Agent skill

Footage Cutlist

by gooseworks-ai in gooseworks-ai/goose-skills

Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local.

MITAuto-check passedMedia & Creative

Install Footage Cutlist

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill footage-cutlist -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills footage-cutlist --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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/footage-cutlist .claude/skills/footage-cutlist && rm -rf skills-src

Use ~/.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/

Facts

Skill name
footage-cutlist
GitHub stars
1.2k
Token cost
~2k tokens
SKILL.md length
1,003 words
Files
10 (incl. scripts)
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local.

  • Works in 9 steps: Watch the whole thing before choosing… → Never commit a window from a 1fps… → The line must be true of the footage… → …
  • Any format that shows real product footage beside
  • SKILL.md covers Run, The cut list, Rules that were learned the… and Going back and forth with the…, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Footage Cutlist is an agent skill from gooseworks-ai/goose-skills. Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local. survey.py turns footage into timestamped contact sheets + scene cuts + motion so the agent can actually look at it; the agent writes a cut list (beat → source window + framing); preview.py draws it as a review sheet the user corrects round by round; cut.py renders the approved list into a silent 1080x1920 layer with the creator's area…

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts (for example `scripts/_common.py`, `scripts/cut.py` and `scripts/cutlist.py`).

It sits in Media & Creative. 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

  • Any format that shows real product footage beside
  • Instead of a creator (split-screen

Example prompts

  • “/footage-cutlist”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Watch the whole thing before choosing anything. Look at every sheet of the whole-clip
  2. Never commit a window from a 1fps glance. Re-survey that range at --every 0.25 and
  3. The line must be true of the footage while it is said. If a line says "it pulls your
  4. Never crop footage that has copy in it. Cropping a UI panel into a narrower zone
  5. Avoid dead screens. A window inside a still run reads as the video having stopped.
  6. A screen region should be roughly the box's shape. A 9:16 box is filled by a source
  7. Watch for a third party inside the footage. A customer logo row or another company's
  8. Don't reuse footage. cut.py warns when two beats overlap in the same source: it
  9. After the takes exist, re-check. Line timing moves when the real voice is aligned

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 7 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    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

Footage Cutlist loads about 2k tokens when it runs. Until then it costs about 207 tokens; SKILL.md has 1,003 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~207
When it runs · the whole SKILL.md, loaded when a task matches
~2k

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). 1,003 words, ~1,951 tokens.

Download SKILL.mdSave it as .claude/skills/footage-cutlist/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
footage-cutlist
description
Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local. survey.py turns footage into timestamped contact sheets + scene cuts + motion so the agent can actually look at it; the agent writes a cut list (beat → source window + framing); preview.py draws it as a review sheet the user corrects round by round; cut.py renders the approved list into a silent 1080x1920 layer with the creator's area left as a plate. Also frames footage as a filmed screen (keystone, bezel, handheld drift) with dissolves between beats, and takes still screenshots as sources. Use for any format that shows real product footage beside, between or instead of a creator (split-screen, screen inserts, walkthroughs).
status
active

footage-cutlist

Picking which moment of a customer's footage plays under which line is a judgement, not a formula. A position map ("beat 3 is 40% through the reel, so take the footage 40% through") was measured up to 5.7s off a person's picks on a reference build. So this capability does the free, mechanical parts and leaves the decision to the agent and the user:

StepScriptWho decides
Look at the footagesurvey.pynobody; it just shows it
Choose a window per linethe agent writes cutlist.jsonagent, then the user
Show the choicespreview.pythe user corrects them
Render the approved choicescut.pynobody

All free. ffmpeg + Pillow + numpy only.

Run

bash
python survey.py --video footage.mp4 --out work/survey            # whole clip, ~1 frame/s
python survey.py --video footage.mp4 --out work/s-12 --from 12 --to 18 --every 0.25 --frames
python preview.py --cutlist work/cutlist.json --out work/review.png
python cut.py --cutlist work/cutlist.json --out work/layer.mp4 [--draft]

survey.json lists cuts (scene changes), motion (change per second) and still (runs of 1.5s+ with nothing moving: a dead screen reads as a frozen video). Read the sheets; open a single frame-*.png (from --frames) when you need to read small UI text.

