image-series-to-video
Skill8 starsChanged 5 months agoMITView on GitHub
图片系列转视频工作流:将带 manifest 的 PNG 图片系列转成带讲解、字幕和验收的 MP4 视频。Workflow skill for converting a manifest-backed image series into a narrated video; use when generated PNGs or an image manifest should become an MP4, long-image explanation video, chapter video, narrated walkthrough, or social video.
In this repository60 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 44
Create or update video.config.json from the manifest:
- Distinctive: line 53
Do not hand-write fixed scene arrays unrelated to the manifest.
- Distinctive: line 57
Use the repository video runner when available.
- Distinctive: line 61
run visual acceptance snapshots
- Distinctive: line 67
Only report completion if:
- Distinctive: line 74
If any step fails, report the failing phase and keep partial artifacts for diagnosis.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Image Series To Video Workflow
This workflow starts only after a real image series exists. It prevents the video stage from inventing fake screenshots, CSS cards, or placeholder images.
Required Inputs
The current artifact scope must contain:
assets/images/*.pngassets/images/manifest.md
The manifest must describe one row per image with title, file, usage, scan mode, and focus guidance.
Output Contract
Final deliverables must stay inside the current task artifact scope:
video.config.jsonindex.htmlassets/audio/*.mp3assets/audio/bgm.wavrenders/*.mp4ffprobe.jsonDELIVERY.md
Phase 1: Input Gate
Validate before building video:
find assets/images -maxdepth 1 -type f -name '*.png' | sort
test -f assets/images/manifest.mdReject the task if images are missing, empty, non-PNG, or not referenced by manifest.
Phase 2: Video Plan
Create or update video.config.json from the manifest:
- one scene per image or chapter
- title and caption per scene
- scan mode and safe focus from manifest
- voiceover text
- inspect timestamps
- output file name
Do not hand-write fixed scene arrays unrelated to the manifest.
Phase 3: Build
Use the repository video runner when available. Otherwise create the minimal equivalent pipeline:
- generate
index.html - generate voiceover audio
- run visual acceptance snapshots
- render MP4
- run ffprobe
Phase 4: Acceptance
Only report completion if:
renders/*.mp4exists and is non-empty.ffprobe.jsonshows video and audio streams.- duration is plausible for the number of scenes.
- sampled frames show real source images, visible captions, and no obvious overlap.
If any step fails, report the failing phase and keep partial artifacts for diagnosis.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

