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Agent skill

Product Video Capture

by kdlbs in kdlbs/kandev

Record, camera-process, encode, stage, and validate polished Kandev product films, landing-page loops, screenshots, and alternate framing from isolated demo data.

AGPL-3.0Auto-check passedMedia & Creative

Install Product Video Capture

skills CLI
$ npx skills add kdlbs/kandev --skill product-video-capture -a claude-code

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

GitHub CLI
$ gh skill install kdlbs/kandev product-video-capture --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/kdlbs/kandev.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/product-video-capture .claude/skills/product-video-capture && 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
product-video-capture
GitHub stars
912
Token cost
~2.2k tokens
SKILL.md length
1,147 words
Files
6 (incl. references)
Skills in repo
45
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Record, camera-process, encode, stage, and validate polished Kandev product films, landing-page loops, screenshots, and alternate framing from isolated demo data.

  • Works in 11 steps: Always invoke /product-demo-seeding… → Create a unique writable CAPTURE_ROOT,… → Review the seed handoff and rehearse the… → …
  • The user asks for product videos
  • SKILL.md covers Prerequisites And Repo Discovery, Choose Deliverable, Pipeline and Non-Negotiable Capture…, plus 2 more sections
  • Runs JavaScript scripts from its folder; calls git

What it does

Product Video Capture is an agent skill from kdlbs/kandev. Record, camera-process, encode, stage, and validate polished Kandev product films, landing-page loops, screenshots, and alternate framing from isolated demo data. Use when the user asks for product videos, GIF-like feature demos, cursor-follow camera motion, desktop/mobile captures, recaptures, landing media, or different framing; always invoke product-demo-seeding first.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `evals/evals.json`, `references/camera-encoding.md` and `references/capture-pipeline.md`).

It sits in Media & Creative, covering Video production and Landing pages. The repository describes itself as: AI Kanban & Development Environment. Orchestrate multiple agents, review changes, open PRs. Multi-provider, self-hostable, no telemetry. The licence is AGPL-3.0.

When your agent uses it

  • The user asks for product videos
  • GIF-like feature demos
  • Cursor-follow camera motion
  • Desktop/mobile captures

Example prompts

  • “/product-video-capture”

Requirements

  • Node.js

Workflow steps

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

  1. Always invoke /product-demo-seeding before this skill. For alternate framing, it validates provenance and isolation instead of…
  2. Create a unique writable CAPTURE_ROOT, for example with mktemp -d "${TMPDIR:-/tmp}/kandev-product-capture.XXXXXX". Use it for every raw…
  3. Review the seed handoff and rehearse the full native desktop/mobile story once. Reject stale UI, fixture accumulation, duplicate tasks…
  4. Reset to a fresh per-take database or deterministically clean all rehearsal-created state. Record one continuous take as an unzoomed…
  5. Record semantic action timestamps, target bounds, target glyph bounds, and dense pointer/touch metadata beside the raw file. Include each…
  6. Stop recording before capturing the clean poster.
  7. Inspect raw frames before post-production. Reject UI bugs, padding, double cursors, fixture text, dead waits, and unreadable states.
  8. Build a smooth, center-biased post camera from semantic events. Ignore micro-jitter, but keep every intentional pointer/touch journey…
  9. Encode WebM, MP4, and WebP through landing's tested camera/encoder scripts.
  10. Review fixed-fraction frames and playback on desktop, native mobile, and reduced motion. For landing films, also review at the actual…
  11. Keep all candidates staged outside production. Promotion is a separate, explicit action after approval; a capture request alone does not…

What it can do on your machine

Read from SKILL.md and the folder at commit 53a00c2. 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 script files (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Product Video Capture loads about 2.2k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 1,147 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from kdlbs/kandev at commit 53a00c2, republished under its AGPL-3.0 licence (© kdlbs). 1,147 words, ~2,245 tokens.

Download SKILL.mdSave it as .claude/skills/product-video-capture/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
product-video-capture
description
Record, camera-process, encode, stage, and validate polished Kandev product films, landing-page loops, screenshots, and alternate framing from isolated demo data. Use when the user asks for product videos, GIF-like feature demos, cursor-follow camera motion, desktop/mobile captures, recaptures, landing media, or different framing; always invoke product-demo-seeding first.

Product Video Capture

Produce reusable clean masters first; derive presentation from them later. Preserve the raw master and camera config so every camera choice is reversible.

