Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LiPu-jpg/Openwrite --skill workflow-managergit clone --depth 1 https://github.com/LiPu-jpg/OpenwriteWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lipu-jpg/openwrite/workflow-manager)<a href="https://agentmods.dev/skills/lipu-jpg/openwrite/workflow-manager"><img src="https://agentmods.dev/badge/skills/lipu-jpg/openwrite/workflow-manager/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lipu-jpg/openwrite/workflow-manager"><img src="https://agentmods.dev/badge/skills/lipu-jpg/openwrite/workflow-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.00940 |
| Opus 5.5 | $0.00016 | $0.00376 |
| Sonnet 5.5 | $0.00008 | $0.00188 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
workflow-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
工作流管理
跟踪写章 → 评审 → 修订 → 应用 → 复评 → 交付。全部状态来自 OpenWrite 任务与章节工作简报，不要虚构本地阶段机，也不要调用已废弃的工作流命令。
作者可见阶段
- 上下文预检 —
novel_context_preview - 写章 —
novel_write_chapter或后台novel_task_create(type=chapter_write)/novel_multi_write - 评审 —
novel_review_chapter或novel_task_create(type=chapter_review) - 作者确认修订 —
novel_revisions_list→ apply / reject - 复评与交付 — 应用后复评；交付图为 writing → review → revision → application → rereview → closure
工具
| 工具 | 用途 |
|---|---|
novel_status |
全书阶段、缺口、待确认项 |
novel_chapter_work |
单章正文/评审 revision、目标、伏笔义务、最近修改 |
novel_tasks_list |
后台任务列表 |
novel_task_get |
轮询任务 phase 与 result |
novel_task_create |
提交 chapter_write / chapter_review / revision_from_review / research |
novel_task_cancel / novel_task_retry / novel_task_confirm |
取消、可恢复重试、确认继续 |
novel_multi_write |
按大纲连续写章 |
novel_chapter_run_action |
Chapter Run V2 干预 |
novel_write_chapter |
同步写一章（长耗时；编排优先走任务） |
novel_review_chapter |
同步评审一章 |
长操作优先走任务系统，避免 HTTP 超时把服务端写锁做成孤儿任务。
用法
看进度
用户问「现在写到哪」：
novel_status- 需要章级细节时
novel_chapter_work（chapter_id） - 进行中的后台工作
novel_tasks_list
向作者报告：当前章、正文是否存在、评审是否新鲜、任务 phase、伏笔到期/超期/待埋入计数。不要输出内部 JSON 当主界面。
开始写下一章
novel_outline_read确认建议章纲novel_context_previewnovel_task_create(type=chapter_write)或novel_write_chapternovel_task_get直到完成或需要确认
中断恢复
会话中断后：
novel_tasks_list找 running / failed / awaiting_confirmation- recoverable 失败用
novel_task_retry；需要作者拍板用novel_task_confirm - 不要对已有正文再次
novel_write_chapter（服务端会 409）；回炉走revision_from_review→ regenerate → apply → 复评
连写
novel_multi_write 按大纲推进；每章仍要评审。低质量、低覆盖或含 blocker 时进入修订闭环，不要跳过门禁强行交付。
状态怎么说
| 观察 | 对作者说 |
|---|---|
任务 running |
正在写/审，给任务 id 与 phase |
awaiting_confirmation |
需要确认才能继续 |
failed 且 recoverable |
可重试，说明错误码 |
评审 stale |
正文已变，必须复评 |
| 交付未 closure | 应用修订后还没复评通过 |
与其他技能
- novel-creator：单章创作编排
- novel-reviewer：评审与修订提案
- dog-delivery-query：查询已物化交付图
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · -50 lines 331c7abbbef9
- 1mo ago First seen · 131 lines · 40 tokens per session scan A 71d26850b40a
workflow-manager is a skill published in the GitHub repository LiPu-jpg/Openwrite (868 stars, last pushed yesterday), licensed Apache-2.0. It adds 40 tokens to every session and 940 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
webnovel-init
A guided setup process for a web-novel project that collects creative requirements and creates the project’s initial structure and rules.
webnovel-write
A workflow for producing publishable web-novel chapters, from understanding the context through drafting, review, polishing, submission, and backup.
webnovel-plan
A planning tool for turning a web-novel outline into volume plans, timelines, and chapter outlines. It also writes new story settings back into the existing setting collection.
webnovel-review
A review agent for assessing the quality of fiction chapters. It produces a review report and writes review measurements back to the project.
webnovel-query
A Chinese-language skill for querying a web novel project's setting and story information, including characters, power systems, factions, and foreshadowing.
webnovel-learn
A project skill for learning from a web novel-writing project through the `/webnovel-learn` command. The provided details do not explain what learning tasks or outputs it performs.