| name | frame-extraction |
| description | Extract useful frames from local video files based on task intent, such as persona research, shot breakdown, product visibility, UI walkthroughs, visual-style review, or CTA/compliance checks. Use this when the goal is not generic video analysis, but selecting the right still frames and contact sheets for a specific downstream need. |
| metadata | {"postplus":{"familyId":"media-production","familyName":"Media and Creative Production"}} |
Frame Extraction
Follow shared public skill rules in:
postplus-shared public skill rules
Use this skill when the user needs frames, not just a text analysis of the video.
This is a general-purpose extraction skill. Do not narrow it to creator-face research only.
Follow shared routing rules in:
postplus-shared research preferences
Use skills/40-creative/video-analysis before or alongside this skill when shot-level understanding already exists or would materially improve frame selection.
Use For
- persona or vibe research
- creator appearance reference packs
- shot or structure review
- product visibility checks
- UI or screen-demo capture
- before/after frame pairs
- opening hook frame sets
- ending CTA or compliance frame sets
- cover-frame or first-frame candidate pulls
Trigger Signals
Use this skill when the user asks for things like:
- extract frames
- capture key frames
- make a contact sheet
- inspect character appearance / vibe
- inspect product exposure
- inspect UI presentation
- capture opening / ending / CTA frames
- pick usable reference frames from a video
Do not use this skill when the user only wants:
- hook analysis
- spoken-line breakdown
- adaptation ideas without frame output
Those are usually better routed to skills/40-creative/video-analysis.
Core Principle
Do not default to uniform frame sampling.
Choose an extraction mode that matches the task intent.
The same video may need different frames depending on whether the user is studying:
- people
- products
- UI
- pacing
- style
- claims or CTAs
Extraction Modes
Use the smallest mode that matches the request.
See:
references/extraction-modes.mdreferences/output-contract.md
Default modes:
uniform-samplescene-changeface-priorityobject-prioritytext-ui-priorityhook-firstcta-lastbefore-after-pairstyle-board
Input Types
This skill should work with:
- one local video file
- a local folder of videos
- a manifest that maps source ids to local files
- a shortlist already linked to source metadata
If the user gives TikTok URLs but local video files are missing, first recover or download the local videos before extracting frames.
Do not lock this skill to one platform.
Output Shapes
Pick outputs based on the use case.
Common outputs:
- selected frame folder
- contact sheet
- frame manifest
- markdown summary
- side-by-side frame comparison
Every extraction run should preserve enough metadata to trace each frame back to:
- source video
- timestamp
- extraction mode
- selection reason
Workflow
1. Clarify the intent
Classify the ask into one of these buckets:
- persona / vibe
- shot / structure
- product visibility
- UI / text readability
- before / after
- hook
- CTA / compliance
- broad visual scan
If the request is ambiguous, ask one short question:
- Are you extracting frames mainly to inspect character/persona vibe, or product / UI / shot structure?
2. Select mode
Map the intent to one primary mode.
Good defaults:
- persona / vibe ->
face-priority
- shot / structure ->
scene-change
- product visibility ->
object-priority
- UI / text ->
text-ui-priority
- hook review ->
hook-first
- CTA / compliance ->
cta-last
- broad scan ->
uniform-sample
Use a secondary mode only if the first one clearly misses the target.
3. Choose scope
Do not always scan the full video.
Typical scope choices:
- first 3-5 seconds
- full video
- final 3-5 seconds
- manually specified timestamp range
Long-video boundary:
- if a source video is longer than 5 minutes, do not run broad full-video
extraction by default
- derive duration before extraction, usually with
ffprobe
- run
scripts/plan_frame_extraction.mjs before extracting frames
- for videos above 5 minutes, the preflight plan caps the first pass to a
maximum frame budget of 60 selected frames
- tell the user: "This video is longer than 5 minutes, so I will extract a limited number of key frames based on the target range to avoid generating thousands of unusable images."
- if the extraction command cannot express the target range and frame budget,
stop before extraction instead of generating an unbounded frame dump
- only expand the scope after the first manifest proves the extraction mode is
useful
4. Package outputs
Match the packaging to the downstream task:
- persona research -> best frames + contact sheet + vibe notes
- shot review -> scene-change frames + timestamp list
- product review -> product-visible frames + proof notes
- UI study -> readable UI frames + OCR or text notes if needed
Public Skill Execution Contract
- keep extraction requests, frame manifests, contact-sheet build inputs, and
other intermediate state under
<work-folder>/.postplus/frame-extraction/
- keep only final user-facing frame exports, contact sheets, or review packs
outside
.postplus/
- use small, task-shaped extraction scopes before broad full-video pulls
- this skill requires
ffmpeg; follow the postplus-shared Local Dependency
Bootstrap Rule before extraction
- if local dependency bootstrap fails, stop immediately instead of switching to
ad hoc shell glue
Scripts
scripts/plan_frame_extraction.mjs
Default Sequence
For TikTok benchmark work:
- use platform research to shortlist videos first
- use local video files when available
- use
video-analysis shot outputs if they already exist
- only then extract frames
When shot-level outputs exist, prefer those as stronger evidence than blind timestamp sampling.
First-Version Boundary
The first version of this skill should stay pragmatic.
Prefer:
- ffmpeg-based extraction
- scene-change driven sampling
- simple timestamp filtering
- contact sheet generation
- lightweight manifests
Do not require:
- identity recognition
- demographic inference
- heavy CV pipelines
- perfect semantic understanding of every frame
Keep These Assets
For reusable benchmark work, keep:
- the local source video
- extracted frames
- contact sheets
- frame manifest
- any markdown summary used in later persona or creative work
Do not treat extracted frames as disposable if they support future persona, visual-style, or benchmark decisions.
