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Color Correction

Community
artokun
color-correction

Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / flat / dull / over-saturated / color-cast, or when deciding between a color-match and a contrast/levels fix.

Overview

Publisherartokun
Repositorycomfyui-mcp
Skill namecolor-correction
Stars
811
Forks
137
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Color Correction 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/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction
  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/artokun/comfyui-mcp.git /tmp/comfyui-mcp
mkdir -p .claude/skills
cp -r /tmp/comfyui-mcp/plugin/skills/color-correction .claude/skills/color-correction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Color Correction 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 Correction 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 Correction 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.

Color Correction (measure, don't eyeball)

The core principle

You cannot reliably judge color from a storyboard or contact sheet. "Is it washed out?" flip-flops by eye, especially on AI-gen video. Make color measurable with the get_image (action:"analyze_color") MCP tool, read the numbers like a colorist reads scopes, pick the fix the data points to, then re-measure to confirm. The whole skill is this loop:

extract a frame ─► get_image (action:"analyze_color") ─► read black/white points + contrast + saturation
       ▲                                          │
       │                                          ▼
   re-measure  ◄──── apply fix (levels / contrast / match) ◄── diagnose from the numbers

Origin: on a WAN-Animate render we argued for many turns over whether the clip was "washed out." The instant we measured it, the answer was unambiguous and the correct fix (a contrast stretch, not the color-match nodes we'd been adding) fell straight out.


The get_image (action:"analyze_color") tool

Read-only. Source = asset_id, a ComfyUI output ref (filename/subfolder/type), or an image path (absolute, or under the output dir). It returns per-image stats, heuristic flags, a one-line verdict, and optionally an overlaid R/G/B/luma histogram PNG.

get_image({ action: "analyze_color", filename: "render_00007_.png" })       # absolute numbers
get_image({ action: "analyze_color", path: "frame.png", reference_path: "src.jpg" })  # + shot-match deltas
get_image({ action: "analyze_color", filename: "x.png", histogram: true })  # + histogram image

For videos, get_image (action:"analyze_color") is image-only (no ffmpeg dep). Extract a frame first with the ComfyUI venv's cv2:

bash
"<comfy-venv>/python" -c "import cv2; c=cv2.VideoCapture(r'IN.mp4'); n=int(c.get(7)); \
  c.set(1, n//2); _,f=c.read(); cv2.imwrite(r'frame.png', f)"

Grab the middle frame, or frame 0. For window-drift checks grab a frame from each window.

What the numbers mean (8-bit)

FieldReads like a scopeHealthy-ish
luma.blackPoint (1st pct)where shadows bottom out~0–16 (lifted if >16)
luma.whitePoint (99th pct)where highlights top out~240–255 (dim if <235)
luma.contrast (std)overall punch~45+ (flat if <45)
luma.dynamicRangewhite−blackwide is good
saturation.meanSaturation (HSV S)vectorscope spread~0.25+ (dull if <0.22)
channels.{r,g,b}Mean + castHintRGB parade / white balancespread <~12 = neutral
luma.clippedHighPct/LowPctblown / crushed pixelskeep low (<~2%)

Flags: washedOut, lowContrast, liftedBlacks, dimHighlights, lowSaturation, colorCast.


The "washed out" signature

Washed out means compressed tonal range, and it has an exact fingerprint:

  • whitePoint well below 255 (e.g. 191), so highlights never reach white
  • blackPoint lifted off 0 (e.g. 45), giving milky shadows
  • contrast low (std < 45)
  • often normal saturation. Washout is usually not a saturation problem.

If you see that, the fix is a levels / contrast stretch, not a color match. In a real case, a WAN-Animate frame measured blackPoint 45 / whitePoint 191 / contrast 43, clearly a range problem; saturation 0.25 was fine.


The load-bearing insight: a reference-match can't add contrast the source lacks

The instinct is to "match the render to the input photo" with a color-match node (ColorMatchV2 mkl/hm, ImageColorMatchAdobe+, easy imageColorMatch). Measure the reference first. If the reference is itself flat (e.g. a casual phone selfie with blackPoint 40, contrast 44), matching to it cannot produce punch; you'll match your way to the same flat numbers. In the real case, mkl and Adobe matches both left the frame flagged washedOut (whitePoint only crept from 191 to ~218).

So:

  • Use a reference-match (ColorMatchV2, ImageColorMatchAdobe+) when you want to match a known-good graded frame or shot-match across clips, and the reference is actually good.
  • Use a levels / contrast stretch when the defect is compressed range (the washout case). It targets full range regardless of the reference. This is usually the real fix.

