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。

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.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.

Health-check your meeting knowledge for contradictions, stale commitments, and decision conflicts. Use when the user asks "any conflicts in my meetings", "check for stale action items", "lint my meetings", "consistency check", "are there contradictions", or wants to audit their decision history.

List recent meetings and voice memos. Use when the user asks "what meetings did I have", "show my recent recordings", "any meetings today", "list my voice memos", or wants an overview of their meeting history. Also use when they need to find a specific meeting by browsing rather than searching.

Act as the user's live meeting sidekick inside the current terminal agent session. Use when the user explicitly asks you, the terminal agent, to watch a meeting, follow the live transcript, answer during the call, offer strategist thoughts, silently watch for risks, or track decisions. Do not use this skill to start or control the separate Minutes Coach HUD; explicit Coach or HUD lifecycle requests belong to minutes-copilot, and an ambiguous request such as "coach me live" requires one short surface clarification.

Self-coaching analysis of your own behavior across meetings — talk-time ratio, filler words, hedging language, monologue length, energy patterns, and (when meetings are tagged via /minutes-tag) what your behavior in winning meetings looks like vs losing ones. Use this whenever the user says "how did I do", "review my last meeting", "mirror", "self-review", "show my patterns", "coach me", "where am I weak", "talk time", "am I improving", "what do I do in meetings I win", "feedback on me", or asks for any kind of personal feedback on their own meeting behavior. This is the rare skill that gives the user a mirror to their own habits — surface it whenever they show curiosity about their own performance, even if they don't use the word "mirror".

Add a note to the current recording or annotate a past meeting. Use whenever the user says "note that", "remember this", "mark this as important", "add a note about", "annotate the meeting", or wants to capture a thought during or after a recording. Plain text input — no markdown needed.

Interactive meeting preparation — builds a relationship brief and talking points before a call. Use when the user says "prep me for my call with", "I'm meeting with X", "prepare me for", "what should I bring up with", "meeting prep", "get ready for my call", or wants to review history with someone before a meeting.

Generate a daily digest of today's policy-authorized meetings and voice memos — key decisions, action items, and themes across available recordings. Use when the user asks "recap my day", "what happened in my meetings today", "daily summary", "what did I discuss today", "any action items from today", or wants a consolidated view of the day's conversations.

Start or stop recording a meeting, call, or voice memo. Use this whenever the user says "record", "start recording", "capture this meeting", "stop recording", "I'm in a meeting", "take notes on this call", or wants to transcribe live audio. Also use when they ask about recording status or want to know if something is being recorded.

Draft user-facing Minutes release notes for a version from the commit range, recent GitHub releases, and the repository release checks. Use when the user asks to write, generate, prepare, revise, or review release notes or a changelog for a Minutes version.

Search past meeting transcripts and voice memos for specific topics, people, decisions, or ideas. Use this whenever the user asks "what did we discuss about X", "find that meeting where we talked about Y", "what did Alex say", "did we decide on", "what was that idea about", or any question that could be answered by searching their meeting history. Also use for "do I have any notes about" or "check my meetings for".

Guided first-time setup for Minutes — download whisper model, create directories, configure audio input. Use when the user says "set up minutes", "install minutes", "first time setup", "configure minutes", "get started with minutes", "how do I start using minutes", or when verify shows missing components.

Lightweight outcome tagging for meetings — won, lost, stalled, great, or noise. Use whenever the user says "tag this meeting", "mark that as a win", "that one was a loss", "tag yesterday's call as stalled", "mark this great", "that meeting was noise", "label that meeting", or any time they describe a meeting outcome in passing. Tagging takes 5 seconds and unlocks /minutes-mirror correlation analysis — the more meetings get tagged, the smarter mirror gets at telling the user what behavior patterns lead to wins. Surface this skill any time the user mentions a meeting result, win, loss, or wasted time.

Verify that Minutes is properly set up and working — model downloaded, mic accessible, directories exist, no stale state. Use when the user says "is minutes working", "check my setup", "verify minutes", "test recording setup", "why isn't minutes working", "minutes health check", or after running setup for the first time.

Analyze a product walkthrough, bug report video, Loom, or ScreenPal using Minutes transcription plus visual review. Use when the user wants a recorded demo or bug clip turned into a durable brief with transcript, key frames, issues, and next steps.

Weekly meeting synthesis — themes, decision arcs, stale commitments, and what deserves your attention next week. Use when the user says "weekly review", "what happened this week", "weekly summary", "recap my week", "any outstanding items", "week in review", or at the end of a work week.

Search agent history or trace code to its original agent session

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Generate a custom checklist for the current feature based on user requirements.

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

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A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.