LLM Mart Basic

用于审阅人物传记。适合检查身份归属、动机代价、能力边界、关系网、写作抓手、心理弧线与索引映射是否成立，并输出可落库、可回写的传记审阅报告。内置 Nuwa 深度审阅（心智模型、表达DNA、矛盾张力、诚实边界、决策启发式）。关键词：审人物传记、人物审阅、能力边界、关系网、心理弧线、传记报告、心智模型、表达DNA。

用于审阅分部大纲、分卷大纲、章清单与单元案执行版。适合检查上级分配内容是否全量落地、5/15 节奏与钩子是否成立、章中回报是否存在、单元案是否能阶段闭环并供血主线，并输出可回写的分层审阅报告。关键词：审分卷、分卷审阅、章清单审阅、章中回报、卷内节奏、分层报告。

用于审阅总大纲、全书总纲与战略层文件组。适合检查总纲包对象是否完整、M0 / X / C 体系是否稳固、四阶段机制递进是否成立、单元案矩阵是否持续供血主线，并输出可回写的总纲审阅报告。关键词：审总纲、全书总纲审阅、M0、X线、C线、总纲报告。

用于审阅故事设定、手法、程序链、证据载体或规则文档。适合检查规则是否可执行、边界与代价是否明确、是否可证据化、是否具备程序摩擦与镜头化表达，并输出可落库、可回写的设定审阅报告。关键词：审故事设定、设定审阅、规则边界、证据化、程序摩擦、设定报告。

用于审阅章节正文的执行质量。适合检查章首抓力、中段回报、章末钩子、现实落地、规则边界、链路失配与可回写的审阅结论。内置人物执行审计（声口一致性、心智模型落地、压力反应匹配、误判/盲区触发、关系拉扯兑现）。关键词：审这章、章节审阅、章首抓力、中段回报、章末钩子、审阅报告、人物执行审计、声口检测。

用于在选定平台与题材之后、设计大纲之前，完成项目级战略初始化。唯一目标：在平台和题材约束下确定最能吸引读者和最能赚钱的小说要素清单。 核心方法：从写作研究和竞对分析数据出发自主决策。核心产出：项目根目录下的 Agents.md（强制产出）+ 项目初始化蓝皮书。 关键词：项目初始化、项目策划、Agents.md、从数据出发、套路自主决策、蓝皮书

用于以默认极严、保守、负面证据优先的口径评估作品在目标平台的签约或过稿潜力。适合统一承接评估流程骨架、分阶段准入评估、材料完整度判断、评分维度、概率区间、封顶与一票否决规则，并兼容竞争位 / 竞品威胁评估语境。评估{目标平台}项目时，强制要求使用多种可用网络搜索工具检索{目标平台}同题材 TopN 候选池（起点默认按起点中文网榜单检索，其他平台按可用性调整），并按 "TopN 候选池 + 3–5 主压制样本 + 四层竞对拆解"做从严对照。目标平台路由优先级：用户显式指定 > Agents.md 主输出平台 > 默认回退起点中文网。关键词：平台签约评估、极

用于强化章节结尾的章末钩子。适合章末抢救、结尾补针、翻页感增强、下一章驱动增强与最后 150–300 字局部强化。关键词：强化章末钩子、结尾没翻页感、只修结尾、下一章驱动、最后一句、章末补针。

用于强化章节开头前 150–300 字的入场抓力。适合章首抢救、首屏留存优化、异常前置、冲突前置与只修前两三段的局部强化。关键词：强化开头、章首抓人、前 150–300 字、首屏留存、开头太慢、只修前两段。

用于设计或重写场景单元、章节场景与微型剧本。适合场景施工卡落地、动作链推进、证据入场、对话承压与控制卡到场景的职责拆解。关键词：场景单元、场景施工卡、微型剧本、动作链、证据推进、场景职责。

用于设计、重写或强化对话冲突。适合把角色对话写成立场碰撞、潜台词拉扯、信息释放与情绪升级并存的有效场景。关键词：对话冲突、盘问、谈判、争执、潜台词、关系升级。

执行微空间或受限空间场景。用于在楼道、电梯、值班室、走廊、地下车库、出租屋、候诊区、办公室后区等熟悉空间里，把空间常态、微反常、人物动作、后果升级与证据入场咬成一个可落地的场景引擎；执行时必须先加载 通用-执行场景单元，再叠加本 Skill 的微空间专项工法。适合微空间惊悚、受限场景压迫、日常异化场景设计。

