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Changelog Video

OrganizationPopular
heygen-com
changelog-video

Turn a weekly changelog .md into a finished branded changelog video (square 1080, ~45-60s, Annie VO, animated brand background, mock-UI visualizations, lowkey captions). Use when the user provides a changelog/digest markdown and wants the weekly video, or says "changelog video". Self-contained — fonts, background, lexicon, and scripts ship in this skill.

Overview

Publisherheygen-com
Repositoryhyperframes
Skill namechangelog-video
Stars
73.3K
Forks
6.6K
Bundled files
14
LicenseApache-2.0
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.

  • 14 bundled files

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

  • Open source

    Published by heygen-com on GitHub. Read the source before you install it.

Installation

Install the Changelog Video 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.
    https://github.com/heygen-com/hyperframes/tree/main/.agents/skills/changelog-video
  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/heygen-com/hyperframes.git /tmp/hyperframes
mkdir -p .claude/skills
cp -r /tmp/hyperframes/.agents/skills/changelog-video .claude/skills/changelog-video
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Changelog Video 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 Changelog Video 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 Changelog Video 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.

Changelog → Branded Video

Input: a changelog .md (themes + items, like the weekly HyperFrames digest). Output: a lint-clean, seam-gate-green HyperFrames project in projects/active/weekly-changelog-<range>/. Render only when asked.

Load first, non-negotiable: motion-doctrine (+ cut-the-curve, oversized-cursor if a cursor appears, seam-craft) and captions-overlay. This skill supplies the changelog-specific pipeline; the doctrine supplies the motion law.

The prime directive: visualize, don't list

Every theme is illustrated by an animated mock of the actual UI or a faithful analog acting out the change in experience — never text bullets. Route every theme/item through references/visualization-registry.md BEFORE writing the script; the registry decides ui-recreate / ui-analog / terminal / checklist. Text checklist is the LAST resort, reserved for genuinely non-visual items (reliability fix lists).

Pipeline

0 · Bootstrap the project from THIS skill's assets — non-negotiable

Do this before writing any composition HTML. Skipping it always produces a video that looks like a similar project you built before, NOT this skill's brand — that's the single most common way this skill goes off-brand. The skill's assets, fonts, and scaffold are the skill; the SKILL.md prompt is a router.

bash
mkdir -p project/assets/fonts
cp <SKILL_DIR>/assets/fonts/*.woff2 project/assets/fonts/
cp <SKILL_DIR>/assets/bgm.mp3 project/bgm.mp3
ffmpeg -y -stream_loop 15 -i <SKILL_DIR>/assets/bg-pattern.mp4 -t <TOTAL> \
  -vf "scale=1080:1080,fps=30,eq=saturation=0.72,drawbox=c=black@0.5:t=fill" \
  -an -c:v libx264 -crf 20 -pix_fmt yuv420p project/assets/bg-pattern-<TOTAL>s.mp4
cp <SKILL_DIR>/examples/master-skeleton.html project/index.html

Then read references/build-spec.md end-to-end (not skimmed) — it defines the brand tokens (TT Norms Pro + ABC Solar Display + TT Norms Mono, cream #f5f6f4, rationed green #5ef17c, glass cards with green-tinted borders, kicker/sec-chip pill shape, 32px caption rail at top: 990) that every scene inherits from the scaffold.

Only THEN begin steps 1-6 below. Steps 1-4 (parse, route, script, VO) plan what goes into the scaffold; step 5 fills placeholders (<RANGE>, <TOTAL>, <CUT_N>, <DUR_N>, scene bodies) inside the already-copied project/index.html — you do NOT rewrite the scaffold's chrome, fonts, palette, or layout shell.

If you catch yourself reaching for cp on a prior video's index.html, or writing your own @font-face declarations, or designing a WebGL shader background instead of using the encoded bg-pattern MP4 above: STOP. Delete the current index.html and restart at the cp of the master-skeleton scaffold. Rebuilding scene content on the right scaffold is cheaper than retrofitting brand into the wrong scaffold.

