Iib
zanllp/infinite-image-browsing
Interact with IIB (Infinite Image Browsing) service for searching, browsing, tagging, and organizing AI-generated images.
Diagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a…
$ npx skills add artokun/comfyui-mcp --skill color-correction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install artokun/comfyui-mcp color-correction --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/color-correction .claude/skills/color-correction && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "color-correction" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction into .claude/skills/color-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "color-correction", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correctionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add artokun/comfyui-mcp --skill color-correction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install artokun/comfyui-mcp color-correction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugin/skills/color-correction .agents/skills/color-correction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "color-correction" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction into .agents/skills/color-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "color-correction", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add artokun/comfyui-mcp --skill color-correction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install artokun/comfyui-mcp color-correction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugin/skills/color-correction .cursor/skills/color-correction && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "color-correction" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction into .cursor/skills/color-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "color-correction", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/artokun/comfyui-mcp.git --path plugin/skills/color-correction--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add artokun/comfyui-mcp --skill color-correction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install artokun/comfyui-mcp color-correction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugin/skills/color-correction .gemini/skills/color-correction && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "color-correction" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction into .gemini/skills/color-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "color-correction", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install artokun/comfyui-mcp color-correctionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add artokun/comfyui-mcp --skill color-correction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugin/skills/color-correction .github/skills/color-correction && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "color-correction" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction into .github/skills/color-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "color-correction", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add artokun/comfyui-mcp --skill color-correction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install artokun/comfyui-mcp color-correction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugin/skills/color-correction .opencode/skills/color-correction && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "color-correction" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/color-correction into .opencode/skills/color-correction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "color-correction", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
color-correctionDiagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a…
Color Correction is an agent skill from artokun/comfyui-mcp. Diagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") 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…
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Media & Creative, covering Diffusion and image models. The repository describes itself as: Local-first, agent-native control plane for ComfyUI — MCP server + sidebar agent that generates images, video & audio, authors and runs workflows, and edits your live graph in… The licence is MIT.
Read from SKILL.md and the folder at commit 6ad6fc0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Color Correction loads about 2.4k tokens when it runs. Until then it costs about 180 tokens; SKILL.md has 1,011 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from artokun/comfyui-mcp at commit 6ad6fc0, republished under its MIT licence (© artokun). 1,011 words, ~2,438 tokens.
.claude/skills/color-correction/SKILL.md (or your agent's skills folder).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 numbersOrigin: 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.
get_image (action:"analyze_color") toolRead-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 imageFor videos, get_image (action:"analyze_color") is image-only (no ffmpeg dep). Extract a frame first with the
ComfyUI venv's cv2:
"<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.
| Field | Reads like a scope | Healthy-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.dynamicRange | white−black | wide is good |
saturation.meanSaturation (HSV S) | vectorscope spread | ~0.25+ (dull if <0.22) |
channels.{r,g,b}Mean + castHint | RGB parade / white balance | spread <~12 = neutral |
luma.clippedHighPct/LowPct | blown / crushed pixels | keep low (<~2%) |
Flags: washedOut, lowContrast, liftedBlacks, dimHighlights, lowSaturation, colorCast.
Washed out means compressed tonal range, and it has an exact fingerprint:
whitePoint well below 255 (e.g. 191), so highlights never reach whiteblackPoint lifted off 0 (e.g. 45), giving milky shadowscontrast low (std < 45)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 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:
ColorMatchV2, ImageColorMatchAdobe+) when you want to match
a known-good graded frame or shot-match across clips, and the reference is actually good.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:
| factor | blackPoint | whitePoint | contrast | sat | clippedHigh | verdict |
|---|---|---|---|---|---|---|
| 1.3 | 29 | 239 | 56 | 0.33 | 0% | ✅ not washed (slightly soft) |
| ~1.4 | ~20 | ~247 | ~60 | ~0.38 | ~1–2% | ✅ sweet spot |
| 1.6 | 7 | 254 | 67 | 0.44 | 7.2% ⚠️ | 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.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.
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.
colormatch, not a final stretch.clippedHighPct; blown highlights are unrecoverable. Prefer
the lower factor that still clears dimHighlights.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 itAlways 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.
get_image action:"analyze_color" product behavior (this repo).© artokun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in plugin/skills/color-correction of artokun/comfyui-mcp.
