LLM Mart Basic

Answers natural-language questions about the user's database. Loads the agami semantic model (subject areas, tables, columns, relationships with join cardinality, entities, metrics) and few-shot examples from <artifacts_dir>/<profile>/, generates SQL via the examples-first traver

Reconciles known (label, expected_value) numbers from an existing dashboard against agami's answers. Input can be a SCREENSHOT of a Metabase / Power BI / Tableau / Looker dashboard (Claude's vision extracts the pairs), a CSV, or numbers pasted inline — the user doesn't need to kn

Saves a user correction so future queries learn from it. Always appends a (question, corrected_sql) pair to the subject area's example library under <artifacts_dir>/<profile>/prompt_examples/<area>/. Additionally, classifies the correction and — when applicable — applies a surgic

Wires the local agami MCP server (python -m mcp_harness) into the Claude Desktop app in one step, so you can ask your database questions from Claude Desktop — not just inside Claude Code. Auto-detects the right Python interpreter (the one with your DB driver), installs the agami-

根据用户描述生成高质量绘图 prompt，并按通用、roadmap、schematic 模式通过 BenszAPI 直接完成 gpt-image-2 或 Nano Banana/Gemini 出图、编辑和多轮迭代；这是自包含的图片生成工作流，选中后不得调用或依赖 imagegen，除非用户明确要求同时使用 imagegen。

当用户明确要求"测试代码"、"运行代码审查"或"进行代码自检"时使用。通过多轮 A 轮批判性代码审查 + B 轮代码质量原则检查，系统化发现、记录、修复程序代码中的问题，并将计划/过程/结果统一沉淀到目标代码根目录的 `.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/auto-test-code/{yyyy-mm-dd-hh-mm}/output/tests/` 隔离工作区。⚠️ 不适用：用户只是想优化功能（应直接修改）、只是询问代码问题（应直接回答）、没有明确"测试代码"意图。

当用户明确要求"测试项目"、"运行 auto-test-project"或"进行项目级测试"时使用。对完整项目进行多轮 A 轮批判性测试 + B 轮质量检查，系统化发现、记录、修复问题。⚠️ 不适用：用户只是想优化功能（应直接修改）、只是询问项目问题（应直接回答）、没有明确"测试"意图。

当用户明确要求"测试技能"、"运行 auto-test"或"进行批判性测试"时使用。通过多轮 A 轮批判性测试 + B 轮质量原则检查，系统化发现、记录、修复问题，并沉淀可追溯的 `.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/auto-test-skill/output/plans/` 与 `.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/auto-test-skill/output/tests/` 文档。⚠️ 不适用：用户只是想优化功能（应直接修改）、只是询问技能问题（应直接回答）、没有明

当用户明确要求"使用 awesome-code / 多代理协作 / 并行协调开发"时使用。通过脚本收集可用 Agent 摘要、配置约束与 `dispatch_gate`，再由 AI 自主判断 single-pass / focused-agent / parallel / sequential 策略并选择子代理；当配置中的 required route agent 缺失时必须阻塞继续执行。⚠️ 不适用：用户仅需单一角色的简单修改或咨询、用户未明确表达多代理协作意图、用户只是了解技能概念。

当 Bensz 系列 skills 在真实用户环境中因 skill 设计缺陷而出现 bug，或用户明确说“我想 report bensz skills bugs”“帮我公开上报 bensz skills 的 bug”时使用。该 skill 负责把 bug 规范化记录到 `~/.bensz-skills/bugs/`，并在用户明确要求公开报告时通过本地 `gh` 轻量上传到 `huangwb8/bensz-bugs`，全程严禁修改用户本地 Claude Code/Codex 中已安装 skills 的源代码。

当用户明确要求"优化 prompt"、"改进提示词"、"润色指令"或"将简陋 prompt 转换为最佳实践版本"时使用。基于 OpenAI 和 Anthropic 官方最佳实践，对用户提供的简陋 prompt 进行结构化优化，输出符合社区标准的高质量版本。

当用户明确要求“压缩/瘦身/精简某个 Agent Skill 的 Markdown 文档”“在不改变功能前提下降低 skill 上下文开销”时使用。先理解目标 skill 的真实能力与安全边界，再在忽略 `tests/`、`plans/` 以及目标 skill 的 `README.md`、`CHANGELOG.md` 的前提下，压缩 `SKILL.md`、`references/*.md` 等工作型 Markdown，并把中间产物隔离到 `.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/compact-bensz-skill

