菜单
Extract/analyze ComfyUI metadata embedded in output files - PNG tEXt, WebP EXIF, MP4/WebM container, .latent safetensors. Use when figuring out what prompt/settings produced a file, or comparing runs.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/laurigates/claude-plugins --skill comfy-metadata命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
Install, configure and troubleshoot MCP servers. Use when adding/enabling servers, editing .mcp.json, fixing OAuth, or when a server runs stale code after an upstream fix.
FinOps snapshot — org billing, workflow stats, cache usage. Use when you want a high-level view of CI spending or workflow health before diving deeper.
GitHub Actions billing, workflow efficiency, and waste analysis at org or repo level. Use when investigating CI/CD costs, wasted runs, or optimizing triggers.
正在显示 SKILL.md
| created | "2026-07-07T00:00:00.000Z" |
| modified | "2026-07-07T00:00:00.000Z" |
| reviewed | "2026-07-07T00:00:00.000Z" |
| name | comfy-metadata |
| description | Extract/analyze ComfyUI metadata embedded in output files - PNG tEXt, WebP EXIF, MP4/WebM container, .latent safetensors. Use when figuring out what prompt/settings produced a file, or comparing runs. |
| allowed-tools | Bash, Read, Grep, Glob |
Every image/video/audio file ComfyUI emits embeds the API-form prompt and the UI-form workflow that produced it. The encoding varies by file format and by which save-node wrote it (core vs. kijai vs. VHS). This skill is the canonical reference for where the data lives, how to read it back, and a small Python toolkit to do it reliably across every format on disk in this install.
| Use this skill when... | Use instead when... |
|---|
| Figuring out what prompt/settings produced an existing output file | Inspecting a live in-graph value during a run -> comfy-debug-preview |
| Scanning, organizing, or comparing a directory of outputs | Auto-arranging the layout of a workflow JSON -> comfy-workflow-layout |
| Format | Saved by | Where the JSON lives | Encoding |
|---|---|---|---|
| PNG still | core SaveImage, kijai PNG path | tEXt chunks (PIL Image.info["prompt"] and ["workflow"]) | each value is a JSON string |
| Animated PNG | core SaveAnimatedPNG | iTXt chunks (same keys) | each value is a JSON string |
| WebP still + animated | core SaveAnimatedWEBP | EXIF tags: 0x0110 (Model) holds "prompt:<json>", 0x010F (Make) holds "workflow:<json>", lower tags hold further extra_pnginfo keys | one EXIF string per key, "key:json" prefix |
| MP4 native | core SaveVideo | Container metadata, separate keys: prompt, workflow, plus any extra | each value is a JSON string |
MP4 kijai (WanVideoWrapper_*.mp4) | WanVideoWrapper.save_video | Container metadata, single key comment | one JSON object: {"prompt": "<json>", "workflow": "<json>"} (double-encoded) |
| WebM / Matroska | core SaveVideo; kijai/MMAudio | Container metadata: per-key (native) or single COMMENT (kijai) | same patterns as MP4 |
| FLAC / OGG / MP3 / WAV | core SaveAudio | Container metadata: prompt, extra_pnginfo* keys | each value is a JSON string |
.latent | core SaveLatent | safetensors header metadata: prompt, workflow | each value is a JSON string |
The library handles all of these uniformly. See REFERENCE.md for code
anchors, exact byte-level details, and edge cases (the _create_webp_metadata
EXIF tag walk, extra_pnginfo keys beyond workflow, the kijai
double-encoded comment format, fp8-scaled safetensors metadata, etc.).
scripts/comfy_meta.py is a single self-contained Python file. It works
as both a library and a CLI with four subcommands. It uses PIL (for
PNG/WebP), PyAV (for MP4/WebM/audio), and safetensors (for
.latent) — all already installed in .venv/.
Run via the project venv:
.venv/bin/python .claude/skills/comfy-metadata/scripts/comfy_meta.py <subcommand> ...
For ad-hoc batch work — renaming, indexing, clustering — calling the CLI
once per file is slow. Import comfy_meta directly instead. It has no
package wrapper, so add its dir to sys.path first:
import sys, pathlib
sys.path.insert(0, str(pathlib.Path(".claude/skills/comfy-metadata/scripts")))
import comfy_meta
for p in pathlib.Path("output").iterdir():
if not p.is_file():
continue
ex = comfy_meta.extract(p) # {"prompt": <api-dict>, "workflow": <ui-dict>}
prompt = ex.get("prompt")
if not isinstance(prompt, dict) or not prompt:
continue # no embedded metadata
summary = comfy_meta.summarize(prompt)
print(p.name, summary.sampler, summary.scheduler, summary.seed)
extract() returns parsed JSON for both halves; summarize() walks the
API prompt and yields a Summary dataclass. See
scripts/rename_outputs.py for a full example that builds new filenames
from summary.samplers[0] and the source file's mtime.
summarize() covers the API prompt, but extract()["workflow"] (the
UI form) carries data the summarizer doesn't surface — most usefully
save-node widgets. A workflow that wrote itself to a dedicated output
bucket (<bucket>/<date>/…) self-labels its outputs, so the prefix is a
free classification signal:
BUCKET = "<bucket>/" # whatever prefix your install sorts into
ex = comfy_meta.extract(p)
workflow = ex.get("workflow") or {}
for n in workflow.get("nodes", []) or []:
wv = n.get("widgets_values")
# SaveImage / SaveWEBM: list[0] is the filename_prefix
if isinstance(wv, list) and wv and isinstance(wv[0], str) and wv[0].startswith(BUCKET):
return BUCKET.rstrip("/")
# VHS_VideoCombine: dict["filename_prefix"]
if isinstance(wv, dict) and str(wv.get("filename_prefix", "")).startswith(BUCKET):
return BUCKET.rstrip("/")
rename_outputs.py's NSFW classifier combines this self-label signal
with API-prompt asset-name token matching (model / text-encoder / LoRA
names). The same approach works for any other categorisation the UI
workflow encodes that the API prompt strips out: node titles, custom
properties, group names, etc.
extract — dump the embedded JSON# Both prompt + workflow as one JSON object on stdout
.venv/bin/python .../comfy_meta.py extract output/WanVideoWrapper_I2V_00001.png
# Just one half (suitable for piping to jq)
.venv/bin/python .../comfy_meta.py extract -k prompt path/to.png | jq .