Open the folder on GitHubat commit 6ad6fc0
Color Correction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Color Correction this skillartokun/comfyui-mcp | 803 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Iibzanllp/infinite-image-browsing | 1.4k | — | ~3.3k | Automated safety check: Pass | MIT | |
| 9Router Image Generationdecolua/9router | 31k | — | ~830 | Automated safety check: Pass | MIT | |
| Character Refseternityspring/shuohao-skills | 4.3k | — | ~1.7k | Automated safety check: Warn | Apache-2.0 | |
| H3 Videoagent-next/video-agent | 120 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Stable Diffusion with DiffusersOrchestra-Research/AI-Research-SKILLs | 13k | 5 repos | ~3.2k | Automated safety check: Pass | MIT |
zanllp/infinite-image-browsing
Interact with IIB (Infinite Image Browsing) service for searching, browsing, tagging, and organizing AI-generated images.
decolua/9router
Generates images through a 9Router gateway's image endpoint, with model discovery, the request fields and per-provider quirks for OpenAI, Gemini, MiniMax and others.
eternityspring/shuohao-skills
给任何故事里的角色真出参考图(小说改编、自己原创的故事、单独设计一个角色都行,不需要小说原文): 一段话描述角色,拆成分层字段、补全后确认, 先出一张正面全身锚点,其余视图(大头照、90° 侧面、背面、细节、45° 大头照)都只参考这张锚点, 按需分档出图。每张图带标识、可单独重出,重出后自动标出哪些图过期。
agent-next/video-agent
OpenVideo skill (v0.1.0): generate high-quality local video with the OpenVideo product (MiniMax H3 backend).
Orchestra-Research/AI-Research-SKILLs
Generates and edits images with Stable Diffusion through Hugging Face Diffusers, covering text-to-image, image-to-image, inpainting, SDXL and custom pipelines.
open-octo/octo-agent
Acquire images as files — generate them with an AI image model (14 providers: OpenAI/gpt-image, Gemini, Qwen, Zhipu, Volcengine, Stability, FLUX, Ideogram, MiniMax, and more), search openly-licensed…
artokun/comfyui-mcp
Train custom LoRAs with ostris AI-Toolkit. An agent skill from artokun/comfyui-mcp.
artokun/comfyui-mcp
Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT).
artokun/comfyui-mcp
Discover Civitai models with the BUILT-IN downloadmodel action:"searchcivitai" and install/generate them locally.
artokun/comfyui-mcp
Authoring ComfyUI v2 frontend extensions with @comfyorg/extension-api, covering defineNode/defineExtension/defineWidget, shell UI (sidebar tabs, commands, hotkeys), typed events, and handles.
artokun/comfyui-mcp
Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed.
artokun/comfyui-mcp
A skill your agent uses when installing a model family from an installer pack, or when building/deriving a new pack from an upstream installer or a workflow JSON.
Categories
Diagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a…. Color Correction is an agent skill from artokun/comfyui-mcp. Diagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet.
Color Correction fits situations like: A render looks washed out / flat / dull / over-saturated / color-cast; deciding between a color-match and a contrast/levels fix.
Run `npx skills add artokun/comfyui-mcp --skill color-correction -a claude-code`. Or copy the skill folder (plugin/skills/color-correction in artokun/comfyui-mcp) into .claude/skills/color-correction in your project. Claude Code loads it when a task matches its description.
Run `npx skills add artokun/comfyui-mcp --skill color-correction -a codex`. Or copy the skill folder (plugin/skills/color-correction in artokun/comfyui-mcp) into .agents/skills/color-correction in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add artokun/comfyui-mcp --skill color-correction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/color-correction, .gemini/skills/color-correction, .github/skills/color-correction and .opencode/skills/color-correction in your project.
SKILL.md names no scripts, command-line tools or credentials: Color Correction is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Color Correction is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.4k tokens (SKILL.md is roughly 9.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Color Correction: Iib (zanllp/infinite-image-browsing, 1.4k stars), 9Router Image Generation (decolua/9router, 31k stars), Character Refs (eternityspring/shuohao-skills, 4.3k stars) and H3 Video (agent-next/video-agent, 120 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
artokun (a GitHub user) maintains it in artokun/comfyui-mcp, which has 803 GitHub stars. The repository holds 42 skills in this directory. The repository was last updated on October 5, 2026.
Source: artokun/comfyui-mcp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.