a feature from a breakdown, or writing a large amount of code. Not for a single settled ticket: use work.
Use when writing or restructuring code, before adding a helper, wrapper, config key, or dependency, or when the user asks for minimal or DRY code. Not for performance tuning: use optimize.
Use when asked to audit comments in code files and propose structural replacements or deletions with per-candidate approval. Not for deterministic commented-out-code removal: use deslop.
Use when a human says "overhaul", "rebuild this subsystem", or "rewrite it from scratch". Not for thin-slice features: use incremental-implementation. Not for root-cause repair: use strike-the-root.
Use when asked to optimize code, speed up a path, reduce allocations, repair a regression, or profile a target. Not for remote, credential, publish, deploy, or irreversible changes.
Use when a request names a working principle (subtract before you add, idempotent operations, never block on the human) or asks which principle applies. Not for running a repair: use strike-the-root.
Use when modernizing APIs, removing compat shims, killing feature flags, or rewriting a subsystem cleanly. Not for additive refactors that must preserve the old path.
Use when a trusted bug or performance report needs reproduction and fix. Not for untrusted reports or scope beyond the named feature.
Use when an implementation has more workarounds than structure and another patch will not pay. Not for in-place re-derivation: use breaking-driven. Not for one-artifact rewrites: use rewrite-clean-v0.
Use when the user says "simplify this diff" or asks for a compression pass over a change-set. Not for dead-code sweeps: use deslop.
Use when an exact symbol, path, entrypoint, or line range can bound a focused code question or patch proposal under a fixed source budget. Not for source changes or broad repository exploration.
Use when writing or verifying framework-specific code, boilerplate, or a documented, correct implementation. Not for remote, credential, publish, deploy, or irreversible changes.
Use when a feature begins or specs are checked in: author or update behavioral specs and keep them current with what ships. Not for producing the initial approved spec and plan: use spec-driven.
Use when a bug, failure, flake, regression, review finding, or ticket needs the core fixed so it cannot recur. Not for greenfield features: use tdd. Not for style-only review or typo-class one-liners.
Use when the user says greenfield this or rescue this codebase, names a field (dark, red, blue, or brown), or diagnoses a subsystem. Not for specs: use to-spec. Not for remote or irreversible changes.
Use when an abstraction leak must be sealed as a module seam, configuration option, or explicit override, or exposed as a named boundary. Not for detecting concealment patterns: use no-hide.
Use when the user says "audit my code", "find all the bugs", "review until clean", or "grill my changes". Not for remote, credential, or irreversible changes.
Use when asked to audit an agent or AI feature for agentic-experience quality (AX review, agent-native critique, trust question). Not for source or remote-system changes.
Use when asked to determine what a change could break before it ships. Not for remote, credential, publish, deploy, or irreversible changes.
Use when a user wants to identify the true sources of complexity qualitatively before counting metrics. Not for source or remote mutation.
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.
/inspect
Inspect
`crabbox inspect` prints the full record for a single lease: state, provider,
/job
Job
Run named, repo-local jobs defined in your Crabbox config.
/list
List
`crabbox list` shows the current Crabbox machines (leases) for a provider. It is
/login
Login
`crabbox login` authenticates the CLI against a coordinator, stores the
/logout
Logout
`crabbox logout` clears the stored broker token from your user config so the CLI
/logs
Logs
`crabbox logs` prints the retained command output for a recorded run.
/marketplace
Marketplace
`crabbox marketplace` previews the Crabbox credits gateway: one Crabbox billing
/media
Media
`crabbox media` turns a recorded desktop video into lightweight review
/open
Open
`crabbox open` prepares an existing SSH-capable lease for an external editor.
/pause
Pause
`crabbox pause` pauses a single lease, freeing the remote compute while
/pond
Pond
`crabbox pond` is the cross-provider peer-discovery and lifecycle surface for a
/pool
Pool
`crabbox pool` contains machine-pool helpers. `pool list` keeps the older
/ports
Ports
`crabbox ports` bridges provider-native port publishing for an existing Crabbox
/prewarm
Prewarm
`crabbox prewarm` leases a reusable box and prepares it for test runs. For
/providers
Providers
`crabbox providers` prints the provider capability matrix that the CLI compiles
/receipt
Receipt
`crabbox receipt <run-id>` retrieves a brokered run's committed terminal
/results
Results
`crabbox results` prints the structured test summary attached to a recorded
/resume
Resume
`crabbox resume` resumes a lease previously paused with [`pause`](pause.md),
/run
Run
`crabbox run` syncs the current dirty checkout to a box, runs a command there,
/screenshot
Screenshot
`crabbox screenshot` captures a single PNG from a desktop lease without opening a
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
14 views 0 likesScale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.
17 views 0 likesThe 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.
19 views 0 likesTransform and optimize your markdown documentation for Large Language Models (LLMs) and RAG systems. Generate llms.txt automatically.
29 views 0 likes合乎周礼:DeepSeek-powered Zhouli-style Chinese translator, web app, and distributable Skill package.
27 views 0 likesThis is a fork of the https://dockbox.dev project I made
25 views 0 likes🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly.
28 views 0 likesA Python framework for modular, self-contained skill management for machines.
32 views 0 likesADHD — 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…
34 views 0 likesAI 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…
15 views 0 likesDeepSeek Harness Desktop App: a local AI desktop workspace for DSH Sessions, projects, files, web research, plugins, and Office artifacts.
13 views 0 likesAutonomous Offensive Security, Bug Bounty & Red Teaming Agent Framework powered by Hermes Agent, specialized reasoning skills, and multi-model LLM orchestration…
14 views 0 likes⌥ Coding agent with the IDE wired in
17 views 0 likesSupercharge AI Agents, Safely
33 views 0 likesX (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…
16 views 0 likesMy AI Stand. Realtime by day, rewriting itself by night. Summon my AI superpower.
14 views 0 likesOpen-source coding agent for your terminal, built in Rust and on a journey of continuous community improvement. Issues and PRs welcome.
15 views 0 likes观澜 / Guanlan:AI Agent 的中文互联网研究、阅读与信源路由工具。
13 views 0 likesMac 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…
15 views 0 likesSemantic version control => entity-level diffs, blame, and impact analysis on top of git. 28 languages via tree-sitter. Built for coding agents.
28 views 0 likes