LLM Mart Basic
@llm-mart · Joined Jun 2026
Use when asked to validate a web app, CLI, API, or generated artifact against a source-blind behavior contract. Not for source or remote-system changes.
Use when building static archives with ar, stripping or converting binaries, mapping crash addresses with addr2line, or demangling C++ symbols. Not for ELF analysis: use elf-inspection.
Use when asked to determine what a change could break before it ships. Not for remote, credential, publish, deploy, or irreversible changes.
Use when the user names one book, course, paper, or source document and asks to distill it into a reusable skill. Not for a folder of sources: use map-corpus.
Use when writing a custom bootloader, jumping to application code, relocating VTOR, or implementing DFU/USB firmware update on Cortex-M. Not for reset-to-main: use baremetal-startup.
Use when C or C++ code needs memory-safety or undefined-behavior guarantees proved with CBMC, or ACSL contracts checked with Frama-C Eva or WP. Not for choosing the proof policy: use proof-driven.
Use when explaining branch predictors, mispredict penalties, speculative execution, Spectre or Meltdown mitigations, or branchless code. Not for pipeline stage theory: use cpu-pipelines-and-hazards.
Use when the user asks for branded or style-governed output. Not for remote, credential, publish, deploy, or irreversible changes.
Use when bloated code needs clean re-derivation, or the user says "this module is bloated" or "break it and rebuild". Not for untracked data or changes without VCS rollback.
Use when the user runs /browser-cookie-store to populate the session cookie store from installed browsers. Not for remote, credential, publish, deploy, or irreversible changes.
Use when the user runs /browser-qa for report-only QA results without entering a fix loop. Not for remote, credential, publish, deploy, or irreversible changes.
Use when building, debugging, or verifying browser-rendered code, or running browser tests for PR- or branch-affected pages. Not for source, remote-system, credential, publish, or deploy changes.
Use when reducing C/C++ compilation times with ccache, sccache, distcc, unity builds, precompiled headers, split DWARF, IWYU, or link time reduction.
Use when asked to analyze a Burp Suite .burp project for audit items, request/response metadata, or captured traffic. Modes: parsed (default) and stream. Not for source or remote-system changes.
Use when product copy needs buyer-objection evidence collected through approved, consented outreach. Not for unsolicited outreach or survey design.
Use when the user wants the current jargon weather of a domain described without advocacy. Not for choosing a positioning move: use buzzword-hijack.
Use when a user wants to choose and execute a bounded positioning move that rides a jargon wave. Not for describing the jargon weather of a domain: use buzzword-analysis.
Use when C code is written or audited and needs a pure-C baseline: standard, undefined behavior, integer and buffer safety, sanitizers, fuzzing, build flags. Not for C++: use modern-cpp-practices.
Use when the user requests a userspace C or C++ security review with a threat model and severity filter and wants validated findings. Not for kernel or bare-metal code: use kernel-security.
Use when the user asks "where to help", "contribution opportunities", or "find a good first issue". Returns data-backed first steps. Not for PR review queues: use gh-review-requests.
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.
/update
Update
CoalWash self-update — check for a newer version and offer to apply it, or set how updates are handled.
/create-skill
Create skill
Create an AI skill from any source (URL, repo, PDF, video, notebook, etc.)
/install-skill
Install skill
One-command skill creation and packaging for a target platform
/sync-config
Sync config
Sync a scraping config's URLs against the live documentation site
/mc-validate
Mc validate
Generate and run validation queries for the current change
/mc-validate
Mc validate
Generate and run validation queries for the current change
/setup-code-intelligence
setup-code-intelligence
Check code-intelligence prerequisites (ripgrep + a language server) and print install hints
/ci-mockup-figure
Ci mockup figure
Create space-efficient paper and proposal figures (HTML mockups, TikZ, or skia-canvas) from tool selection through LaTeX insertion
/editable-figure
Editable figure
Design concise overview, mechanism, or workflow figures as editable PowerPoint objects
/implement-review
Implement review
Run the implement-review staged-change review loop
/my-router
My router
Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill
/prun
Prun
Run prun: parallel delegation fan-out on Agy workers (the session coordinates)
/readme-polish
Readme polish
Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim
/vet
Vet
Vet the staged change: run the implement-review review loop (short alias)
/ci-mockup-figure
Ci mockup figure
Create space-efficient paper and proposal figures (HTML mockups, TikZ, or skia-canvas) from tool selection through LaTeX insertion
/editable-figure
Editable figure
Design concise overview, mechanism, or workflow figures as editable PowerPoint objects
/implement-review
Implement review
Run the implement-review staged-change review loop
/my-router
My router
Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill
/prun
Prun
Run prun: parallel delegation fan-out on Agy workers (the session coordinates)
/readme-polish
Readme polish
Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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8 views 0 likes小红书/抖音/快手/视频号/B站 自媒体账号体检+爆款拆解工具。扫同赛道找对标、拆爆款为什么爆、诊断为什么没人看,顺手出可粘贴仿写初稿。支持带货电商模式。支持codex, claude code, workbuddy
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