LLM Mart Basic
@llm-mart · Joined Jun 2026
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.
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.
/quick-commit
Quick commit
Quick commit with automatic formatting, linting, and conventional commit message
/resync
Resync
Resync external skill submodules to latest upstream versions
/scaffold
Scaffold
Scaffold a new Solana project with programs, frontend, tests, and CI
/setup-ci-cd
Setup ci cd
Setup CI/CD pipeline with automated security checks for Solana programs
/setup-mcp
Setup mcp
Configure MCP server API keys in .env
/test-and-fix
Test and fix
Run tests and automatically fix common issues
/test-dotnet
Test dotnet
Run .NET/C# tests for Unity projects and backend services
/test-rust
Test rust
Run Rust tests for Solana programs and backend services
/test-ts
Test ts
Run TypeScript tests for Solana frontends and Anchor programs
/update
Update
Update solana-ai-kit to latest version from upstream
/write-docs
Write docs
Generate documentation for Solana programs, APIs, and components
/README
README
Seventy-two slash commands for Claude Code, grouped by what you are doing.
/aliases
Aliases
Find missing aliases
/archive
Archive
Move cold material out of the wiki
/ask
Ask
Ask a question answered only from the vault
/audit
Audit
Audit what an agent did
/backfill
Backfill
Bulk import an archive in batches
/bridges
Bridges
Find the pages holding the graph together
/changed-my-mind
Changed my mind
What I have revised
/claude-md
Claude md
Build or update your CLAUDE.md
🤖 MateClaw — Your second brain with Multi-Agent Orchestration, MCP Protocol, Skills & Memory, Dream, and Multi-Channel Support. Built on Spring AI Alibaba.
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