shuohao-skills is a collection of agent skills for turning a novel into production materials for an AI short-drama pipeline, including character profiles, outlines, art references, scripts, and storyboards. It is for creators using Claude Code or Codex to plan and prepare AI-generated short videos. The catalogue add-ons implement the separate stages of this workflow and can also assemble their reports.
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 eternityspring/shuohao-skills --skill novel-staginggit clone --depth 1 https://github.com/eternityspring/shuohao-skillsWrote 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/eternityspring/shuohao-skills/novel-staging)<a href="https://agentmods.dev/skills/eternityspring/shuohao-skills/novel-staging"><img src="https://agentmods.dev/badge/skills/eternityspring/shuohao-skills/novel-staging/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/eternityspring/shuohao-skills/novel-staging"><img src="https://agentmods.dev/badge/skills/eternityspring/shuohao-skills/novel-staging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00259 | $0.03031 |
| Opus 5.5 | $0.00104 | $0.01212 |
| Sonnet 5.5 | $0.00052 | $0.00606 |
| Haiku 4.5 | $0.00026 | $0.00303 |
Grade A, and why
novel-staging 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 9d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
novel-staging
给 AI 短剧的每一场排开场站位：这一场一开始，每个出场的人在哪件东西旁、坐还是站、面朝哪。
为什么要单独排：短剧的每一段视频是一次独立生成，各段自己决定谁坐哪。群戏里六个人围一张长桌，第一段桌边多出五个没名字的宾客，第三段端酒坐着的人穿着主角的衣服。分镜的走位句一段接一段地交接，可每一场第一段的那一句是分镜自己编的，人看不到、改不动。所以在分镜之前，每场先定一份站位，分镜照抄。
| 交付 | 解决什么 |
|---|---|
| 认图结果（每个地点 4–20 件东西） | 站位只落在图上真有的东西旁——提示词里写了、图上没画的高台，人坐上去，每段视频都不一样 |
| 俯视平面 | 在透视的场景图上「桌子北侧」只是桌子后面一小条，前后深浅说不清；摆平了从上往下看，方位一目了然 |
| 开场站位（每场） | 谁开场就在、谁后来才进；坐还是站跟着剧本；主位、对立、孤立用位置说出来 |
| 开场走位句 | 程序按站位写，一字不差交给分镜：「胡二爷坐在第三排坐板上，面朝北；其余座位空着，没有其他人。」 |
| 能调整的报告 | 人在页面上拖动、改坐站和朝向，改完导出就是新的 staging.json，不用手改 JSON |
{baseDir} = 本文件所在目录。脚本 {baseDir}/scripts/novel-staging.mjs，零依赖，node 直接跑。
边界（不做的事）：不写分镜、不写剧本、不出图。只管开场那一刻——中途谁站起来、走到哪，是分镜的事，分镜从这一刻往后接着写。
Step 0 — 定输入
- script.json（novel-script 的产出）——硬前提：有哪几场、每场在哪个地点、谁出场、剧本怎么写的
- art.json（novel-art 的产出）——地点的名字；还没有图时，按它的锚点和主视角提示词推算
- outline.json（novel-outline 的产出）——人物编号对应的名字（C01 → 沈知微）
- 场景图——每个地点一张，放在
images/<地点名>-sheet.png（novel-art 的设定图版面，主视角在左上）。最好有：站位要落在图上真有的东西旁。没有图也能先排，但只是草稿（见 Step 2）
Step 1 — seed 骨架
node {baseDir}/scripts/novel-staging.mjs seed <script.json> --art <art.json> --outline <outline.json> > <workdir>/staging.json
确定性搬运：地点（按第一次出现的顺序）、每一场的集号场号、在哪个地点、光照时段、出场人物，每个人一条空的站位。这些事实不要让模型重新想一遍。 只要其中几集就加 --eps 1-3。
Step 2 — 认图（每个地点一次）
每个地点一份任务，能并发就并发。读 {baseDir}/references/read-pass.md，照着做：看场景图的主视角，认出人会站在旁边、坐在上面的东西，每件写名字、另一种语言、图上的框（box）、平面上的位置（plan）、能不能坐（on）、是不是圆的（round），4–20 件，readFrom 写 picture。
最重的两条：图里看不到的不写（提示词里有、图上没有的一律不写）；plan 按透视推算，不是照抄 box。
还没有图：按 art.json 的锚点和主视角提示词推算，readFrom 写 prompt、不写 box。报告会标出来；图出来以后重新认一遍、核对站位。
Step 3 — 排站位（每场一次）
每场一份任务，能并发就并发（同一个地点连着几场的，按顺序排，保持位置连贯）。读 {baseDir}/references/staging-pass.md，拿这一场的剧本、出场人物和这个地点认出来的东西，给 places 里每个人填：开场在不在（present）、在哪件东西（item）的哪一侧（side）、沿这一侧多远（along）、坐还是站（posture）、面朝哪（facing）；再填群演（extras / extrasWhere）和一句 why。
最重的三条：后来才进来的人开场不在；剧本没写有别人就没有群演；只使用认出来的那些东西。
Step 4 — 校验 ⛔ 不能跳
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/report-en.jpg 157 KB
- assets/report.webp 67 KB
- examples/渡口-staging.json 17 KB
- README.en.md 5.8 KB
- README.md 6.3 KB
- references/read-pass.md 4.1 KB
- references/report-style.md 4.0 KB
- references/schema.md 4.9 KB
- references/staging-pass.md 4.2 KB
- references/test-fixtures/upstream/README.md 613 B
- references/test-fixtures/upstream/渡口-art.json 16 KB
- references/test-fixtures/upstream/渡口-outline.json 9.4 KB
- references/test-fixtures/upstream/渡口-script.json 38 KB
- scripts/novel-staging.mjs 69 KB runs code
- scripts/selftest.mjs 17 KB runs code
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.
- 9d ago First seen · 169 lines · 259 tokens per session scan A 0afc3ade75ee
novel-staging is a skill published in the GitHub repository eternityspring/shuohao-skills (5,046 stars, last pushed yesterday), licensed Apache-2.0. It adds 259 tokens to every session and 3,031 once invoked, about $0.0010 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 2026-10-16.
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