The cut list

json
{
  "size": [1080, 1920], "fps": 30, "seam": 768, "creator_side": "bottom", "bg": "auto",
  "sources": {"demo": "footage/demo.mp4"},
  "beats": [
    {"id": "b1", "start": 0.0, "end": 3.1, "state": "creator", "vo": "..."},
    {"id": "b2", "start": 3.1, "end": 7.4, "state": "split", "vo": "...",
     "source": "demo", "in": 12.0, "fit": "width", "why": "the brand kit fills in at 13.4s"}
  ]
}
  • state: creator (creator full frame), split (footage in the product zone, creator in the other), product (footage full frame).
  • in is where the window starts in the source; out defaults to in + slot (1.0x). A window may play between 0.5x and 2x; it is never looped or frozen to fill a slot.
  • fit: width (whole frame, letterboxed; the default), cover (fill + crop around focus), crop (a source box [x0,y0,x1,y1] in fractions, then fit by width).
  • bg: auto samples the footage's own corner colour so the letterbox and the footage read as one surface; blur; or #rrggbb.
  • look: "screen": frame the footage as a screen filmed at close range (thin bezel, dark room, faint moire, grain, slow handheld drift). crop picks the source region, screen: {rot, keystone, fill, fit, drift} the geometry, mask boxes are blurred in the source (an email, a customer name). Inserts over a creator: keystone ~0.007, rot within ±1°. Walkthroughs: keystone ~0.024, rot -1.5..0, fill ~0.80. Never bigger angles and never alternate them between beats (read as a wonky camera).
  • transition: {"dissolve_frames": 7} (top level): cross-dissolve every beat into the next. A hard cut between two screens reads as an edit; 6-7 frames reads as the camera moving.
  • A source can be a still image (png/jpg/webp): it holds for its slot. Give it look: "screen" so the drift keeps it alive.
  • why: one line on what the window shows. It is printed on the review sheet so the user can see the reasoning, and it makes the agent say what it saw.

Beats must tile the timeline from 0 with no gaps. cut.py refuses a list that does not.

Rules that were learned the hard way

  1. Watch the whole thing before choosing anything. Look at every sheet of the whole-clip survey first. The moment that proves a line is often not where the recording's order suggests.
  2. Never commit a window from a 1fps glance. Re-survey that range at --every 0.25 and look at every frame. A click, a page load or a half-typed field lives between the samples, and it is exactly what a user then points out ("you didn't look at the video properly").
  3. The line must be true of the footage while it is said. If a line says "it pulls your brand colours", the colours must be on screen during that line, not a second later. Name what is visible in why.
  4. Never crop footage that has copy in it. Cropping a UI panel into a narrower zone slices through words. Default to fit: width; use crop only to isolate a small subject, and check the review sheet for cut-off text.
  5. Avoid dead screens. A window inside a still run reads as the video having stopped. Start it where something moves, or shorten it.
  6. A screen region should be roughly the box's shape. A 9:16 box is filled by a source region of ~0.6-0.8 aspect: crop fewer COLUMNS at full height. A wide, short crop floats in black. For a complete panel with copy, use fit: width and let the room fill.
  7. Watch for a third party inside the footage. A customer logo row or another company's name in the recording ends up in the ad. Crop above it or mask it.
  8. Don't reuse footage. cut.py warns when two beats overlap in the same source: it reads as a loop.
  9. After the takes exist, re-check. Line timing moves when the real voice is aligned (align_beats.py). Re-run preview.py + cut.py on the aligned list and look again.
Show full SKILL.md (255 more words)Show less

Going back and forth with the user

Expect several rounds. Each round:

  1. Show review.png (and a --draft layer if they want to see motion).
  2. The user names beats: "b4 is wrong, use the part where the report loads."
  3. Re-survey just that stretch densely, find the moment, and change only that beat's window. Say what you changed and why.
  4. Re-render the review sheet and show it again.

Picking is free, so take as many rounds as the user needs. Never spend on the creator until the user has approved the cut list.

