Video Understand
calesthio/OpenMontage
Understand video content locally using ffmpeg frame extraction and Whisper transcription.
Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a…
Install Render Whiteboard Explainer
$ npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills render-whiteboard-explainer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ads/capabilities/render-whiteboard-explainer .claude/skills/render-whiteboard-explainer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "render-whiteboard-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainer into .claude/skills/render-whiteboard-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-whiteboard-explainer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills render-whiteboard-explainer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ads/capabilities/render-whiteboard-explainer .agents/skills/render-whiteboard-explainer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "render-whiteboard-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainer into .agents/skills/render-whiteboard-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-whiteboard-explainer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills render-whiteboard-explainer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ads/capabilities/render-whiteboard-explainer .cursor/skills/render-whiteboard-explainer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "render-whiteboard-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainer into .cursor/skills/render-whiteboard-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-whiteboard-explainer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/ads/capabilities/render-whiteboard-explainer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills render-whiteboard-explainer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ads/capabilities/render-whiteboard-explainer .gemini/skills/render-whiteboard-explainer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "render-whiteboard-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainer into .gemini/skills/render-whiteboard-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-whiteboard-explainer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills render-whiteboard-explainerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ads/capabilities/render-whiteboard-explainer .github/skills/render-whiteboard-explainer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "render-whiteboard-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainer into .github/skills/render-whiteboard-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-whiteboard-explainer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills render-whiteboard-explainer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ads/capabilities/render-whiteboard-explainer .opencode/skills/render-whiteboard-explainer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "render-whiteboard-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-whiteboard-explainer into .opencode/skills/render-whiteboard-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-whiteboard-explainer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
render-whiteboard-explainer- GitHub stars
- 1.2k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 1,100 words
- Files
- 15 (incl. scripts)
- Skills in repo
- 273
- Repo updated
- First seen
- Licence
- MIT
At a glance
Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a…
- Works in 5 steps: The plate. One photograph of a blank… → The drawings. One black line drawing per… → The layout. Solved from the beats, not… → …
- The whiteboard format
- SKILL.md covers The three decisions that make…, Choices, What it renders and Non-negotiables, plus 4 more sections
- Runs Python scripts from its folder
What it does
Render Whiteboard Explainer is an agent skill from gooseworks-ai/goose-skills. Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim. The board is a PHOTOGRAPH and the ink is multiplied onto it, so the surface sheen and window light come through the strokes; the drawings are generated as line art and traced to ordered strokes, then revealed in drawing order on…
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts (for example `scripts/anchors.py`, `scripts/beats.example.json` and `scripts/gen-art.py`).
It sits in Media & Creative, covering Video production. It works with FFmpeg. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
When your agent uses it
- The whiteboard format
- Tasks that involve Video production
Example prompts
- “/render-whiteboard-explainer”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- The plate. One photograph of a blank board in a room, generated once per brand, then
- The drawings. One black line drawing per subject the script names, generated once per
- The layout. Solved from the beats, not authored. Rows go down a left column, drawings
- The video. Marks appear stroke by stroke in drawing order, each starting on its own word.
- The master. Gentle compression before a measured two-pass loudness normalisation, then a
What it can do on your machine
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 13 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Render Whiteboard Explainer loads about 1.9k tokens when it runs. Until then it costs about 194 tokens; SKILL.md has 1,100 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,100 words, ~1,875 tokens.
Download SKILL.mdSave it as
.claude/skills/render-whiteboard-explainer/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.- name
- render-whiteboard-explainer
- description
- Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim. The board is a PHOTOGRAPH and the ink is multiplied onto it, so the surface sheen and window light come through the strokes; the drawings are generated as line art and traced to ordered strokes, then revealed in drawing order on the word they belong to. The layout is SOLVED from the beats, never hand-placed. FREE assembly (Pillow plus ffmpeg) apart from one board photo and one drawing per subject, both one-time per brand. Use for the whiteboard format.
- status
- draft
render-whiteboard-explainer
The renderer for the whiteboard video ad format: a voice explains one idea while a board fills up in marker, the way a graphic recorder works a room. Every mark lands on the word it belongs to, and the board is wiped and reused between sections.
