LLM Mart Basic

Design and review Azure platform automation and DevOps delivery for landing zones, shared platform services, and safe infrastructure rollout flows. Use for IaC approach selection, Bicep versus Terraform positioning, bootstrap/run phase separation, pipeline control design, secret-

Use this skill for Azure Private Link and private endpoint adoption planning, including hub-versus-spoke placement, private DNS zone linkage, route implications, centralized versus workload-local endpoint trade-offs, and safe rollout validation.

Use this skill for Azure RBAC, Entra-backed access, role assignment, custom role, scope, subscription, management group, or least-privilege review tasks. Trigger when the user asks whether Azure access is too broad or how to grant access safely.

Use this skill for Azure resilience, business continuity, and disaster recovery reviews covering RTO/RPO realism, failover and failback assumptions, shared-responsibility gaps, and recovery runbook or drill quality.

Use this skill for Azure Resource Health, Service Health, activity-log alert, and first-pass incident triage when the question is whether Azure platform health is part of the problem.

Use this skill when the user asks which Azure role to assign, how to grant minimum access, whether a built-in role is sufficient, or when a custom role may be required.

Use this skill for Azure security posture review, baseline hardening, managed identity adoption, Key Vault posture, private access decisions, Azure Policy guardrails, and logging or audit gap analysis. Trigger when the user asks how to harden an Azure workload or platform without

Use this skill for Azure management-group hierarchy, subscription placement, resource-group boundary, and platform-versus-workload ownership decisions that affect governance, operations, and landing-zone scale.

Review Azure workload cost posture against the Well-Architected Framework Cost Optimization pillar: cost modeling, rightsizing, reservations, hybrid benefit, storage lifecycle, and idle resource elimination.

Review Azure workload reliability against the Well-Architected Framework Reliability pillar: availability targets, AZ/region topology, health monitoring, data resilience, deployment safety, and chaos testing.

Review Azure workload security posture against the Well-Architected Framework Security pillar: identity and access, segmentation, data protection, threat detection, secure development lifecycle, incident response, and policy compliance.

Use this skill when reviewing Backstage Scaffolder software templates. Trigger when the user asks whether a template is safe for developer self-service, whether template RBAC gates are in place, whether input parameters are validated, whether a step action has excessive blast rad

Use this skill for Cilium network policy review across the three policy formats (Kubernetes NetworkPolicy, CiliumNetworkPolicy, CiliumClusterwideNetworkPolicy), L7 policy via embedded Envoy, ClusterMesh cross-cluster semantics, Hubble flow observability, and CiliumEgressGatewayPo

用于对小说作者做"自我进化"蒸馏：①手动 / 周期回访（挖创作判断、捕获叙述原声（A 类）与创作判断原声（B 类）、周期蒸馏回填"作者小说风格画像"（十层）并反向净化防 AI 自创）；②全流程嵌入（选题构思 / 竞品 / 大纲 / 人物 / 背景 / 章节创作等全部环节自动调用，低频环节必采 1–2 问、高频草稿环节只读消费画像）；③散落触点（改稿痕迹 / 审阅回流 / 作者窗口 / 口述转写原声）；④像不像审计（原声句占比 / 惯用语命中 / 立场显影溯源 / 风格漂移，脚本取数）。与 通用-蒸馏作者文风 组成"静态种子 + 动态进化"双闭环，让写出来

用于围绕起点中文网或其他目标平台的同题材 TopN 候选池，按“市场数据层→内容创作层→运营策略层→受众反馈层”的四层框架锁定 3–5 本核心强样本并逐本深搜式竞对分析；若用户明确要求，也可对 TopN 全量逐本落盘。适合搜索榜单、筛选强样本、阅读目录/公开正文/设定/书评/读者评论/读后感/拆解材料，并在 `竞对分析/` 中为每部作品分别写出可举证、可横比、可回用于当前项目的详细竞对分析报告。关键词：竞对分析、竞品分析、标杆作品拆解、TopN 榜单、同题材对标、榜单深搜、四层拆解、读者评论分析、单书竞对报告。

用于创建连载小说章节正文、作者有话说与章节后记。适合新写单章、重写章节、扩写草稿、直接写回章节文件与完整跑通正文落稿工作流；其中 `## 作者有话说` 默认按读者向小剧场处理，不写成章节点评或创作总结。关键词：写章节正文、重写这章、扩写正文、作者有话说、章节后记、直接写回文件。

用于对章节正文做去 AI 味重写。适合主编式多轮去味、先诊断再定强度、整章去模板腔、局部拆解释腔、打散均匀句群、保信息重写与人物声音去同腔化。关键词：去AI味、主编式去味、多轮改稿、模板腔、解释腔、均匀句群、太像AI、重写这段。

