LLM Mart Basic

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.

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.

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.

credo - Set the session mode to autonomous (work approved GO items unattended, hook-enforced keep-alive ON)

credo - Initialize session with main agent workflow instructions

credo - Set the session mode to passive (user available for clarifications only, no keep-alive)

credo - Set up Claude Code with recommended workflows and plugins

dogma - Find and fix AI-typical patterns in code (reactive cleanup)

dogma - Sync documentation across README files and wiki articles

dogma - Interactively collect and apply CLAUDE rules to the project

dogma - Add ignore patterns to multiple locations at once

dogma - Run project-specific linting and formatting on staged files (non-interactive)

dogma - Create or update DOGMA-PERMISSIONS.md interactively (Git, File, and Workflow permissions)

dogma - Sanitize git history from Claude/AI traces and fix tracking issues

dogma - Intelligently sync Claude instructions from a source to the current project with interactive review

dogma - Check and fix version mismatches across all version files

Install GSD resources into the active Claude config dir (${CLAUDE_CONFIG_DIR:-$HOME/.claude}/get-shit-done/) (required before using other GSD commands)

Remove GSD resources from the active Claude config dir (${CLAUDE_CONFIG_DIR:-$HOME/.claude})

hydra - Remove already merged worktrees and their branches

hydra - Create a new Git worktree for isolated work

hydra - Safely remove a Git worktree

hydra - Show available commands and explain the concept

hydra - List all Git worktrees of the repository

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Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

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AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…