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Video Content Engine

CommunityPopular
ericosiu
video-content-engine

Diagnose and transform any authorized video URL, upload, recording, transcript, podcast, interview, presentation, screen recording, webinar, ad, or published video into the strongest justified content portfolio. Use for video audits, format recommendations, long-form editing, mid-form explainers, Shorts and Reels, teasers, tutorials, case studies, paid cutdowns, captions, packaging, delivery, or publication preparation.

Overview

Publisherericosiu
Repositoryai-marketing-skills
Skill namevideo-content-engine
Stars
3.5K
Forks
685
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericosiu on GitHub. Read the source before you install it.

Installation

Install the Video Content Engine AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericosiu/ai-marketing-skills.git /tmp/ai-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-skills/video-content-engine .claude/skills/video-content-engine
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video Content Engine in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Video Content Engine on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Video Content Engine is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Video Content Engine

Turn one grounded source into the strongest justified portfolio. Do not manufacture a fixed number of derivatives. Find distinct promises, select formats that can pay them off, and give every asset a portfolio job.

Preamble

Run the repository's privacy-preserving version check and telemetry initializer when available:

bash
python3 telemetry/version_check.py 2>/dev/null || true
python3 telemetry/telemetry_init.py 2>/dev/null || true

Remote telemetry is opt-in. Never log content, URLs, paths, credentials, names, or business data.

State the operating contract

Before work, state the exact source, owner, channel, destination, requested modules, delivery format, this turn's artifact, stop condition, and known blocker class.

Preserve the source read-only. Never claim a render, upload, preview, publication, or QC pass without fresh evidence. Treat performance targets as experiments, not forecasts.

Accept any grounded source

Accept a public or authorized URL, uploaded file, local file, transcript, audio recording, or source folder. A bare request such as run this skill on this video defaults to diagnose and recommend, not automatic production.

For a link:

  1. Resolve the exact page, owner, channel, title, duration, and accessible media or transcript.
  2. Prefer the original file, then an authorized downloadable master, then the published stream.
  3. Never substitute a mirror, alternate upload, account, episode, or transcript.
  4. If authentication, permissions, DRM, missing media, or an unavailable transcript prevents grounded analysis, request an accessible source. Do not make editorial recommendations from metadata alone.

Unless production is requested, return a brief with the source score, recommended editorial and operating modes, opportunity counts, first release wave, repairs and dependencies, production complexity, portfolio jobs, and approval required to begin.

Inventory the source

  1. Record path or URL, size, duration, streams, and SHA-256 when available.
  2. Produce a speaker-aware timestamped transcript. Correct names, products, and numbers against the source.
  3. Build:
    • a claim ledger separating results, estimates, anecdotes, forecasts, and targets;
    • a rights ledger for third-party media, logos, consent, embargoes, and sponsors;
    • a content-atom inventory with timestamps, viewer, promise, proof, tension, framework, payoff, visuals, caveats, and dependencies.
  4. Read references/opportunity-routing.md, score viable atoms, and record selected and rejected opportunities.

The opportunity inventory is the source of truth. Recommend how many assets the source supports; do not default to a quota.

Before editing, read references/editorial-review.md for complete track coverage, proof-led openings, and whole-asset review. Inventory camera, screen-share, presentation, and playback-audio tracks separately; a camera composite may omit the evidence the speaker discusses.

Choose the transformation

Read references/quality-gates.md. Score hook, clarity, proof, pacing, and payoff from 0–20 each.

Choose the least destructive editorial mode:

  1. Polish — remove errors, dead air, and technical distractions.
  2. Tighten — remove repetition and tangents while preserving structure.
  3. Re-architect — rebuild the cold open, move proof, reorder sections, and bridge gaps.
  4. Rebuild — add pickups, narration, demonstrations, or graphics to create a materially new product.

For Tighten, Re-architect, or Rebuild, create an exact-source word- or sentence-level timestamped transcript before final cuts. Memo timestamps are section guides, never literal cut points.

Read references/format-modes.md. Select one primary mode and justified secondary modules. When no mode is named, default to Portfolio Audit and recommend the smallest useful release wave.

Lock the portfolio spine

Write:

[Viewer] should believe [verdict] because [proof], then use [framework] to reach [outcome].

Give each selected asset one promise, viewer, platform, job, payoff, parent, destination, and packaging intent. Reject repeated promises, incomplete excerpts, unsupported claims, and assets that cannot stand alone.

Produce selected modules

Long-form

Create a cut/reorder map and EDL before rendering. Resolve clips against the exact transcript and begin and end on complete thoughts. Run:

bash
python3 scripts/audit_edit_boundaries.py \
  --transcript <exact-source-transcript.json> \
  --clips <retained-clips.json> \
  --output <boundary-audit.json>

After rendering, transcribe the master and review every join. Record the left tail, right head, transition, verdict, and repair. Technical decoding does not replace semantic review. Preserve previous renders as versioned files.