Siblings in the chat-UI family are render-imessage-chat, render-chatgpt-chat and
render-apple-notes-chat, where a screen is the creative. Reach for this one when the creative
is someone explaining a thing by drawing it.
The three decisions that make this format work
The board is a photograph. A drawn board reads as a drawn board, and a plain white frame reads as a sketch film. Both were built and both were rejected. One generated still of a real blank whiteboard in a real room is the plate, and the ink is multiplied onto it so the surface sheen and the window light come through the strokes rather than sitting on top as flat black. The handheld drift is generated over both layers at once, so nothing can slide against anything.
The drawings are generated as line art, then traced. Do not hand-code them, and do not trace painterly images. Art traced from a photograph carries every contour the photograph had and reads as clip-art; art generated as a marker drawing traces to a handful of confident paths. An open hand came back as a single continuous stroke. Chain each generation off the first so the line weight matches across the set.
There is no colour. Black marker throughout. Do not add an accent.
Choices
The recipe asks these before any paid step; this renderer only draws what the beats say.
- the script — what the narrator says. Everything downstream is anchored to its word timings, so it is settled first and never changed afterwards.
- the beats — for each moment, what is SAID and what is DRAWN. A row of text, a drawing, or both. One number can be marked as the hero, which letters it large instead of as another row.
- the title and subtitle — lettered on the first board.
- the payoff — the closing line, lettered on the last board, with a ring closing around the claim it refers to.
- the boards — how many times the board is wiped and reused. Three suits a half-minute.
Brand facts come from the brand kit.
What it renders
- The plate. One photograph of a blank board in a room, generated once per brand, then cropped so the board fills the frame with its side edges running out of shot. The writable surface is measured off the image and pulled inside the caption safe zone before anything is mapped through it.
- The drawings. One black line drawing per subject the script names, generated once per brand and traced into ordered stroke paths.
- The layout. Solved from the beats, not authored. Rows go down a left column, drawings down a right one, with an enforced gutter between them, and the content runs the full height of the board. Boards are split at a pause near a balanced boundary, which is where a person would wipe.
- The video. Marks appear stroke by stroke in drawing order, each starting on its own word. Text is written letter by letter. The board wipes between sections. Captions sit inside the safe zone and never run ahead of the voice.
- The master. Gentle compression before a measured two-pass loudness normalisation, then a limiter. Targets minus fourteen LUFS with true peak under minus one and a half, and keeps the payoff hold intact.
Show full SKILL.md (510 more words)Show less
Non-negotiables
- Every mark lands on its own word. The voiceover is the clock. An anchor that resolves to the wrong word does not look wrong, it silently reorders the video: one that matched an early word instead of a late one once dragged a whole section to the front, and the ad played its ending first with nothing reporting a problem. Anchors are resolved once, shared by the solver and the renderer, and an ambiguous one is refused rather than guessed at.
- Nothing is hand-placed. Anything that refers to something else — an arrow, a note, a ring — takes its target as an argument and works out its own position. Anything sharing space with a drawing is bounded by that drawing's box. Two things placed at coordinates chosen independently will eventually meet.
- Copy a reference's grammar, never its content. The reference board bullets its rows with a drawn eye and fans emphasis marks beside its phrases, but those belong to that board's brand. Reproduced on another script they are decoration that means nothing. Filler has to be about something: a drawing of what the line refers to, never abstract specks.
- Two reviews, both human. The storyboard still costs seconds and is where relevance is
judged — whether a mark is about the line it sits under, whether the payoff says the closing
line. No automated check decides that. Then the finished cut, watched end to end with the
watchskill.
Inputs
- the spoken audio and its word-level timings, as described below
- a beats file — see the example beside the scripts
- a brand kit for the facts
The voice comes with its word timings. create-vo-elevenlabs returns audio only, and this
format needs a timing for every word. scripts/gen-voice.py --project <dir> reads vo and voice
from beats.json and calls the proxy's /with-timestamps route, writing voice/vo.mp3 and
voice/words.json, which make-episode.py requires. A dry run prints the character count and the
cost; --yes spends. It routes through the GooseWorks proxy (scripts/media_proxy.py), never the
speech provider directly, and refuses a payload it has already paid for.