提纯多平台输出前的中文母稿。用于在不同平台改写前，把正文母稿修成信息稳、事件链稳、情绪主轴清、证据与规则关键点不丢、追读链完整、平台可分化的高质量源稿，避免后续平台适配建立在松散底稿上。适合多平台前置精修、母稿加固、跨平台源稿提纯。

用于撰写、重写或审计小说书评。适合把全书 / 分部 / 分卷 / 分章的阅读体验、核心卖点、平台传播口径与非剧透钩子写成可直接发布的书评文件。关键词：撰写书评、推荐文案、分章安利、平台推荐评述、非剧透书评、今日头条。

用于撰写、重写或审计作品的内容简介。简介即开篇——读者看简介的决策模式与看开头完全一致：在 3–5 秒内决定要不要点开。适合把题材卖点、主角处境、核心冲突、持续承诺与情绪抓手压缩成平台可用文本，支持全层级（全书/分部/分卷/章节）简介。关键词：内容简介、作品简介、平台简介、卖点压缩、简介重写、简介审计、简介同构。

用于润色已有章节正文。适合整章精修、局部重写、节奏提纯、章首抓力增强、中段回报提亮与章末钩子收紧。关键词：润这章、精修正文、整章精修、局部重写、节奏提纯、直接回写。

润色章节后的 `## 作者有话说`。用于把作者有话说修成读者向小剧场：一丢丢创作花絮 + 一点贴着本章余波的情绪共鸣 + 一个开放式轻追问，而不是创作说明会、设定讲义或章节总结。适合发布前精修、后记前小剧场重写、把作者有话说从复盘腔改回读者腔。

专用于小说写作的结构化深度研究 Skill。支持对题材趋势、平台生态、专业知识、场景环境、人物原型、写作技法、读者市场等研究域，执行大纲生成→并行深搜→报告输出的全流程深度调研。关键词：深度研究、写作研究、题材调研。

用于生成写前章节控制卡、章节施工卡或章节作战卡。适合写前施工、场景拆分、中段回报预埋、章末钩子预埋与上游供血对齐。关键词：章节控制卡、施工卡、作战卡、中段回报、章末钩子、写前施工。

用于对单章或连续多章执行"初始化→控制卡→创作→润色→去AI味→审阅回炉→读者产物→摘要"的完整章节闭环。适合章节写作 SOP、批量写章、日志续跑、9.2+ 回炉与最终摘要收口。关键词：章节创作闭环、跑完整 SOP、批量写章、回炉到 9.2、日志续跑、最终摘要。

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.

Validate application configuration with schemas, per-environment rules, runtime checks, and secure handling of sensitive values

Preflight a DGX Spark system for an ML training or inference workload and emit env-report.json

Set up debugging and tracing with remote debugging, distributed tracing, debug logging, profiling, and production diagnostics

Generate API, architecture, code, and user documentation from a codebase and automate keeping it current

Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.

Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.

Coordinate specialized review agents in parallel or in sequence and synthesize their findings into one code review

Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.

Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.

AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.

Generate comprehensive migration plans and scripts for transitioning codebases between frameworks, languages, versions, or platforms with minimal disruption.

Plan and execute safe, incremental dependency upgrades with minimal risk — including breaking-change migration paths and proper test verification.

Orchestrate legacy system modernization using the strangler fig pattern with gradual component replacement

Scaffold React and React Native components with TypeScript, tests, styles, and Storybook stories

Scan React, Vue, Angular, and vanilla JavaScript code for XSS vulnerabilities and report fixes with secure coding examples

Orchestrate end-to-end full-stack feature development across backend, frontend, database, and infrastructure layers

Orchestrate git workflow from code review through PR creation with quality gates

Create a role-specific onboarding plan for a new team member, from pre-arrival setup through the first 90 days

Enhance a pull request with a generated description, review checklist, risk assessment, and test coverage report

Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…

Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…

Ultimate Multi-Agent OS for Autonomous AI NPCs 2026

Personal AI Agent Hub 2026 — Build Your 24/7 Autonomous Assistant

Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control

Slash API Batch: Cut AI Costs by 50% in 2026

Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

Agent Skills for Solopreneurs

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…