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Color Grading Primary Workflow

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geegl
color-grading-primary-workflow

当用户需要对视频/影片进行一级校色时调用。核心工作流：先调对比度（Lift/Gamma/Gain）建立良好的黑位和白位，再调色彩平衡校正偏色，最后微调饱和度。三个步骤有强交互性需迭代调整。

Overview

Publishergeegl
Repositorydirectorskills
Skill namecolor-grading-primary-workflow
Stars
68
Forks
7
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by geegl on GitHub. Read the source before you install it.

Installation

Install the Color Grading Primary Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/geegl/directorskills/tree/main/output/books/color-grading/color-grading-primary-workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/geegl/directorskills.git /tmp/directorskills
mkdir -p .claude/skills
cp -r /tmp/directorskills/output/books/color-grading/color-grading-primary-workflow .claude/skills/color-grading-primary-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Color Grading Primary Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Color Grading Primary Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Color Grading Primary Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

一级校色三步工作流

R — 原文 (Reading)

比较好的调整方法是，在最开始调整时先调整画面对比度，然后再调整色彩。颜色和反差调整之间的互动会十分频繁，其中一组进行调整就会影响另一组。 — Van Hurkman, 第4章, p.g_0037

I — 方法论骨架 (Interpretation)

一级校色是所有调色工作的基础，目的是建立技术正确的画面——有良好的黑位、白位、色彩平衡和饱和度。其核心是一个三步迭代工作流：

  1. 对比度优先：用Lift/Gamma/Gain将画面的黑位设到0%附近、白位设到90-100%附近、中间调分布合理。这一步决定画面的"骨架"。
  2. 色彩平衡：在对比度已定的基础上，用色彩平衡控件（三路色彩校正或RGB曲线）校正偏色。先调暗部再调中间调最后调高光，或使用Offset整体校正。
  3. 饱和度微调：检查并调整整体饱和度水平。

关键认知：这三步不是线性的而是迭代的。调整对比度会改变饱和度（RGB处理方式下扩大对比度自动增加饱和度），调整色彩平衡会改变对比度感知。因此每一步完成后需要回到前几步检查。

A1 — 书中的应用 (Past Application)

案例：扩展高对比度画面 (第3章)

  • 问题: 原始素材曝光保守，黑位在23%、白位在87%，画面看起来平淡。
  • 方法论的使用: 先降低Lift将黑位拉到5%，再提高Gain让高光接近100%，最后微调Gamma平衡中间调。对比度扩展后画面"像把胶片灰尘擦掉了"。
  • 结论: 看似很小的对比度调整能带来显著的画面改善。

案例 2: 修复室内偏绿素材

  • 问题: 日光灯室内拍摄的素材整体偏绿，人物肤色发灰发绿，客户要求'看起来自然'
  • 方法论的使用: 先用Lift/Gamma/Gain将黑位拉到5%、白位推到95%建立对比度骨架，再用色彩平衡控件在中间调区域加入品红色偏抵消绿色，最后检查矢量示波器确认肤色回到I-bar附近并微调饱和度
  • 结论: 三步迭代工作流在处理偏色素材时尤其有效——对比度为色彩校正提供了稳定的亮度基础
  • 结果: 肤色恢复自然，室内光感清透

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 拿到一段未调色的素材需要建立基础画面——"这段素材看起来灰灰的/偏色了"
  2. 客户要求"让画面看起来更生动/更有电影感"——需要先打好一级校色基础
  3. 准备进行二级调色或创意Look——需要先确保一级校色技术正确

语言信号

  • "画面看起来灰/平淡/没有对比度"
  • "偏色了/偏暖/偏冷/偏绿"
  • "需要调色但不知道从哪开始"
  • "先做基础调色"、"一级校色"

E — 可执行步骤 (Execution)

  1. 设定对比度

    • 降低Lift直到波形底部到达5%左右（不要切掉暗部细节）
    • 提高Gain直到高光接近但不超过100%（保留镜面高光的微小峰值）
    • 调整Gamma平衡中间调至画面看起来自然
    • 判停：广播监视器上画面看起来有"密度"和"活力"

  2. 校正色彩平衡

    • 检查RGB分量示波器，找到三个通道不对齐的位置
    • 用色彩平衡控件或Offset整体校正偏色
    • 按暗部→中间调→高光的顺序逐一平衡
    • 判停：灰色/白色物体在监视器上看起来中性

  3. 微调饱和度

    • 检查矢量示波器，确认饱和度在合理范围

    • 注意：步骤1可能已自动增加了饱和度

    • 判停：色彩鲜艳但不过度，肤色看起来健康

    • 🔴 判停条件: 若经过3轮迭代对比度和色彩仍无法平衡（如黑位始终偏低或偏色反复出现），检查素材本身是否有技术缺陷（如LOG素材未正常化）

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

  • 素材是Log编码且尚未正常化（使用color-grading-log-workflow）
  • 用户只需要对特定颜色做局部调整（使用color-grading-hsl-qualifier）
  • 用户已经在做镜头匹配而非单镜头调色（使用color-grading-shot-matching）

失败模式

  • 交互循环: 对比度和色彩互相影响导致反复调整——设定迭代上限（通常3轮即可）
  • 饱和度失控: 扩大对比度后忘记检查饱和度，画面过饱和
  • 中间调偏移: 大幅调整Lift/Gamma/Gain后中间调偏离预期——每次大幅调整后重新评估中间调

作者的盲点 / 时代局限

Alexis Van Hurkman 的调色方法论基于专业 DaVinci Resolve 工作流，对轻量级工具（如 Premiere Lumetri、手机调色 App）的适配讨论不足。他假设调色师有参考级监视器和校色环境，独立创作者在消费级显示器上的调色策略覆盖较少。书中色彩科学理论偏重 REC.709，对 HDR/WCG 工作流的实操指导相对薄弱。

此外，一级调色的讨论偏重技术准确性，对创意决策的讨论不足——何时应该'技术正确'vs 何时应该'感觉正确'。此外，他假设调色师有参考级监视器，对在消费级显示器上进行一级调色的策略和风险覆盖较少。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Color Grading Primary Workflow AI skill do?

当用户需要对视频/影片进行一级校色时调用。核心工作流：先调对比度（Lift/Gamma/Gain）建立良好的黑位和白位，再调色彩平衡校正偏色，最后微调饱和度。三个步骤有强交互性需迭代调整。

Why use Color Grading Primary Workflow on TypingMind?

Because you install it once and use it with any model. Color Grading Primary Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Color Grading Primary Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/geegl/directorskills/tree/main/output/books/color-grading/color-grading-primary-workflow. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Color Grading Primary Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Color Grading Primary Workflow?

As many as you like. As long as a model supports skills, you can use Color Grading Primary Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Color Grading Primary Workflow AI skill free?

Yes. It is published on GitHub by geegl under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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