用于做章节或片段的多平台适配策略。适合平台分发前的策略设计、改写前推演与平台撞车修复。关键词：多平台适配、平台差异矩阵、改写前推演、平台撞车修复、平台风格拉开。

用于对单章或多章执行多平台输出全流程编排。适合多平台输出 SOP、批量平台分发、断点恢复、门禁回炉与最终摘要收口。关键词：多平台输出编排、平台分发、断点恢复、门禁回炉、平台日志、最终摘要、今日头条。

用于对指定提纲执行"审阅→修改→复审"的闭环，直到双轴评分（技法分 ≥ 6.0 / 留存分 ≥ 5.0）加权的综合评分连续两轮独立审阅在保留两位小数后均严格大于 9.20。适合大纲审阅优化 SOP、报告复用、回炉循环、评分门槛控制与最终回执收口。关键词：大纲审阅闭环、回炉到 9.20 以上、双轴评分、复审循环、主报告覆盖、提纲优化 SOP。

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.

CoalWash self-update — check for a newer version and offer to apply it, or set how updates are handled.

Create an AI skill from any source (URL, repo, PDF, video, notebook, etc.)

One-command skill creation and packaging for a target platform

Sync a scraping config's URLs against the live documentation site

Generate and run validation queries for the current change

Generate and run validation queries for the current change

Check code-intelligence prerequisites (ripgrep + a language server) and print install hints

Create space-efficient paper and proposal figures (HTML mockups, TikZ, or skia-canvas) from tool selection through LaTeX insertion

Design concise overview, mechanism, or workflow figures as editable PowerPoint objects

Run the implement-review staged-change review loop

Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill

Run prun: parallel delegation fan-out on Agy workers (the session coordinates)

Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim

Vet the staged change: run the implement-review review loop (short alias)

Create space-efficient paper and proposal figures (HTML mockups, TikZ, or skia-canvas) from tool selection through LaTeX insertion

Design concise overview, mechanism, or workflow figures as editable PowerPoint objects

Run the implement-review staged-change review loop

Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill

Run prun: parallel delegation fan-out on Agy workers (the session coordinates)

Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim

Open-source, self-hosted AI media server. One server replaces your entire media stack, with a web app, native iPhone app, and an AI agent that gets things done.…

A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.

🦦 Crayotter: A Multimodal AI-Agent for Video-Editing, Video-Composing, and Video Production. Powered by Multimodal LLMs for autonomous Text-to-Video agentic fr…

Webextension tool for Odoo

基于多模态视觉感知与 LLM Agent 的 macOS 微信自动化框架 | Visual RPA for WeChat

The operational superset of Pi Coding Agent — everything Pi, plus observability, governance, recovery, evaluation and multi-agent orchestration. Pi Coding Agent…

Metrik 可以集中查看本机多个 Agent 的配额余量和 Token 消耗，目前支持 ChatGPT、Claude、GLM、Kimi 等主流 AI 服务。

An AI agent with a real self — soul she wrote, desires that drive her, a heartbeat for autonomous action, dreams she processes when you're away. Capability supe…

Production-ready open source terminal coding agent with readable, layered code: permission rules, OS sandboxing, MCP, skills, sub-agents, and Anthropic, OpenAI-…

Answer me with HTML — an agent skill that answers hard questions with a one-page HTML you can actually read. 让 AI Agent 用一页 HTML 回答复杂问题。

Prompt packs that make any AI agent a LaTeX expert — fix errors, polish writing, format for venues, read papers, recover source

Claude Code plugin: universal radial-tree exploration engine. One tree skill + swappable presets (brainstorm / attack / design / code-audit) for divergent ideat…

Claude Code + OpenClaw + Codex + WorkBuddy 中文教程 | 50篇完整教程 + 1张速查卡 | 80万+内容量 | 1500+实操示例 | AI Coding / Agent 四线学习路径（编程+助手+Agent+办公）

SmartLabelBench 2026: LLM-Powered Auto Annotation and Dataset Builder for Everything

从零开始玩转OpenClaw：最全面的中文教程，涵盖安装、配置、实战案例和避坑指南（github版）

Native Android GUI for running AI coding agents locally on-device. No terminal or PC required.

基于 LangChain/LangGraph 的 ReAct Agent ，结合 RAG、工具调用与 Streamlit 界面，面向智能客服与报告生成场景。

An open-source, local-first AI learning workbench

Open-source coding agent for your terminal, built in Rust and on a journey of continuous community improvement. Issues and PRs welcome.

Matt Pocock 技能集的中文翻译版 — 地道中文，原汁原味的技术术语。基于 mattpocock/skills 复刻。每日中午12点钟同步