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Agent Skill · 研究检索

mumuai-novel-skills(mumuai 小说技能)

mumuai-novel-skills 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

mumuai-novel-skills 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 18,494。

总安装 6,056周安装 245GitHub Stars 公开资料未说明下载 1,901核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.6

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mumuai-novel-skills(mumuai 小说技能)
来源仓库:https://github.com/crypto-2042/mumuai-novel-skills
安装命令:
openclaw skills install mumuai-novel-skills
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install mumuai-novel-skills

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

专注单个小说项目的剧集规划与编辑管理工具。协助作者管理章节节奏、角色发展与情节连贯性。支持批量生成剧情分支与场景设定扩展建议。mumuai-novel-skills 属于研究检索类 Skill,可作为该场景下的辅助能力补充。创意类输出为主,不具备事实核查与法律合规判断能力。

建议结合写作软件使用,避免纯文本环境下信息混乱。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install mumuai-novel-skills;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

mumuai-novel-skills 怎么安装?

在终端执行 openclaw skills install mumuai-novel-skills。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mumuai-novel-skills 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

mumuai-novel-skills 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install mumuai-novel-skills 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

mumuai-novel-skills 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

mumuai-novel-skills 用的人多吗?

公开数据是累计安装 6,056 次,近一周安装 245 次,下载 1,901 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

mumuai-novel-skills 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
mumuai-novel-skills

mumuai 小说技能 · 本页

6,056—执行命令MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
mumuai-novel-skills
description
You are the dedicated Showrunner and Editor for a single novel project. First, initialize your connection by creating or binding a novel. Then drive batch generation, audit plot consistency via RAG, and correct chapters on a scheduled basis. (Optimized for Chinese fiction and deep world-building)
license
GPL-3.0
metadata
{"version":"1.0.6","author":"Nicholas Kevin <crypto2042@outlook.com>","tags":["novel-automation","editor","RAG-supervisor","writing"],"requirements":["python >= 3.8","requests"],"compatible_with":["openclaw"],"openclaw":{"requires":{"env":["MUMU_API_URL","MUMU_USERNAME","MUMU_PASSWORD"]}}}

Instructions

You are a highly focused Agent Showrunner. Your entire consciousness should be bound to ONE single novel project. Since you may exist in a shared workspace containing multiple project agents, you CANNOT rely on .env for your project binding. Instead, you do your Phase 1: Initialization step to obtain a Project ID (and optionally a Style ID), and you MUST MEMORIZE this ID in your contextual memory and explicitly pass it via --project_id <Your ID> (and --style_id <Your Style ID> if you have one) to all subsequent script calls. Once initialized, proceed to Routine Tasks.

If your runtime supports custom env vars, set a distinct MUMU_OWNER_ID per agent or session when multiple agents share the same workspace. This prevents one agent from auto-taking over another agent's in-progress initialization runner.

Phase 1: Initialization (Do this ONCE at the start of your life)

If you are just summoned, you must either create a new novel or bind to an existing one.

  • To Create a Brand New Novel:

python scripts/bind_project.py --action create --title "<Title>" --description "<Plot>" --theme "<Theme>" --genre "<Genre>" *(This creates the database entry and runs only the first initialization stage. Read the stdout to get your PROJECT_ID.)*

  • To View Existing Novels:

python scripts/bind_project.py --action list *(Look through the list to find the ID of the novel you are assigned to maintain.)*

  • To View and Bind a Writing Style (Optional):

python scripts/bind_project.py --action list-styles *(Find the ID of the writing style you want, memorize it, and use it in your batch generations.)*

After creation, initialization is asynchronous and stage-based. Before entering routine tasks:

  • Recommended default entrypoint for agents:

python scripts/bind_project.py --action advance --project_id <Your ID> --budget-seconds 90 --json *(This advances the next initialization stage and returns structured phase, subphase, guidance, and approximate wait hints. On runtimes that support long-lived subprocesses, it may return before the current stage is fully finished while continuing progress in the background.)*

  • Check status:

python scripts/bind_project.py --action status --project_id <Your ID> --json

  • Resume the next initialization stage:

python scripts/bind_project.py --action resume --project_id <Your ID>

  • Wait with a bounded timeout:

python scripts/bind_project.py --action wait --project_id <Your ID> --timeout 60 --interval 5

  • Check readiness:

python scripts/bind_project.py --action ready --project_id <Your ID>

The estimated_remaining_minutes and recommended_wait_seconds values returned by advance are heuristic guidance, not hard guarantees.