当用户明确要求"提交 Git 改动"、"生成 commit 信息"或"创建 git commit"时使用。仅用 Git 分析改动并自动生成 conventional commit 信息（可选 emoji）；必要时建议拆分提交，默认运行本地 Git 钩子（可 --no-verify 跳过），提交后默认自动 push（可 --no-push 跳过）。

当用户明确要求“review 某个 GitHub PR”“评估某个 pull request 是否值得 merge”“帮我判断这个 PR 怎么处理”时使用。基于用户提供的 GitHub 仓库地址、PR 编号/链接和补充说明，进行只读、证据驱动的 PR 审查：理解 PR 解决的问题、评估方案优劣与局限、默认优先使用内置“好 PR”标准并在必要时联网补充、识别恶意或高风险改动，并输出是否建议 merge 的 Markdown 决策报告。⚠️ 不适用：用户要你直接修改 PR 代码、直接 merge PR、或在本地执行 PR 分支中的不可信代码。

当用户明确要求"发布项目到 GitHub"、"创建 GitHub Release"或"生成 Release Notes"时使用。智能分析 tag 间历史变化，生成专业且吸引人的 Release Notes，自动创建 GitHub Release。支持首次发布、常规版本、预发布版本（alpha/beta/rc），自动识别 prerelease 标记。

当用户明确要求"初始化项目"、"创建项目指令文件"或"生成 AGENTS.md"时使用。完全自动化：自动检测操作系统默认语言，分析项目目录结构（支持 Python/Web/Rust/Go/Java/数据科学/文档项目等），推断项目类型和用途，一键生成规范的项目指令文档。生成结果包括：AGENTS.md（跨平台通用项目指令，Single Source of Truth）、CLAUDE.md（Claude Code 特定适配，通过 @./AGENTS.md 引用）、README.md（项目介绍与使用方法）、CHANGELOG.md（项目变更记录）、.giti

当需要把本仓库 skills/alpha 下的生产 skills 安装到系统级（默认同时安装到 Codex: ~/.codex/skills 和 Claude Code: ~/.claude/skills），以便在任意项目/对话中可被发现与调用时使用。默认不安装 skills/beta；只有显式指定 beta 源目录时才处理 beta skill。使用 MD5 哈希进行版本控制，仅安装有更新的 skills；支持 --skill 指定单个或少量技能安装/更新、强制覆盖安装、指定单一目标安装和远程安装模式（--remote --check/--auto）。

当用户明确要求"并行执行同一条 Vibe Coding 指令 / 多个独立 agent 或 subagent 同时审查、想方案、优化、对比多条路线 / 多线程独立尝试"时使用。默认使用智能模式：由宿主原生 subagent 独立分析并由主 agent 汇总；智能模式和代码模式必须使用同一套 `.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/parallel-vibe/{yyyy-mm-dd-hh-mm}/` 运行目录、`@main/plan.json`、thread `workspace/`、`RESULT.md` 与 `r

当用户明确要求"转换图片格式"、"修改图片格式"、"图片格式转换"时使用。支持任意格式图片到目标格式的转换，包括：本地文件/网络URL/剪贴板图片输入，PNG/JPEG/WEBP 等常见格式输出，单文件或批量处理模式。核心特点：自动检测输入格式、支持透明度处理、批量处理保持原始文件名结构。⚠️ 不适用：用户只是想调整图片大小/裁剪（应使用图片编辑工具）、只是想查看图片信息（应直接使用文件查看器）、没有明确"格式转换"意图。

规范 AI 开发 R Markdown 分析脚本的行为准则。当用户要求"写 Rmd 分析"、"开发 R 脚本"、"做数据分析"时触发。核心原则：遵循主业与副业分离架构（.R 保留完整数据，.Rmd 应用业务阈值），优先使用用户已有 R 包资源；图表默认按 Nature 级别可读性与出版质量生成；专家级解读兼顾弱背景读者，提供四层框架、指标导读与不常用指标首次解释协议；路径验证确保跨平台兼容性。前提：luckyBase 为硬依赖。

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.