Checks it runs itself
The build refuses to continue when any of these fail, and names what broke:
- every anchor resolves to exactly one intended word
- sections start in script order
- every mark finishes being drawn before its board is wiped
- rows stay inside the column and cannot reach a drawing
- loudness, true peak and the payoff hold
Cost
The board photo and the drawings are one-time per brand. A second video for the same brand costs only the voiceover. Everything else — the layout, the lettering, the tracing, the render and the master — is free and local. Rendering a half-minute takes under three minutes.
Honest ceiling
Measured against the filmed reference this reaches about a fifth of its ink cover, because the reference is a time-lapse of a much longer drawing session while every mark here waits for its word. Filler narrows that and does not close it. If a brief needs a genuinely full board, the script has to be longer or the marks have to stop waiting for words.
© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 14 other files (scripts) in skills/ads/capabilities/render-whiteboard-explainer of gooseworks-ai/goose-skills.
- SKILL.md
- scripts/anchors.py
- scripts/beats.example.json
- scripts/gen-art.py
- scripts/gen-voice.py
- scripts/illos.py
- scripts/lettering.py
- scripts/make-episode.py
- scripts/master.py
- scripts/media_proxy.py
- scripts/prep-plate.py
- scripts/render.py
- scripts/solve-layout.py
- scripts/trace.py
- skill.meta.json
Open the folder on GitHubat commit c650c6d
Compare with similar skills
Render Whiteboard Explainer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Render Whiteboard Explainer this skillgooseworks-ai/goose-skills | 1.2k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Video Understandcalesthio/OpenMontage | 66k | — | ~841 | Automated safety check: Pass | AGPL-3.0 | |
| HyperFrames Video Entry Pointheygen-com/hyperframes | 59k | 3 repos | ~5.2k | Automated safety check: Pass | Apache-2.0 | |
| Video Shotseternityspring/reelbench-skills | 872 | 1 repos | ~1.8k | Automated safety check: Notes | Apache-2.0 | |
| Mobile Demo Film Editorsuperset-sh/superset | 15k | — | ~1.8k | Automated safety check: Pass | Custom licence | |
| Video Editcalesthio/OpenMontage | 66k | — | ~855 | Automated safety check: Notes | AGPL-3.0 |
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Related
Works with
Categories
Questions about Render Whiteboard Explainer
What does Render Whiteboard Explainer do?
Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a…. Render Whiteboard Explainer is an agent skill from gooseworks-ai/goose-skills. Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim.
When should I use Render Whiteboard Explainer?
Render Whiteboard Explainer fits situations like: the whiteboard format; tasks that involve Video production.
How do I install Render Whiteboard Explainer in Claude Code?
Run `npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a claude-code`. Or copy the skill folder (skills/ads/capabilities/render-whiteboard-explainer in gooseworks-ai/goose-skills) into .claude/skills/render-whiteboard-explainer in your project. Claude Code loads it when a task matches its description.
How do I install Render Whiteboard Explainer in Codex?
Run `npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a codex`. Or copy the skill folder (skills/ads/capabilities/render-whiteboard-explainer in gooseworks-ai/goose-skills) into .agents/skills/render-whiteboard-explainer in your project. Codex loads it when a task matches its description.
Can I use Render Whiteboard Explainer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill render-whiteboard-explainer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/render-whiteboard-explainer, .gemini/skills/render-whiteboard-explainer, .github/skills/render-whiteboard-explainer and .opencode/skills/render-whiteboard-explainer in your project.
What does Render Whiteboard Explainer need to run?
Going by SKILL.md and its folder, Render Whiteboard Explainer needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Render Whiteboard Explainer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Render Whiteboard Explainer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Render Whiteboard Explainer use?
Render Whiteboard Explainer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Render Whiteboard Explainer use?
About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Render Whiteboard Explainer?
Skills that share tags, products or a category with Render Whiteboard Explainer: Video Understand (calesthio/OpenMontage, 66k stars), HyperFrames Video Entry Point (heygen-com/hyperframes, 59k stars), Video Shots (eternityspring/reelbench-skills, 872 stars) and Mobile Demo Film Editor (superset-sh/superset, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Render Whiteboard Explainer?
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,239 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.