1 · Parse + editorial cut

  • Extract: week range, headline stats (releases, commits), themes, items.
  • Budget: 45-60s total. Title ≤2s, outro ≤3.5s, 4 themes ≈ 9-12s each.
  • Per theme keep ONE hero visualization + at most 3 spoken items. Everything else exists only as the outro's "full digest" pointer. Cutting is the job: a changelog with 30 items still yields ≤14 spoken beats.
  • Order themes by story: marquee feature → product surface → performance → reliability (the digest usually already reads this way).

2 · Visualization routing

For each theme, pick the surface from references/visualization-registry.md and write one line: theme → surface → the 2-4 sequenced actions the mock performs, each tied to a script phrase. If no registry surface fits and no faithful analog exists, it's a checklist scene — don't invent fake UI for something we can't represent honestly.

3 · Two-layer script (spoken vs display)

Write the script as token lines per references/script-voice.md: conversational register, every technical term carrying a spoken phonetic form from references/lexicon.json while display keeps standard spelling. Captions show display; the VO reads spoken. Any term not in the lexicon: STOP and ask the user how it's pronounced, then add it to the lexicon. Save as script-tokens.json in the project.

4 · VO — Annie (HeyGen, pinned)

bash
# spoken-layer text only; words JSON = ground-truth timestamps of the SPOKEN text
# Repo-native path: the changelog-video skill runs from the hyperframes repo root,
# so it uses the tracked hyperframes-media TTS helper directly (no `npx hyperframes
# skills` install step). If you've copied the skill into another repo, swap in
# your own path to the media-use / hyperframes-media heygen-tts.mjs.
node skills/hyperframes-media/scripts/heygen-tts.mjs ./vo-spoken.txt \
  -o voiceover.mp3 --words vo-words.json \
  --voice 330290724a1b470fb63153f34d4c0183   # Annie — lifelike (do not substitute)

Requires heygen CLI ≥0.3.0 authenticated (heygen auth login --oauth). Then align spoken timestamps back to display tokens:

bash
node <SKILL_DIR>/scripts/align-captions.mjs \
  --tokens script-tokens.json --words vo-words.json --out captions.json

captions.json is the caption-rail input (display spelling, spoken timing). The aligner prints MISMATCH warnings — resolve every one before building (usually a lexicon spelling the TTS renders as multiple words). The audio is the clock: all beat times come from vo-words.json; a VO regen re-opens every seam.

Word-timings are a hard gate. Before moving on to step 5, verify vo-words.json is non-empty and has a words: [...] array with start/end per word. If it's empty (0 bytes) or missing the array — a known failure mode when the TTS provider returns audio but no timestamp payload — DO NOT proceed without them. Fallback: forced-align the produced audio against the display script using local whisper:

bash
uvx --from openai-whisper whisper voiceover.mp3 \
  --model base.en --language en --word_timestamps True \
  --output_format json --output_dir .
# then run align-captions.mjs with --words voiceover.json (same shape)

Whisper mishears TTS renderings ("gee-sap" → "gsap", "heyjen" → "hey Jen", etc.) — captions still use the DISPLAY spelling from script-tokens.json; whisper only supplies the timestamps. align-captions.mjs handles the join. This fallback is the difference between a captioned build and a silently uncaptioned one.

5 · Build

Follow references/build-spec.md exactly: brand tokens + fonts (bundled in <SKILL_DIR>/assets/), the animated background encode, scene scaffold, chrome, caption rail, one rationed green moment per scene. Then the doctrine order: ledger.json (all ordinary seams cut-the-curve LEFT) → seam-stamp → internal beats on VO words → seam-gate verify.

Captions are non-optional. The master-skeleton ships a caption-rail IIFE that reads a LINES array — leaving that array empty is a shipped bug, not a style choice. Populate it from captions.json before proceeding to step 6:

javascript
// paste in place of "const LINES = /* … */ []" in the caption-rail IIFE:
const LINES = /* contents of captions.json */ [
  { id: 0, end: 2.74, w: [["This", 0.0], ["week,", 0.30], …] },
  …
];

If align-captions.mjs was skipped or LINES is [], the frame check in step 6 will fail — do not paper over it by removing #cap-line from the scaffold.