`crabbox attach` follows the recorded events of an active coordinator run and

`crabbox azure` groups Azure provider setup commands. It currently has a single

`crabbox bench` records and reports local benchmark timing observations. It is a

`crabbox cache` inspects, purges, or warms package and build caches on a

`crabbox capsule` captures, replays, and tracks lightweight failure capsules.

Save the state of a lease, then restore it onto another box or fork it into a

`crabbox claims list` prints the lease claims stored on the current machine. It

`crabbox cleanup` sweeps direct-provider machines and local provider state that

`crabbox code` bridges a Linux lease's `code-server` workspace into the

`crabbox config` inspects and updates user configuration. It has three

`crabbox connect` resolves a lease and opens an interactive SSH session to it.

`crabbox cp` copies files or directories between the host and a Crabbox-owned

`crabbox desktop` drives a visible desktop session on a lease that was warmed

`crabbox doctor` runs a preflight before you commit to a long workflow. It is

`crabbox egress` gives a lease mediated outbound network: a lease-local browser

`crabbox events` prints the broker's event log for a recorded run.

`crabbox heartbeat` refreshes the idle deadline for one owned lease and prints

`crabbox history` lists recorded remote command runs from the broker. Each run is

`crabbox image` holds the trusted-operator controls for provider base images:

`crabbox init` onboards the current repository: it writes the minimal config

Conduit — native SwiftUI iOS client for Hermes Agent

一个现代化的可视化规则引擎平台，用于编排复杂的 AI 工作流。在无限画布上设计、测试和部署 AI 流程——无需编写代码。结合 flowgram.ai 的能力与 Java 服务端，提供生产级工作流管理。在此之上提供AI Agent / Copilot的助手编排能力。

Job application tracker and AI-powered job search assistant. Helps job seekers manage their search journey with AI resume review, job matching, task logging, an…

📝 Markdown and HTML renderer for Svelte 5 — built for streaming AI agent output from Claude Code, ChatGPT, and agentic workflows. XSS-safe defaults, token cach…

ServiceNow MCP server: 500+ tools and 26 AI capabilities for any AI (Claude, ChatGPT, Gemini, Cursor, Copilot). Multi-transport (stdio, SSE, HTTP), A2A, dynamic…

Free crypto news API - real-time aggregator for Bitcoin, Ethereum, DeFi, Solana & altcoins. No API key required. RSS/Atom feeds, JSON REST API, historical archi…

Build production-ready AI agents in both Python and Typescript.

From agent user to agent builder: build a Claude Code-style coding agent from scratch in Python: 8 articles, 4 videos, one codebase

🪁 A lightweight, modern Kubernetes dashboard that unifies multi-cluster and resource management, enterprise-grade user governance (OAuth, RBAC, and audit logs)…

Stateful runtime management for LLM agents—inject, manipulate, and retrieve Python objects across turns.

AgentCall lets AI Agents join meetings with voice, video & screen-share to build together. Supports Google Meet, Teams, Zoom (Beta)

Kition brings Markdown, DataTable, WhiteBoard, a tool-using AI agent, browser research, and visual workflows into one desktop workspace.

IntentKit is an open-source, self-hosted cloud agent cluster that manages a collaborative team of AI agents for you.

A powerful Model Context Protocol (MCP) server providing comprehensive Google Maps API integration with LLM processing capabilities.

Turn your Claude Pro/Max subscription into an OpenAI-compatible API for your IDEs and devices — LAN auth, per-key quotas, response cache, disciplined cli.js ali…

Paw Work - selection-first web agent for Chrome: select on the live page, describe the outcome, take away an editable office file. BYOK, sandboxed, no server.

基于 AI Agent + MCP 工具链 + 渗透 Skill 编排， 配合大语言模型， 自然语言输入 → 自动完成「信息收集 → 漏洞发现 → 漏洞利用 → 报告生成」全流程。

Lexora — Personal AI workspace built around Desktop / 以 Desktop 为核心的个人 AI 工作台

First AI Journey for DevOps - with comprehensive learning paths, practical tips, and enterprise guidelines

AI coding agent with one Python core and three front-ends — headless CLI, Textual TUI, and an Electron desktop. Works with any OpenAI-compatible API, with risk-…