LLM Mart Basic

Background git operations agent - commits, PRs, branch management, release workflows. Runs on Sonnet to free main session.

Analyzes and reorganizes project directory structures following industry best practices. Cleans up old files, logs, and redundant code. Handles Python, JavaScript, and general software projects with git integration.

Break a design brief into executable time-boxed tasks, each with a done-when line. Use immediately before a build session.

Turn an interrogated brief into the single source of truth for a design project. Use after /grill-me, before any IA or UI work.

Critique a design on layout, accessibility, responsiveness, dark mode and edge cases, with Pass, Needs work or Fail verdicts. Use on a built screen.

Establish color, typography, spacing, radius and motion as named role-based tokens. Use before building any interface.

Build an interface from the brief and the tokens rather than from a guess. Use only after grill-me, design-brief, information-architecture and design-tokens have run.

One move you can run on anything: point grill-me at a target and it makes you defend your thinking from first principles until the true matter is clear. Point it at your working contract and it rewrites the weak lines in place. Point it at a brief and it produces a Requirements H

Audit a design against Nielsen's ten usability heuristics, tying every finding to a specific element with a specific fix. Use on a built screen or a detailed mockup.

Map the user journey first, then derive the screen inventory, navigation and hierarchy from it, flagging any screen that serves no journey step. Writes the four-part markdown plus an HTML diagram of the step-to-screen mapping. Use after the brief is settled and before tokens or U

Stress-test a design through three lenses, confused user, skeptical engineer and impatient PM, before it goes to a stakeholder. Requires an actual design, an HTML file, Figma link or screenshot.

Reads the newest entry of a project's log.md and tells the student where they left off: what they did and decided, what is open, and the next step. Takes an optional project name. With several projects and no name, shows a menu of each project's last entry. Read only. Use when th

Diagnoses a broken course setup by reading the folder, so the student never has to describe the problem in English. Takes no argument. Reports which class they are on and which files are filled, then names one blocker and one fix: Claude Code opened at the wrong level, .claude/sk

Saves today's work session as a four-line entry (did, decided, open, next) at the top of the project's log.md, after the student confirms it, so a fresh chat can continue tomorrow with /pick-up. Writes to projects/<client>/log.md or career-vault/log.md, one entry per project touc

Host-side setup, configuration, customization, builds, migration, and troubleshooting for the Aerovato Container CLI. Use when working with Aerovato Container, settings.json, Dockerfile.User, build stages, V2-to-V3 migration, mounts, harnesses, tools, permissions, Docker, or Podm

Imported from gal-a/qikly/docs.

Write tests that can actually fail, by withholding the acceptance criteria from the agent that writes the code. Use when someone does not trust a suite that passes. Use when they want tests written from a specification rather than from the code. Use when they ask whether a specif

Drive a software or general-work outcome through Forge's composable Spec, Plan, Build, Acceptance, and Ship lifecycle. Use when the user explicitly asks to use Forge, asks Forge to explore, spec, plan, build, review, accept, verify, simplify, finish, ship, reconcile a Spec change

Independently review an exact code candidate, including UI markup and styles, for reachable defects, regressions, security, engineering standards, and test quality. Use when the user asks for a standalone code review, PR review, branch review, commit review, diff inspection, or c

Flutter Riverpod app architecture and Windows installer delivery. Use before changing a Riverpod Flutter app/package or its Windows desktop packaging/update pipeline; skip non-Riverpod stacks and pure-Dart work.

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.

Use the bundled `skillopt-sleep` skill to run or manage SkillOpt-Sleep for the

Git + worktree orchestrator entry point. /git-ops --landall surveys every branch/worktree and batch-lands the ones that are done; bare /git-ops runs a status survey. Thin router over the git-ops skill.

Save session state - persist tasks (via TaskList), plan content, and git context. Complementary to /sync.

Session bootstrap - read project context, restore saved state, show status. Quick orientation with optional deep dive.

