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 character-refsgit 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/character-refs)<a href="https://agentmods.dev/skills/eternityspring/shuohao-skills/character-refs"><img src="https://agentmods.dev/badge/skills/eternityspring/shuohao-skills/character-refs/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/character-refs"><img src="https://agentmods.dev/badge/skills/eternityspring/shuohao-skills/character-refs.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 | $0.00242 | $0.03880 |
| Opus 5.5 | $0.00097 | $0.01552 |
| Sonnet 5.5 | $0.00048 | $0.00776 |
| Haiku 4.5 | $0.00024 | $0.00388 |
Grade A, and why
character-refs 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 11d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
character-refs
输入一段角色描述，输出一组角色参考图——角色从哪来都行：小说改编、自己编的故事、临时想到的一个人。一张正面全身锚点，其余每张都只参考这张锚点。 给视频模型（H3 / Wan / Seedance）当参考图使用，也给人看。
{baseDir} = 本文件所在目录。脚本 {baseDir}/scripts/character-refs.mjs，零依赖，node 直接跑。
**这是仓库里唯一真出图的 skill。**其余 skill 只交提示词，因为出图那一刻才能决定模型和画风； 这个 skill 就是「那一刻」——模型在 Step 0 由用户选定，画风在建角色时选择（默认动漫，另有写实预设，也能挂自定义画风）。
核心规则（先读懂再动手）
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**一组图只有一个根：正面全身锚点。**只有它是文生图，其余全是「锚点 + 一句改图指令」。 头发、脸、领口三类细节额外挂确认过的正脸大头照。图与图之间不再互相参考—— 所以任何一张出坏了，单独重出这一张就行，不会连锁。
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分档，默认出到第二档：
档位 内容 什么时候出 1 正面全身（锚点） 总是，第一张 2（默认） 正脸大头照、90° 侧面（面朝画面右侧）、背面 默认就出齐 3 细节图（最多 8 张）：用户点名的、角色最能认出来的地方；没有就用默认的头发 / 发饰、领口、袖口、鞋 有配饰或服装局部的特写 4（默认不出） 45° 大头照（面朝画面左侧） 用户明确要 H3 实测：只给正面全身，服装和背影都对，但脸会走样；加一张大头照，脸最接近。所以默认出齐第二档。 只要锚点（比如先看看长相再说）：
gen <asset.json> --tier 1。 -
模型不锁定：主图与派生图可以使用不同模型，重出单张也可以换模型。一致性来自「同一张锚点」， 不来自「同一个模型」（实测 Qwen 锚点 + GPT 派生、GPT 锚点 + Qwen 派生、同组混用都衔接得上）。 每张图记录自己的模型，同组混用只在报告里提醒。
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冻结的只有三样：锚点文件（sha256）、画风快照、文字描述指纹。任何一样变了， 依赖它的图标成「过期」——只标记，不删除，旧版本全部留着。
Step 0 — 初次设置（每台机器一次）
node {baseDir}/scripts/character-refs.mjs config --show
输出末尾写「还缺：……」就要问用户这三件事，一次问完：
- 主图（锚点）使用哪个模型：
qwen—— Qwen Image 2.1，走自己的 ComfyUI 服务器。快（每张 25–45 秒）、尺寸精确、不吃订阅额度。 需要COMFY_URL（可选COMFY_USER/COMFY_PASS），放环境变量或一个 .env 文件codex—— codex 内置出图（GPT）。不要 API key，但吃 ChatGPT 订阅额度（Plus 约 25 张会触顶，几小时后恢复），每张 80–160 秒openai—— OpenAI Images API，默认gpt-image-2。按量计费，要OPENAI_API_KEY；也能指到兼容接口（--openai-base-url）custom:<名字>—— 用户自己的出图命令，见references/models.md
- 派生图使用哪个模型：默认与主图相同。可以分开（比如锚点 GPT、派生 Qwen）
- 出完锚点要不要停下来等人确认：推荐
yes——锚点是全组的根，它不对后面全白出。 选no则自动放行，报告里标「未经人工确认」；没过检查门的锚点不会自动放行，要人看过再confirm或重出
node {baseDir}/scripts/character-refs.mjs config --model qwen --confirm-anchor yes --qwen-env-file <.env 路径>
# 分开设置：--anchor-model codex --derive-model qwen
配置写在 {baseDir}/config.local.json（不进版本控制，重新安装不会覆盖）。以后默认一直使用它，
用户说「换成 xx」就重新跑 config。不要把 .env 里的密码或 key 打印出来，config --show 已经打码。
What ships with it
16 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.webp 93 KB
- examples/阿禾-intake.json 2.8 KB
- examples/阿禾-描述.txt 324 B
- README.en.md 7.1 KB
- README.md 7.6 KB
- references/intake.md 5.7 KB
- references/models.md 5.1 KB
- references/prompting.md 8.0 KB
- references/schema.md 4.4 KB
- scripts/character-refs.mjs 34 KB runs code
- scripts/core.mjs 32 KB runs code
- scripts/i18n.mjs 11 KB runs code
- scripts/looks.mjs 4.9 KB runs code
- scripts/models.mjs 19 KB runs code
- scripts/png.mjs 6.9 KB runs code
- scripts/selftest.mjs 39 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.
- 11d ago Changed 16f39d589dbf
- 28d ago First seen · 246 lines · 242 tokens per session scan A 9f20a635e81d
character-refs is a skill published in the GitHub repository eternityspring/shuohao-skills (5,046 stars, last pushed yesterday), licensed Apache-2.0. It adds 242 tokens to every session and 3,880 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 2026-09-27.
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