.venv/bin/python .../comfy_meta.py extract -k workflow path/to.mp4 | jq '.nodes | length'
# Re-import a downloaded JPEG/MP4 back into ComfyUI by saving its workflow:
.venv/bin/python .../comfy_meta.py extract -k workflow some.mp4 > user/default/workflows/2026-05/recovered.json
summary — one-line, analysis-friendly settingsThe summarizer walks the API-form prompt and pulls out the fields that actually matter for "what was different between run A and run B": model, text encoders, VAE, every sampler invocation (sampler/scheduler/steps/ cfg/denoise/seed), latent dims, num_frames, every LoRA + strength, and the positive/negative prompt text.
.venv/bin/python .../comfy_meta.py summary output/WanVideoWrapper_I2V_00001.mp4
Output is JSON; use -p for a human-readable two-column print instead.
scan — index a directory into JSONLWalk a tree (recursively by default) and emit one JSON record per output file. Use this to build a queryable index of every render on disk.
.venv/bin/python .../comfy_meta.py scan output/ -o /tmp/runs.jsonl
# Then analyze with jq:
jq -r '[.path, .summary.steps, .summary.cfg, .summary.sampler] | @tsv' /tmp/runs.jsonl
# Group by sampler+steps+cfg to see what combinations were used:
jq -s 'group_by(.summary.sampler+"|"+(.summary.steps|tostring)+"|"+(.summary.cfg|tostring))
| map({key: .[0].summary | "\(.sampler) steps=\(.steps) cfg=\(.cfg)", count: length})' \
/tmp/runs.jsonl
Files without embedded metadata (e.g. phone photos in the same tree)
get {"path": "...", "error": "no metadata"} so the index still
covers everything.
diff — what changed between two runs.venv/bin/python .../comfy_meta.py diff a.mp4 b.mp4
Prints a unified diff of the summarized settings. Useful when one of two near-identical workflows produced a better result and you want to see which knob actually moved.
model: UNETLoader.unet_name / CheckpointLoaderSimple.ckpt_name
/ WanVideoModelLoader.model / Image-Edit's diffusion path
text_encoders [list]: CLIPLoader / DualCLIPLoader / TripleCLIPLoader
/ LoadWanVideoT5TextEncoder / TextEncoderLoaderHiDream …
vae: VAELoader.vae_name / WanVideoVAELoader.model_name
samplers [list]: every KSampler / KSamplerAdvanced / WanVideoSampler /
WanVideoSamplerv2 / SamplerCustomAdvanced — each with
{sampler, scheduler, steps, cfg, denoise, seed, start_step,
end_step, add_noise} as found
latent_dims: width × height from EmptyLatentImage / EmptySD3LatentImage /
EmptyMochiLatentVideo / WanVideoEmptyEmbeds / etc.
num_frames: from WanVideoEmptyEmbeds.num_frames / Empty*Video.length
loras [list]: every LoraLoader / LoraLoaderModelOnly / Power Lora Loader
entry — {name, model_strength, clip_strength}
shift: ModelSamplingAuraFlow / ModelSamplingSD3 shift values
positive [list], negative [list]: CLIPTextEncode-style text inputs, with
the upstream node's title as a hint when present
Heuristic, not exhaustive — but covers ~95% of the workflows on this
install. New node-types missing from the summarizer are still preserved
in the raw prompt half of extract; add them to comfy_meta.py's
SUMMARIZERS registry when a class becomes worth pulling out.
A few cases that look like ComfyUI outputs but lack the JSON:
--disable-metadata — the save nodes
short-circuit before adding tEXt/EXIF/container tags.ffmpeg -i in.mp4 -c copy out.mp4 does
preserve container metadata; -c:v libx264 … (re-encode) typically
drops it unless -map_metadata 0 is passed.For the second case, when you mv or cp files between dirs the
metadata is fine — the OS-level operations preserve byte content. Only
re-encoding strips it.
The embedded prompt JSON contains the full positive and negative
text prompts, the exact seed, file paths to LoRAs/checkpoints/VAEs
(which can leak local directory structure like
models/loras/lgates/private_face_v1.safetensors), and sometimes
authoring metadata in extra_pnginfo. Before sharing a ComfyUI output
file publicly, decide whether you want to ship the metadata with it.
To strip metadata in-place (lossless):
oxipng --strip safe file.png (keeps colorspace, strips text)cwebp -metadata none or magick convert in.webp -strip out.webpffmpeg -i in.mp4 -c copy -map_metadata -1 out.mp4Or set --disable-metadata on the ComfyUI server (in comfyui.service)
if you want all future outputs to be metadata-free — but the toolkit
becomes useless then.
comfy-workflow-layout — once you've extracted a workflow with
extract -k workflow, run it through scripts/layout_workflow.py to
tidy node positions before importing.comfy-cli — comfy node install-deps <workflow.json> consumes a
workflow JSON file; pipe extract -k workflow straight into a temp
file to install the missing custom nodes for an imported workflow.