Prerequisites And Repo Discovery

  • Require Linux Xvfb, Chrome for Testing, Playwright/CDP, FFmpeg/FFprobe, a Kandev checkout with E2E fixtures, and the landing repository.
  • Resolve the Kandev root with git rev-parse --show-toplevel, then verify it contains scripts/dev-isolated and apps/web/e2e/.
  • Resolve the landing root from KANDEV_LANDING_REPO when set and verify it contains both scripts/product-loop-camera.mjs and scripts/product-loop-encoder.mjs. If the variable is unset or its path fails verification, search the available workspace and sibling checkouts for both marker files; do not select a directory by name alone.
  • If discovery finds zero or multiple landing candidates, ask the user to identify the checkout. Record the resolved roots as KANDEV_REPO and LANDING_REPO, and use those variables for every command and copy operation.

Choose Deliverable

RequestPath
New feature/storySeed with /product-demo-seeding, then capture desktop and mobile masters
Different zoom/crop/pacingInvoke /product-demo-seeding to re-prove source/isolation/provenance, then reuse an approved raw master and change camera config only
New poster/static imageExtract a settled pointer-free frame from approved master or recapture native screenshot
Longer walkthroughKeep continuous 1x source; add a tested delivery profile instead of speed ramps
Actual GIF requiredDerive from approved video last; retain WebM/MP4 as primary web formats

Pipeline

  1. Always invoke /product-demo-seeding before this skill. For alternate framing, it validates provenance and isolation instead of manufacturing new visible state. An approved raw is reusable only when its source SHA still equals freshly fetched origin/main; otherwise recapture. Then resolve and verify KANDEV_REPO and LANDING_REPO as described above. Do not assume task-specific absolute paths.
  2. Create a unique writable CAPTURE_ROOT, for example with mktemp -d "${TMPDIR:-/tmp}/kandev-product-capture.XXXXXX". Use it for every raw, proof, config, and staged delivery path; do not write candidates into production assets.
  3. Review the seed handoff and rehearse the full native desktop/mobile story once. Reject stale UI, fixture accumulation, duplicate tasks, selector drift, or a theme that differs from the requested product surface before starting the recorder.
  4. Reset to a fresh per-take database or deterministically clean all rehearsal-created state. Record one continuous take as an unzoomed high-resolution master per form factor. Use true physical pixels, not a padded Playwright video canvas.
  5. Record semantic action timestamps, target bounds, target glyph bounds, and dense pointer/touch metadata beside the raw file. Include each intentional movement's start, intermediate samples, arrival, pointer glyph bounds, and visibility interval.
  6. Stop recording before capturing the clean poster.
  7. Inspect raw frames before post-production. Reject UI bugs, padding, double cursors, fixture text, dead waits, and unreadable states.
  8. Build a smooth, center-biased post camera from semantic events. Ignore micro-jitter, but keep every intentional pointer/touch journey inside the tested safe frame. For long travel, widen, pan with the active pointer, then tighten after arrival; complete menus, provider panels, sheets, and dialogs have framing priority.
  9. Encode WebM, MP4, and WebP through landing's tested camera/encoder scripts.
  10. Review fixed-fraction frames and playback on desktop, native mobile, and reduced motion. For landing films, also review at the actual theater width; full-resolution frames alone do not prove readable marketing media.
  11. Keep all candidates staged outside production. Promotion is a separate, explicit action after approval; a capture request alone does not authorize overwriting landing assets.

Read capture-pipeline.md before recording and camera-encoding.md before conforming media.

Non-Negotiable Capture Properties

  • Fresh isolated data; no main instance, credentials, database, or production ports.
  • Separate desktop and native-mobile scripts. Never crop desktop footage into a mobile deliverable.
  • Exact profiles: desktop source 3840x2400 at 25 fps delivers 1920x1200 at 25 fps; native mobile source and delivery are 1290x2796 at 25 fps.
  • Raw master is one continuous take at 1x with no internal cuts, speed ramps, or audio. It also has no body transform, camera, crop, or concat.
  • Disable OS cursor in X11 capture; show one intentional high-contrast DOM cursor/touch treatment.
  • Use real UI input and retain dense semantic cursor/touch samples, target bounds, independently measured target and pointer glyph bounds, and motion-inclusive visibility intervals for camera design and audit.
  • Browser chrome absent; product fills the physical frame.
  • Any responsive/product defect remains visible or blocks capture. Do not hide it with capture CSS.
Show full SKILL.md (451 more words)Show less

Camera And Delivery

Use $LANDING_REPO/scripts/product-loop-camera.mjs and $LANDING_REPO/scripts/product-loop-encoder.mjs. Their tests define dimensions, frame rate, maximum zoom, smoothness, loop reset, codecs, and poster quality. For semantic landing desktop stories, use formFactor: "landing" with cameraProfile: "landing-editorial" and cap maximum zoom at 2.0x. The landing-editorial profile requires both focusTrack and pointerTrack; configs without either are invalid. Native mobile stays native-size and uses the tested mobile cap, never more than 2.0x. Change these contracts test-first when the user requests a genuinely different delivery format.