Product-photo motion and caption review

A real product photo can use look: "photo". This is a dedicated contain/pan/zoom treatment without a screen bezel. photo accepts start/end scale (0.85–1.0), start/end pan pairs (-1–1), a normalized content box, and normalized protected source rectangles. An optional crop must contain every protected product/text rectangle. The selected region remains visible throughout motion. Use photo.box to reserve space above/below the seam for a qualification. Review first/middle/last frames; preview and render share the photo framing function. Existing plain stills and screen looks remain supported.

Before showing the cutlist, generate a footprint with caption-burn's bundled footprint.py, using the final style, anchor and font. Pass that JSON to preview's caption-footprint option. The amber band is the union of the actual rendered caption groups for that beat. Rebuild it after copy/timing/style changes. If it covers qualifying text, adjust photo.box, crop or layout, then inspect the final captioned video. Do not reduce or hide the qualification to make the claim fit.

© 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 9 other files (scripts) in skills/ads/capabilities/footage-cutlist of gooseworks-ai/goose-skills.

  • SKILL.md
  • scripts/_common.py
  • scripts/cut.py
  • scripts/cutlist.py
  • scripts/filmed.py
  • scripts/photo.py
  • scripts/preview.py
  • scripts/survey.py
  • skill.meta.json
  • tests/smoke-test.md

Open the folder on GitHubat commit c650c6d

Compare with similar skills

Footage Cutlist 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.

Footage Cutlist compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Footage Cutlist this skillgooseworks-ai/goose-skills1.2k—~2kAutomated safety check: PassMIT
Guizang Social Cardsop7418/guizang-social-card-skill7.4k1 repos~7.8kAutomated safety check: PassAGPL-3.0
Weekly Changelog Videoheygen-com/hyperframes60k—~3.3kAutomated safety check: PassApache-2.0
Anthropic Brand Stylinganthropics/skills180k30 repos~559Automated safety check: PassApache-2.0
MoneyPrinterTurbo Video Generatorharry0703/MoneyPrinterTurbo130k—~2.1kAutomated safety check: WarnMIT
HyperFrames Media Useheygen-com/hyperframes60k—~2.4kAutomated safety check: PassApache-2.0

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Questions about Footage Cutlist

What does Footage Cutlist do?

Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local. Footage Cutlist is an agent skill from gooseworks-ai/goose-skills. Watch a brand's own product footage (screen recording, product film), pick the moment that proves each line with the user, and render those picks as a product layer — free, local.

When should I use Footage Cutlist?

Footage Cutlist fits situations like: any format that shows real product footage beside; instead of a creator (split-screen.

How do I install Footage Cutlist in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill footage-cutlist -a claude-code`. Or copy the skill folder (skills/ads/capabilities/footage-cutlist in gooseworks-ai/goose-skills) into .claude/skills/footage-cutlist in your project. Claude Code loads it when a task matches its description.

How do I install Footage Cutlist in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill footage-cutlist -a codex`. Or copy the skill folder (skills/ads/capabilities/footage-cutlist in gooseworks-ai/goose-skills) into .agents/skills/footage-cutlist in your project. Codex loads it when a task matches its description.

Can I use Footage Cutlist 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 footage-cutlist -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/footage-cutlist, .gemini/skills/footage-cutlist, .github/skills/footage-cutlist and .opencode/skills/footage-cutlist in your project.

What does Footage Cutlist need to run?

Going by SKILL.md and its folder, Footage Cutlist needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Footage Cutlist 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 Footage Cutlist 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 Footage Cutlist use?

Footage Cutlist 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 Footage Cutlist use?

About 2k tokens (SKILL.md is roughly 7.8k 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 Footage Cutlist?

Skills that share tags, products or a category with Footage Cutlist: Guizang Social Cards (op7418/guizang-social-card-skill, 7.4k stars), Weekly Changelog Video (heygen-com/hyperframes, 60k stars), Anthropic Brand Styling (anthropics/skills, 180k stars) and MoneyPrinterTurbo Video Generator (harry0703/MoneyPrinterTurbo, 130k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Footage Cutlist?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 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.