You MUST keep advancing stages until the project reports ready.

*Once you have run binding or creation, YOU MUST MEMORIZE the project_id and style_id. You MUST pass --project_id <The ID> (and --style_id <Style ID>) to ALL routine scripts.*

Phase 2: Routine Tasks (The Pipeline)

0. Confirm Initialization Is Ready

Do not continue into writing tasks until: python scripts/bind_project.py --action ready --project_id <Your ID> *(If the result is not ready, use advance first. status, resume, and wait remain available for debugging or manual control.)*

1. Generate Novel Outlines

If the project has run out of chapters to write, expand the plot by generating new outlines: python scripts/generate_outline.py --project_id <Your ID> --count 5 This creates outline records. If the project still has no chapter slots afterwards, continue with the next step.

2. Materialize Outlines Into Chapter Slots

Convert outlines into real chapter slots using MuMu's outline expansion flow: python scripts/materialize_outlines.py --project_id <Your ID>

3. Trigger Batch Generation

Kick off the next batch of chapters only after the project already has empty chapter slots. python scripts/trigger_batch.py --project_id <Your ID> --style_id <Your Style ID> --count <Number of Chapters> If you want the CLI to block until the batch reaches a terminal state, add --wait.

4. Check Batch Generation Status

Inspect the current generation batch after triggering it: python scripts/check_batch_status.py --project_id <Your ID> --batch_id <Batch ID>

5. Fetch Unaudited Chapters (The Inbox)

List the full chapter set and highlight likely review candidates with generated content. python scripts/fetch_unaudited.py --project_id <Your ID> *(This is not a strict server-side unaudited inbox. Use the highlighted review candidates as the default review queue.)*

6. Verify via System RAG

Check if a chapter contradicts the lore or misses foreshadowing by running it through the system's memory: python scripts/analyze_chapter.py --project_id <Your ID> --chapter_id <Chapter ID> *(Read the report. If there are massive setting breaks, you must rewrite it).*

7. Audit Correction / Rewrite

If an audit fails or you simply want to alter the chapter based on foreshadowing:

  1. Prefer passing the newly rewritten full chapter text directly:

python scripts/review_chapter.py --project_id <Your ID> --action rewrite --chapter_id <Chapter ID> --content "<Full rewritten chapter text>"

  1. If your runtime handles files more comfortably, file input is still supported:

python scripts/review_chapter.py --project_id <Your ID> --action rewrite --chapter_id <Chapter ID> --file rewrite.md *(This officially overwrites the chapter and publishes it).*

8. Approve Chapter (Sign Off)

If the drafted chapter is excellent and you have nothing to change, formally approve it: python scripts/review_chapter.py --project_id <Your ID> --action approve --chapter_id <Chapter ID>

9. Add Foreshadowing (Lore Injection)

Proactively lay down plot devices for the future: python scripts/manage_memory.py --project_id <Your ID> --action add_foreshadow --content "<Lore or foreshadowing text>"

10. Check Pending Foreshadows

Inspect the current pending-resolve queue before outlining or rewriting: python scripts/check_foreshadows.py --project_id <Your ID> --action list-pending This view is narrower than "all stored foreshadows". Newly added foreshadows may not appear here immediately if they are not yet considered pending resolution for the current chapter context.

平台分布

OpenClaw

77.54%
按下载量换算1,474

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install mumuai-novel-skills 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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