laohan-fenjingtishici

laohan-fenjingtishici is a skill for Claude Code, Codex from hanzhcn/laohan-skills. It costs 99 tokens per session (1,398 once invoked), scanned A, original, MIT.

A workflow for creating and checking image prompts for video storyboards. It produces prompts for image models such as FLUX, SDXL, and Gemini, or validates and separates returned storyboard results into individual files.

In plain words
What is it for?
Use it to generate storyboard prompts from a video length and product description, or to check and split generated storyboard output.
Why use it?
It catches common storyboard problems such as the wrong frame count, inconsistent product placement, missing camera details, or changing scenes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to generate storyboard prompts from a video length and product description, or to check and split generated storyboard output.

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Install with agentmods
npx agentmods add skills/hanzhcn/laohan-skills/laohan-fenjingtishici
Install

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.

Any agent
npx skills add hanzhcn/laohan-skills --skill laohan-fenjingtishici
Clone the repo
git clone --depth 1 https://github.com/hanzhcn/laohan-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for laohan-fenjingtishici

README.md
[![agentmods](https://agentmods.dev/badge/skills/hanzhcn/laohan-skills/laohan-fenjingtishici/github.svg)](https://agentmods.dev/skills/hanzhcn/laohan-skills/laohan-fenjingtishici)
Your own site
<a href="https://agentmods.dev/skills/hanzhcn/laohan-skills/laohan-fenjingtishici"><img src="https://agentmods.dev/badge/skills/hanzhcn/laohan-skills/laohan-fenjingtishici/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.

agentmods 80×15 button for laohan-fenjingtishici

Your own site · 80×15
<a href="https://agentmods.dev/skills/hanzhcn/laohan-skills/laohan-fenjingtishici"><img src="https://agentmods.dev/badge/skills/hanzhcn/laohan-skills/laohan-fenjingtishici.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,398 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00099 $0.01398
Opus 5 $0.00049 $0.00699
Sonnet 5 $0.00020 $0.00280
Haiku 4.5 $0.00010 $0.00140

Measured 12d ago against content hash b72eee6906dd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

laohan-fenjingtishici 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 12d 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.

laohan-fenjingtishici/SKILL.md · 111 lines

How it starts

The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.

分镜提示词生成与校验

为产品带货/短视频生成分镜图片提示词,并校验输出质量。

不适用场景

  • 生成视频本身 → 这是分镜图片提示词,不是视频生成
  • 生成封面图 → 用 laohan-fengmianqiuzhi
  • 生成口播稿 → 用 laohan-chuangzuo
  • 没有产品描述 → 模式1必须提供产品视觉描述才能生成

使用

# 模式1:生成 Gemini 提示词模板
/laohan-fenjingtishici <视频秒数> <产品视觉描述>

# 模式2:校验并拆分 Gemini 返回的分镜
/laohan-fenjingtishici <用户粘贴的 Gemini 输出>

模式1:生成提示词模板

输入视频秒数和产品描述,输出可直接粘贴到 Gemini 的提示词(内嵌所有规则)。

提示词模板内容见 references/prompt_template.md

模板中需要替换的变量:

  • {VIDEO_LENGTH} → 实际视频秒数
  • {FRAME_COUNT} → 向上取整(视频秒数 ÷ 5)
  • {PRODUCT_DESCRIPTION} → 产品的视觉特征(颜色、材质、形状)

模式2:校验并拆分

用户把 Gemini 返回的分镜结果粘贴过来,按检查清单逐项验证,通过后拆分为独立文件。

检查清单

校验以下 8 项,每项通过/失败+具体问题:

# 检查项 通过标准
1 帧数正确 帧数 = 向上取整(视频秒数 ÷ 5)
2 产品占位符一致 所有帧中产品位置都使用 [PRODUCT] 占位符(除非物理状态变化)
3 动势预设 每帧都有 mid-action / 动态姿势描述
4 正负分离 Positive Prompt 和 Negative Prompt 已分离
5 无填充词 没有 "Generate an image..."、"HIGH RESOLUTION" 等对话式指令
6 无 meta-tags 没有 "(Product reference: ...)" 标签,产品用 [PRODUCT] 占位
7 摄影术语 包含景别、角度、f值、焦距、光源方向、色温K值、照明技法
8 场景一致性 所有帧的 [SCENE] 描述完全相同(背景/环境/氛围不可跳变),只允许机位和光位变化

校验失败处理

  • 项 1(帧数错误):自动修正帧数,提示用户重新生成
  • 项 2(占位符不一致):标出差异帧,建议统一替换为 [PRODUCT]
  • 项 3-8:标出具体问题帧和修改建议,不自动修改(保持 Gemini 原始输出)

拆分规则

校验通过后,每帧拆为一个文件:

{输出目录}/frame_1_prompt.txt
{输出目录}/frame_2_prompt.txt
...
{输出目录}/frame_N_prompt.txt

输出目录默认为当前项目目录,可由用户指定。

文件格式

每个文件包含帧标题 + Positive Prompt + Negative Prompt,和 Gemini 输出格式一致:

Frame N [timestamp - storytelling purpose]:

Positive Prompt:
[完整提示词]

Negative Prompt:
[negative 内容]

提示词工程规则

这些规则同时嵌入模板和用于校验:

  1. 5秒分段:每帧对应一个 5 秒视频片段(Wan 2.2 的 81 帧 ÷ 16fps)
  2. 余数向上取整:32 秒视频 → 7 帧(35 秒),最后一帧按完整 5 秒节奏描述,后期裁剪
  3. 单一节拍:每帧只描述一个动作/时刻
  4. 纯视觉描述:适配扩散模型,不含对话式指令,分辨率由工具控制
  5. [PRODUCT] 占位符:产品外观用 [PRODUCT] 占位,生图时由上传的产品参考图决定外观,不写具体产品描述
  6. 动作适配(CRITICAL):彻底根除参考视频中绑定原产品的专属交互(套硅胶壳、穿挂绳、塞入耳塞),替换为通用高级商业交互("adjusting outer textures"、"arranging items gracefully"、"presenting side-by-side")
  7. 环境强制隔离:剥离参考视频的特定背景/风格,强制用 "real commercial photography style"
  8. 场景一致性锁定:所有帧的 [SCENE] 必须完全相同(同一背景/环境/氛围),只允许机位和光位变化
  9. 动势预设:每帧描述 mid-action 姿态,为 I2V 提供运动张力
  10. 正面/负面分离:可直接复制到 ComfyUI 的两个 CLIPTextEncode 节点
  11. 负面提示词语义floating objects 压制的是无因漂浮噪声,不与 positive 中的手部受因悬停矛盾
  12. 负面基线:每帧 Negative Prompt 默认包含 "CG, glowing effects, over-saturation"

Read the full file on GitHub · 111 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 12d ago First seen · 111 lines · 99 tokens per session scan A b72eee6906dd

Subscribe to this mod's changes

laohan-fenjingtishici is a skill published in the GitHub repository hanzhcn/laohan-skills (11 stars, last pushed 2d ago), licensed MIT. It adds 99 tokens to every session and 1,398 once invoked, about $0.0005 per session on Opus 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.

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