6 · Gates (all green before presenting)

  1. node packages/cli/bin/hyperframes.mjs check --caption-zone "x0=0;y0=.90;x1=1;y1=1;severity=error;seek=.02,.06,.10,.14,.18,.22,.26,.30,.34,.38,.42,.46,.50,.54,.58,.62,.66,.70,.74,.78,.82,.86,.90,.94,.98" (or the installed hyperframes CLI from the repo-local skills/hyperframes-cli/ skill) — 0 errors (contrast: dim text ≥ .66 alpha; scene content stays above the caption rail). Do NOT reach for npx hyperframes@latest; the tracked repo-local CLI is the source of truth for the composition contract this skill produces against.
  2. seam-gate.mjs verify — 0 fail.
  3. Restart the preview server (it caches the bundle), spot-check 3-4 beats via __player.seek on the raw comp page.
  4. Do NOT render unless the user asks. After a requested render, verify frames from the MP4 (ffmpeg -ss <t> … -frames:v 1): captions present, background video not black, no tiny/frozen frames.
  5. Caption presence gate — hard fail. Sample 3-4 frames spread across the VO's spoken window (e.g. t=3, t=15, t=30, t=42 for a 48s VO) and confirm the caption rail at top: 990 renders visible text on each. If any frame in a spoken interval is missing captions, the build ships uncaptioned — treat it as a red gate and re-check step 5's LINES population. This is exactly what went wrong on the Jul 13-20 v4 build.

Project layout

projects/active/weekly-changelog-<range>/
├── index.html            # single-doc master (scenes as slides, stamped seams)
├── ledger.json           # vector ledger (seam-stamp input)
├── script-tokens.json    # two-layer script (source of truth for VO + captions)
├── vo-spoken.txt         # generated: spoken layer, one line
├── voiceover.mp3 + vo-words.json + captions.json
├── bgm.mp3               # copy from <SKILL_DIR>/assets/bgm.mp3 (the house track) unless the user supplies one
└── assets/fonts/ + assets/bg-pattern-<dur>s.mp4

Anti-patterns

Don'tInstead
Bullet-point slides for UI changesMock the surface acting out the change
Fake UI for un-representable itemsHonest checklist scene
Plain "JSON"/"CLI" in the TTS textLexicon spoken forms; display stays standard
Phonetic spellings in captionsCaptions always render the display layer
Guessing an unknown term's pronunciationAsk, then grow the lexicon
Speaking every changelog item≤3 per theme; the digest link carries the rest
Green accents everywhereOne green moment per scene (#5ef17c)
Starting from a prior video's index.htmlStep 0 — copy examples/master-skeleton.html from this skill into project/index.html, always
Hand-crafted @font-face / WebGL shader / custom BGMStep 0 — copy this skill's assets/ verbatim; the skill's assets ARE the brand
Delivered without CloudFront invalidationRun aws cloudfront create-invalidation on distribution E2BSLVSZ7FG3U0 for the exact path after any S3 replace — CDN caches the old file otherwise
Shipping with the LINES array empty in the scaffoldStep 4 must produce a populated captions.json; step 5 must paste it into the IIFE; step 6 gate 5 must confirm captions on rendered frames. An empty LINES = uncaptioned ship = re-do the run
No vo-words.json → skip captions and ship anywayFall back to whisper forced alignment on the produced audio; captions are non-optional

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 Changelog Video AI skill do?

Turn a weekly changelog .md into a finished branded changelog video (square 1080, ~45-60s, Annie VO, animated brand background, mock-UI visualizations, lowkey captions). Use when the user provides a changelog/digest markdown and wants the weekly video, or says "changelog video". Self-contained — fonts, background, lexicon, and scripts ship in this skill.

Why use Changelog Video on TypingMind?

Because you install it once and use it with any model. Changelog Video 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 Changelog Video in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/heygen-com/hyperframes/tree/main/.agents/skills/changelog-video. 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 Changelog Video?

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 Changelog Video?

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

Is the Changelog Video AI skill free?

Yes. It is published on GitHub by heygen-com under the Apache-2.0 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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