LLM Mart Basic
@llm-mart · Joined Jun 2026
When GitHub Actions fails, fetch failing job logs and assign each failing job to a separate subagent that fixes its slice of the problem in parallel. Use for multi-job CI failures where jobs are independent.
Run four parallel read-only subagents that each review the same diff from a different lens — security, performance, correctness, and readability — then merge findings into one report. Use before merging large or risky PRs.
Explore a large codebase in parallel by launching multiple explore subagents that each investigate a different area simultaneously. Use when onboarding onto a new project, understanding architecture, or investigating a cross-cutting concern.
When multiple tests fail, assign each failing test file to a separate subagent that fixes it independently in parallel.
Profile a running web application's CPU performance using Cursor's built-in browser profiler. Captures call stacks, identifies slow functions, and suggests optimizations. Use when a page feels slow or janky.
Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.
Test-driven development in Python using uv as the package manager. Covers the red-green-refactor cycle, vertical slicing, and uv project setup.
Build mobile apps with React Native and Expo — navigation, platform-specific code, performance, and native modules.
Execute a user flow step-by-step in Cursor's built-in browser while documenting each action, then emit a Playwright test that replays the same flow using stable selectors derived from the accessibility tree.
Open the app in Cursor's browser at multiple viewport sizes, screenshot each, and report any layout breakage.
Perform a thorough code review focused on correctness, maintainability, performance, and best practices.
Automatically persist useful context — research, decisions, learnings, templates — to workspace files so knowledge survives across conversations.
Generate a visual changelog or PR description by taking before/after screenshots of UI changes using Cursor's built-in browser. Use when preparing a PR with visual changes.
Audit technical SEO — meta tags, structured data, Open Graph, sitemaps, robots.txt, performance, and accessibility signals.
Set up a GitHub Actions CI/CD pipeline with linting, testing, type-checking, and deployment steps.
Set up Terraform infrastructure-as-code for cloud resources, including provider configuration, modules, state management, and CI integration.
When the user keeps asking for the same check to run (lint, tests, type-check), suggest a Cursor hook to automate it.
When the user repeats the same correction or convention multiple times, suggest a Cursor rule to encode it permanently.
When the user struggles with a task that a known skill could handle, suggest installing it.
Switch the current Cursor workspace to a different project directory using the cursor-app-control MCP. Use when the user asks to switch projects, open another repo, jump to a different codebase, or move to a worktree.
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.
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.
/review
Review
Command dispatcher for review.
/tool
Tool
Command dispatcher for tool.
/browser-cdp
Browser cdp
浏览器操控。通过 CDP 协议控制 Chrome,复用已有登录态,执行浏览器自动化操作。
/story-cover
Story cover
网文封面生成。分析书名题材,生成专业封面图。
/story-deslop
Story deslop
网文去AI味。检测并清除文本中的AI写作痕迹,让文字回归自然。
/story-import
Story import
逆向导入已有小说。将已写好的小说反向解析为标准项目目录结构。
/story-long-analyze
Story long analyze
长篇网文拆文。深度拆解爆款长篇小说的黄金三章、人设、爽点、节奏。
/story-long-scan
Story long scan
长篇网文扫榜。分析起点、番茄、晋江等平台排行数据,提炼市场趋势。
/story-long-write
Story long write
长篇网文写作。从大纲到正文,辅助长篇网络小说的创作。
/story-review
Story review
多视角对抗式审查。使用多个 Agent 对作品进行多维度审稿。
/story-setup
Story setup
网文写作环境部署与检查。部署 hooks、rules、agents、项目指令等基础设施;传入 check 只检查不改动。
/story-short-analyze
Story short analyze
短篇网文拆文。拆解爆款短篇的故事核、结构、情感线和反转设计。
/story-short-scan
Story short scan
短篇网文扫榜。分析知乎盐言、番茄短篇等平台热门数据。
/story-short-write
Story short write
短篇网文写作。辅助短篇小说创作,从构思到成稿。
/story
Story
网文工具箱路由入口。根据模糊意图自动分发到对应的写作、拆文或扫榜工具。
/browser-cdp
Browser cdp
浏览器操控。通过 CDP 复用 Chrome 登录态执行浏览器自动化。
/story-cover
Story cover
小说封面生成。根据书名、作者名和题材生成专业网文封面。
/story-deslop
Story deslop
网文去 AI 味。检测并清理模板化、解释腔和过度工整表达。
/story-import
Story import
逆向导入已有小说,将成稿或半成品解析为可续写项目。
/story-long-analyze
Story long analyze
长篇网文拆文,分析黄金三章、人设、爽点和长线节奏。
Implementation of Podlite markup language
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