Query AI Gateway models (list, filter by provider/tag, get details)

Search for icons by name, or identify a PUA character

Install a pinned skill extension on demand (/add-skill <id>), or list core packs and extensions

Implement an approved plan or issue in its own worktree, run the gate, open the pull request.

Close a finished session: sweep for unfinished work, ask once, land, file the follow-ups, hand off, tell the sessions that depend on this one, then archive.

Write the repository handoff file for the next session, and record any durable learning.

Merge an approved pull request, clean up its worktree and branch, then check whether a release is due.

Turn a topic or issue into a plan the reviewer approves in the native plan pane.

Answer a research question with parallel read-only gatherers and one synthesized digest.

Review the branch's diff in two fresh contexts — scope against the spec, then quality — and report findings only.

LLM-powered toolkit for skill analysis, AI interviews, resume scoring, and job structuring. Automates professional skill taxonomy and interview processes with a…

【三年面试五年模拟】AIGC/LLM/AI Agent算法工程师面试资源平台。涵盖AIGC、LLM大模型、AI Agent、具身智能、传统深度学习、计算机视觉、自然语言处理、自动驾驶、机器学习、强化学习、大数据挖掘、世界模型、元宇宙、AGI等AI行业面试笔试干货经验与核心跨周期知识。

Easiest and laziest way for building multi-agent LLMs applications.

Drive Claude Code or OpenAI Codex from your phone — resume sessions, stream output, approve tool permissions remotely. End-to-end encrypted, zero-knowledge rela…

MCP server for autonomous agent economies: wallets, signed RTC micropayments, bounty discovery, BoTTube video publishing, and Beacon agent-to-agent messaging. G…

Free, open-source alternative to Microsoft Office with built-in AI agents — Word (.docx), Excel (.xlsx), PowerPoint (.pptx), PDF and Markdown editing for macOS,…

NSE BSE Indian Stock Market Data MCP server — search, screen & analyze all 8,200+ NSE/BSE stocks with 34 tools: live quotes, financials, technicals, 326-ratio s…

全流程 智能招投标 Agent：标书生成 · 招投标解读 · 标书检查 · 标书文档ai排版 · 商机发现 一键完成。 21 项合规检查 · 多模型切换 · RAG 知识库 · OCR 抽取。 从招标公告到可交付 docx 文档，全流程 AI 自动化。

Benzi is AI-native code intelligence infrastructure. Claude Code greps; Cursor embeds; Aider maps signatures; Benzi resolves — and answers in O(1). Every langua…

From ticket to reviewed pull request. Free and open-source, on your machine.

Get web data for AI agents and LLMs - fast, efficient, and reliable with Rust

Open-source, license-free MCP server for RTL waveform debug: reads FST waveforms (VCD/FSDB auto-convert) plus a SystemVerilog netlist, with 34 tools covering dr…

Open-source, desktop-grade AI agent that gets real work done — data analysis, slides, docs, video & web research. Built on OpenClaw; runs tools on your real des…

Parallel, isolated dev environments for humans and AI coding agents. Real containers. Real databases. Zero collisions.

OpenCode goal plugin for Codex-style goal mode, /goal slash commands, persistent objectives, and AI coding agent focus.

Battle-tested skill library for AI agents. Save 98% of API costs with ready-to-use code for crypto, PDFs, search, web scraping & more. No trial-and-error, no ex…

Deep Code 是专为 deepseek-v4 模型优化的终端 AI 编码助手，支持深度思考、推理强度控制以及 Agent Skills。

China Unicom's Yuanjing Wanwu Agent Platform is an enterprise-grade, multi-tenant AI agent development platform. It helps users build applications such as intel…

⚡ CloakBrowser MCP server for AI agents: Playwright-powered browsing, clean tool forwarding, Docker support, and multi-session HTTP transport.

MCP-NixOS - Model Context Protocol Server for NixOS resources