LLM Mart Basic

Structured debugging methodology — reproduce, isolate, hypothesize, verify. Covers git bisect, binary search, logging, and minimal reproduction.

Monitor a build process (webpack, turbo, docker) for warnings and errors as they stream. Summarize issues and fix them before the build finishes.

Safely update an npm package by checking npmjs.com for the latest version, reading release notes, and handling minor vs major upgrades differently. For minor updates, just do it. For major updates, find the upgrade guide, validate breaking changes, and produce a detailed migratio

Enforce a configuration-driven design system when generating UI. Ensures consistent spacing, colors, typography, dark mode, interactions, and accessibility across all AI-generated components.

After making code changes, start the dev server, open the app in Cursor's built-in browser, and verify everything works — check rendering, console errors, and network health. Use proactively after any UI or API change.

Verify that a Markdown file has correct formatting — headings, lists, links, code blocks, spacing, and consistent style.

Visually QA a web application by launching it in Cursor's built-in browser, taking screenshots, checking console errors, and auditing network requests. Use after making UI changes to verify they look correct.

Write clear, conventional commit messages with proper type prefixes, scopes, and body content.

Write marketing copy for landing pages, product descriptions, CTAs, emails, and app UI text.

Analyze existing code and write comprehensive unit and integration tests for it. Detects the test framework, identifies untested code paths, and generates tests with proper mocking, edge cases, and assertions. Use when the user asks to add tests, improve coverage, or test a speci

A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

"Memory is the cornerstone of intelligent agents. Without it, every

Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.

This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Enforces research-backed best practices for minimal, high-signal agent documentation.

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

Expert in designing and building autonomous AI agents. Masters tool

AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

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.

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.

Setup automated synchronization workflows

Resolve synchronization conflicts automatically

Sync GitHub issues to Linear workspace

Sync Linear tasks to GitHub issues

Link pull requests to Linear tasks

Monitor GitHub-Linear sync health status

Synchronize task status with git commits, ensuring consistency between version control and task tracking.

Simulate system performance under various loads with capacity planning, bottleneck identification, and optimization strategies.

Create Linear tasks from pull requests

Test-driven development workflow with Red-Green-Refactor process and branch management

Balance team workload distribution

Automate changelog testing workflow

Analyze and report test coverage

I need you to create an integration testing plan for $ARGUMENTS

Accelerate scenario testing with rapid iteration cycles, confidence intervals, and compressed decision timelines.

Manage project todos in a todos.md file with add, complete, remove, and list operations

Generate troubleshooting documentation

Deep analysis and problem solving mode

Sets up a professional Unity project with industry-standard structure and configurations

Update current git branch name based on analysis of changes made

PiG (Pi in Go) is a faithful Go port of upstream Pi, the TypeScript codebase behind the Pi coding agent. It is a parity-bound translation, not a rewrite: upstre…

An AI Agent that lives in your pocket. Local-first and privacy focused.

Unofficial skill that teaches coding agents to build with TypeSafe AI's Jev: typed decisions, calibrated confidence, and prior art from 150+ community projects.

Adaptive Test-time Learning and Autonomous Specialization

Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests

Knowledge Management for Humans and Agents

Open-source Claude Cowork / Codex / WorkBuddy alternative — a local-first AI office agent that turns one request into real PPTX, DOCX, XLSX and HTML files. Runs…

DeepAgent Code: AI coding agent with persistent memory and control plane

Awesome Jev — evidence-graded index of TypeSafe System One: SDKs, MCP tools, agents, apps and open models. 20 languages, rebuilt every 2 hours.

CLI for Telegram — agent-friendly, daemon-based, with webhook event push.

AI deep-research agent that turns any question into a cited report: plans searches, reads real sources, verifies evidence. Self-hosted, multi-provider, Docker-r…