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.
/know
Know
What do I know about a topic
/link
Link
Find and add missing connections
/lint
Lint
Audit structure and repair what is mechanical
/merge
Merge
Merge two pages
/metrics
Metrics
Record a dated metrics snapshot
/monthly
Monthly
The monthly structural review
/orphans
Orphans
Find unreachable pages
/outline
Outline
Outline a piece from your concept pages
/privacy
Privacy
Audit what should not be in the vault
/project-status
Project status
Where a project stands
/project
Project
Create a project
/prune
Prune
Find what is safe to remove
/publish
Publish
Check what is safe to publish
/quiz
Quiz
Test yourself on your own pages
/rename
Rename
Rename a page and fix every link
/report
Report
Write a research report
/retype
Retype
Fix a page's type
/review
Review
Periodic review of what the vault learned
/rollback
Rollback
Undo the last run
/schedule
Schedule
Set up scheduled maintenance
Open-source, self-hosted AI media server. One server replaces your entire media stack, with a web app, native iPhone app, and an AI agent that gets things done.…
3 views 0 likesA curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.
3 views 0 likes🦦 Crayotter: A Multimodal AI-Agent for Video-Editing, Video-Composing, and Video Production. Powered by Multimodal LLMs for autonomous Text-to-Video agentic fr…
3 views 0 likesWebextension tool for Odoo
3 views 0 likes基于多模态视觉感知与 LLM Agent 的 macOS 微信自动化框架 | Visual RPA for WeChat
0 views 0 likesThe operational superset of Pi Coding Agent — everything Pi, plus observability, governance, recovery, evaluation and multi-agent orchestration. Pi Coding Agent…
0 views 0 likesMetrik 可以集中查看本机多个 Agent 的配额余量和 Token 消耗,目前支持 ChatGPT、Claude、GLM、Kimi 等主流 AI 服务。
0 views 0 likesAn AI agent with a real self — soul she wrote, desires that drive her, a heartbeat for autonomous action, dreams she processes when you're away. Capability supe…
0 views 0 likesProduction-ready open source terminal coding agent with readable, layered code: permission rules, OS sandboxing, MCP, skills, sub-agents, and Anthropic, OpenAI-…
0 views 0 likesAnswer me with HTML — an agent skill that answers hard questions with a one-page HTML you can actually read. 让 AI Agent 用一页 HTML 回答复杂问题。
1 views 0 likesPrompt packs that make any AI agent a LaTeX expert — fix errors, polish writing, format for venues, read papers, recover source
1 views 0 likesClaude Code plugin: universal radial-tree exploration engine. One tree skill + swappable presets (brainstorm / attack / design / code-audit) for divergent ideat…
1 views 0 likesClaude Code + OpenClaw + Codex + WorkBuddy 中文教程 | 50篇完整教程 + 1张速查卡 | 80万+内容量 | 1500+实操示例 | AI Coding / Agent 四线学习路径(编程+助手+Agent+办公)
1 views 0 likesSmartLabelBench 2026: LLM-Powered Auto Annotation and Dataset Builder for Everything
1 views 0 likes从零开始玩转OpenClaw:最全面的中文教程,涵盖安装、配置、实战案例和避坑指南(github版)
1 views 0 likesNative Android GUI for running AI coding agents locally on-device. No terminal or PC required.
0 views 0 likes基于 LangChain/LangGraph 的 ReAct Agent ,结合 RAG、工具调用与 Streamlit 界面,面向智能客服与报告生成场景。
0 views 0 likesAn open-source, local-first AI learning workbench
2 views 0 likesOpen-source coding agent for your terminal, built in Rust and on a journey of continuous community improvement. Issues and PRs welcome.
2 views 0 likesMatt Pocock 技能集的中文翻译版 — 地道中文,原汁原味的技术术语。基于 mattpocock/skills 复刻。每日中午12点钟同步
2 views 0 likes