The fix: contrast / levels stretch (core nodes, no install)

AdjustContrast (core comfy_extras.nodes_dataset, category image/adjustments) has one factor (1.0 = none, >1 = more). It pivots around mid-gray, so it pushes the white point up and the black point down together. Tune it by measurement, not feel. A real measured sweep on the washout frame:

factorblackPointwhitePointcontrastsatclippedHighverdict
1.329239560.330%✅ not washed (slightly soft)
~1.4~20~247~60~0.38~1–2%✅ sweet spot
1.67254670.447.2% ⚠️punchy but blows highlights

Pick the factor that lands whitePoint ~248 to 255 with clippedHighPct < ~2%. Going too far (1.6 here) blows highlights and over-warms, because per-channel contrast drops blue more than red and castHint worsens. The sweet spot was ~1.4.

Other levers when contrast alone isn't enough:

  • CurveEditor (core, utilities) feeds a CURVE for precise black/white-point and gamma control (a true levels curve) when you need more than a single contrast pivot.
  • AdjustContrast plus a small saturation/brightness adjust (same image/adjustments family) to fine-tune after the stretch.
  • A mild reference-match after the stretch, only if you need to shot-match.

The sandbox pattern (test corrections side by side, then measure)

Don't tune blind on the full pipeline. Build a tiny separate workflow (panel_new_workflow) and let one run produce several candidates you then measure:

LoadImage (the washed frame, staged into input/)
LoadImage (the reference, if shot-matching)
        │
        ├─► AdjustContrast factor 1.3 ─► SaveImage "contrast13"
        ├─► AdjustContrast factor 1.6 ─► SaveImage "contrast16"
        ├─► ColorMatchV2 (mkl, ref)   ─► SaveImage "balance_mkl"
        └─► ImageColorMatchAdobe+(LAB)─► SaveImage "balance_adobe"

Run once, then get_image (action:"analyze_color") every output, plus the reference and the untouched frame, and compare blackPoint / whitePoint / contrast / saturation / clippedHigh. Whichever lands the numbers in range wins; interpolate the factor (1.3 vs 1.6 gives 1.4) and confirm with one more pass. Stage the frame into the ComfyUI input dir first (cp the extracted PNG there) so LoadImage can see it. Input and output dirs may be custom, so don't guess paths.


Porting the fix into a render graph

Place the chosen correction after decode, before the save. For a WAN/video graph that's right after WanVideoDecode (or the per-chunk color-match) and feeding the VHS_VideoCombine/save node. One AdjustContrast node is usually the whole fix. Keep it as a single inline node (or a small bypassable group) so it's easy to toggle and re-tune. Re-render a clip, extract a frame, get_image (action:"analyze_color") it, and nudge the factor to hit whitePoint ~250.


Video-specific gotchas

  • Per-window drift (WAN long video). WAN-Animate/long clips render in temporal windows and can desaturate or dim across them. Measure a frame from the first window and a late window. If they differ, that's drift, not a global grade issue; use the embeds' between-window colormatch, not a final stretch.
  • Relight LoRA is not levels. A WanAnimate relight LoRA changes lighting, not black/white points. It won't fix a compressed-range washout. Measure before and after to prove it.
  • Don't over-stretch. Watch clippedHighPct; blown highlights are unrecoverable. Prefer the lower factor that still clears dimHighlights.
  • Cast is often inherited. If the reference has the same warm/cool cast (festival/sunset light), a matching cast on the render is correct. Don't "fix" it.

Quick reference — the decision tree

get_image (action:"analyze_color") on the frame
  ├─ washedOut / lowContrast / dimHighlights ........ contrast/levels stretch (AdjustContrast ~1.4 → measure)
  ├─ lowSaturation only ............................. saturation boost (small)
  ├─ colorCast (and reference is neutral) ........... white-balance / neutralization, or reference-match
  ├─ want to MATCH a known-good graded frame ........ ColorMatchV2 / ImageColorMatchAdobe+ (ref must be good)
  └─ blackPoint/whitePoint already 0/255, sat ok .... color is healthy — stop touching it

Always re-measure after the fix. If get_image (action:"analyze_color") still flags it, the fix was wrong. Adjust and measure again. Numbers over vibes.

Sources

  • Official: comfyui-mcp get_image action:"analyze_color" product behavior (this repo).
  • Empirical: washed-out signature and levels-stretch recipes from observed renders.

Frequently asked questions

What does the Color Correction AI skill do?

Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / f...

Why use Color Correction on TypingMind?

Because you install it once and use it with any model. Color Correction 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 Correction in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Color Correction?

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 Correction?

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

Is the Color Correction AI skill free?

Yes. It is published on GitHub by artokun 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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