video-analysis
Analyze local or remote social videos through PostPlus, especially for TikTok/Reels shot beats, timelines, voiceover or on-screen text capture, creative strategy, and natural Markdown outputs. Use this when you need video-level analysis beyond metadata and want results linked back to source metadata.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/postplusai/postplus-skills --skill video-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads video-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Video Analysis
Use When
- The user provides a local video file or video URL and asks to watch, inspect, break down, deconstruct, analyze hooks, understand shots, capture spoken lines, or explain why a video works.
- Use this for video-level evidence beyond metadata. Do not answer actual video-understanding requests from transcript guesses or general marketing knowledge.
Do Not Use When
- The task belongs to ideation, QA, or another released skill listed in the handoff section.
- Required inputs are missing and guessing would change the result.
Execution Boundary
- Analysis runs through
postplus media analyze; discover its current flags withpostplus media schema --json. - Supported local formats are
.mp4,.m4v,.mov, and.webm. - Pass a local path, HTTPS URL, existing PostPlus media reference, or video data
URI directly to
--video. PostPlus prepares the media and runs the request; do not pre-upload it or author a manual request object. - If media preparation or analysis fails, stop on that error.
Source And Path
- A local file or HTTPS video URL can be analyzed directly.
- Preserve
sourceId,sourceUrl,videoFilePath,sourceMetadataPathor dataset path, model, prompt version, and source basis so results can be joined back to source metadata. - Keep downloaded videos when they are expensive to source. Keep analysis Markdown files and manifests under a stable workspace path.
Analysis Scope
- The default analysis is a single output per source video.
- It covers practical short-form structure such as hook, pacing, shot beats, VO/on-screen text, product timing, and creative strategy.
- It also asks for Visual & Brand Signals when visible: genre/mood, color palette, lighting, camera language, editing rhythm, brand feeling, and best-fit creative use cases.
Output And Handoff
media analyzereturns the hosted analysis response. Read the analysis text from that response and write one natural Markdown file per source video into a stable workspace path; there is no batch runner or summary file.- The analysis should cover useful video evidence such as shot beats, timeline, VO/on-screen text, reusable content structure, and creative strategy when those are relevant. If the result wraps the analysis in JSON, unwrap it to readable Markdown in-context.
- Results should stay grounded in observable video evidence. Database fields, catalog frontmatter, or search indexes belong to a separate ingestion step, not to the general video-analysis boundary.
Public Command Boundary
- Run
postplus media analyze <model-key> --video <local-path-or-url> --prompt <analysis-prompt> --output <result.json>. When the source video duration is known (for example from the local file), pass--video-seconds <n>so the hosted boundary can route eligible short videos efficiently; omit it when the duration is unknown.
postplus media analyze video-analysis \
--video ./reference.mp4 \
--prompt "Describe the result you need" \
--wait \
--output ./result.json
- Discover the model keys and request shape with
postplus media schema --json; do not use another execution interface. - If the CLI returns a quote-confirmation challenge, run
postplus quote confirm --json --challenge-file <challenge.json>and retry with the returned token. - Choose the smallest matching command from the user input and run it directly.
- Readiness diagnostics:
postplus doctor --skill video-analysis. If a command fails, report the exact error and stop. Do not bypass the failure by answering from metadata, rewriting media, readiness probing, or unowned fallbacks.
FAQ
What does the video-analysis skill do?
Analyze local or remote social videos through PostPlus, especially for TikTok/Reels shot beats, timelines, voiceover or on-screen text capture, creative strategy, and natural Markdown outputs. Use this when you need video-level analysis beyond metadata and want results linked back to source metadata.
How do I install video-analysis?
Run npx skills add https://github.com/postplusai/postplus-skills --skill video-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/video-analysis/ (personal) or .claude/skills/video-analysis/ (project).
Which AI agents can use video-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is video-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains video-analysis?
It is published by postplusai in postplusai/postplus-skills. It has 1,074 recorded installs.
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