Mid-form

Create self-contained 3–12 minute videos around one framework, case study, question, argument, or demonstration. Give each an independent hook, context, proof, payoff, package, and destination.

Short-form and micro

Create 15–90 second vertical assets only from complete atoms. Start on the hook, preserve claim context, render at 1080×1920, include burned captions plus SRT, and end on a payoff or useful question.

Create 6–20 second teasers, hook tests, story units, paid cutdowns, or quote motion only when they route truthfully. Label promotional clips separately.

Captions and opening treatment

Default masters to readable burned-in captions plus matching SRT unless the user opts out. Validate monotonic non-overlapping cues, at most two lines, mobile-safe margins, and cue end at or before the master.

Use the approved brand brief for opening-overlay duration; without one, use four seconds as a starting point, or a clean opening when the source calls for it. An overlay cannot repair a weak spoken hook. Reinforce the spoken promise without strengthening the claim. Check collisions with captions, lower thirds, chapter cards, and qualifiers. Claim qualifiers take priority.

Carousels and written derivatives

Produce a swipe narrative, newsletter, article, post, thread, show notes, checklist, or summary when the format improves comprehension or distribution. Require supplied brand assets or use a neutral system; never reconstruct a person, mascot, logo, or identity from memory.

Package every asset

  • Long-form: title, thumbnail brief, cold open, first-30-second promise, description, chapters, pinned comment, and end screen.
  • Mid-form: browse/search title, cover, opening line, series label, description, and parent route.
  • Short-form/micro: first-frame visual, spoken hook, headline, cover, post copy, comment prompt, and CTA.
  • Carousel: cover promise, swipe progression, caption, CTA, final slide, sources, and alt text.

Create promise hypotheses, not cosmetic variants. Reject packaging whose promise is not paid off.

Plan distribution and control side effects

Inventory justified B-roll, screenshots, citations, diagrams, lower thirds, qualifiers, chapter cards, overlays, captions, punch-ins, music, sound design, pickups, narration, sponsor placement, and end-screen bridges. Define which visual layer yields when elements collide.

When requested, produce a release map with order, spacing, platform, routes, timely versus evergreen status, cannibalization warnings, and recut windows.

Never publish, schedule, upload, change sharing, spend quota, or activate a campaign without explicit approval and authoritative readback.

Review and hand off an editable project

Read references/editing-workflow.md before planning previews, revisions, or team handoff. Provide full-duration access to every selected asset, immutable versions, timestamped feedback, and editable source mappings. Hook tests and compact previews supplement full review. Respect the user’s storage and foreground-playback preferences.

This package contains an operating skill and two validators. It does not contain a hosted editor, media storage, render service, or multiplayer application. Use an available authorized editor or renderer; describe missing infrastructure plainly. The shared-workspace design in the reference is an implementation contract, not a deployed capability.

Converge, deliver, and learn

Apply references/quality-gates.md. Confirm source integrity, portfolio alignment, media decode and timing, edit boundaries, rendered joins, captions, overlays, claims, rights, packaging, and delivery files.

technical integrity does not equal a coherent edit

Read references/delivery-contract.md, produce its modular folder and manifest, then run:

bash
python3 scripts/validate_delivery.py --root <delivery-folder>

The delivery validator checks referenced paths, hashes, and selected manifest fields. A PASS does not establish playable media, caption quality, editorial approval, or publication readiness. Keep pending and failed human/agent review gates visible.

Return scores, mode, opportunity counts, portfolio map, runtimes, packages, sources, outputs, QC, verified destination, publication status, and a 24-hour, 72-hour, and seven-day measurement plan. Promote a lesson only after repeated comparable results.

Maintain the engine

Run on request; this skill installs no background job. Track each asset through prepared, review candidate, changes requested, approved, rendering, and delivered; track publication separately with destination evidence. Record corrections as reusable editing rules without private project details. Keep performance observations separate from brand preferences. Promote a workflow only after real artifacts pass its applicable gates, and retire a rule when a documented replacement supersedes it.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Video Content Engine AI skill do?

Diagnose and transform any authorized video URL, upload, recording, transcript, podcast, interview, presentation, screen recording, webinar, ad, or published video into the strongest justified content portfolio. Use for video audits, format recommendations, long-form editing, mid-form explainers, Shorts and Reels, teasers, tutorials, case studies, paid cutdowns, captions, packaging, delivery, or publication preparation.

Why use Video Content Engine on TypingMind?

Because you install it once and use it with any model. Video Content Engine is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Video Content Engine in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericosiu/ai-marketing-skills/tree/main/video-content-engine. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Video Content Engine?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Video Content Engine?

As many as you like. As long as a model supports skills, you can use Video Content Engine with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Video Content Engine AI skill free?

Yes. It is published on GitHub by ericosiu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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