Do not remove waits with cuts or speed changes. Improve the source choreography or recapture. One trim at the beginning/end is acceptable; time skips are not.

The camera is center-biased, not cursor-naive: follow the active semantic target while retaining the context needed to understand the action. Use one smooth establishing tighten, then keep a stable working depth. Widen-pan-tighten for long journeys. Keep the full dialog or menu visible as a priority, even when that means less zoom. The visible cursor never leaves the frame, including its complete rendered glyph and safety margin.

Explicit rejection rules:

  • Reject lazy global zoom that holds one crop regardless of the active target.
  • Reject zoom breathing: repeated in/out depth changes around one subject or state.
  • Reject any zoom or camera move away from the active cursor while it is traveling.
  • Reject stale UI, stale builds, accumulated rehearsal data, or captures from a non-current source checkout.
  • Reject wide shots that make product text unreadable at actual size.

Acceptance Gate

Follow qa-checklist.md. Do not ship until:

  • raw and delivery dimensions are proven by decoded pixels, not only container metadata;
  • cadence is constant 25 fps, timestamps have no gaps, the FFmpeg log proves zero duplicated and dropped frames, and no audio stream exists;
  • 10/25/50/75/90% frames, an overview contact sheet, actual-size 100% frame proofs, and full playback pass visual review;
  • camera motion is smooth, reaches intended depth, and uses the profile's tested loop frame: centered 1x for a wide loop, or identical first, settled penultimate, and final crops for an approved focused landing/docs loop that improves readability while preserving identifying context;
  • text, menus, diffs, pointer, and touch targets remain inside frame; frame-by-frame pointer containment passes with a deliberate edge margin;
  • staged WebM, MP4, WebP, responsive source selection, lazy loading, and reduced-motion behavior pass codec probes and real-browser playback;
  • SHA-256 hashes cover raw masters, camera configs, semantic metadata, proofs, and every delivery candidate;
  • provenance records Kandev and landing commit SHAs, commands, source/delivery profiles, isolated ports/display/browser profile, and deterministic seed identity;
  • teardown proves all capture processes, ports, X displays, browser profiles, temporary specs, databases, and temp data are gone.

Report story, seed, form factors, raw/delivery dimensions, durations, codecs, camera profile, output sizes/hashes, visual audit, browser checks, teardown, and any unsupported or blocked surface.

© kdlbs, AGPL-3.0. 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 5 other files (references) in .agents/skills/product-video-capture of kdlbs/kandev.

  • SKILL.md
  • evals/contract.test.mjs
  • evals/evals.json
  • references/camera-encoding.md
  • references/capture-pipeline.md
  • references/qa-checklist.md

Open the folder on GitHubat commit 53a00c2

Compare with similar skills

Product Video Capture 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.

Product Video Capture compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Product Video Capture this skillkdlbs/kandev912—~2.2kAutomated safety check: PassAGPL-3.0
SaaS Videopexoai/pexo-skills804—~1.3kAutomated safety check: PassMIT
Scroll Promo Site Builderkangarooking/kangarooking-skills662—~2.6kAutomated safety check: PassMIT
Product DemoAlexwtlf/agentic-product-demo387—~8.8kAutomated safety check: PassCustom licence
Remotion Ad Videoleosssvip-dot/remotion-ad-video-skill111—~6.3kAutomated safety check: PassMIT
Motion Adfabricioctelles/skills106—~4.1kAutomated safety check: PassApache-2.0

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Categories

Questions about Product Video Capture

What does Product Video Capture do?

Record, camera-process, encode, stage, and validate polished Kandev product films, landing-page loops, screenshots, and alternate framing from isolated demo data. Product Video Capture is an agent skill from kdlbs/kandev. Record, camera-process, encode, stage, and validate polished Kandev product films, landing-page loops, screenshots, and alternate framing from isolated demo data.

When should I use Product Video Capture?

Product Video Capture fits situations like: the user asks for product videos; GIF-like feature demos; Cursor-follow camera motion; desktop/mobile captures.

How do I install Product Video Capture in Claude Code?

Run `npx skills add kdlbs/kandev --skill product-video-capture -a claude-code`. Or copy the skill folder (.agents/skills/product-video-capture in kdlbs/kandev) into .claude/skills/product-video-capture in your project. Claude Code loads it when a task matches its description.

How do I install Product Video Capture in Codex?

Run `npx skills add kdlbs/kandev --skill product-video-capture -a codex`. Or copy the skill folder (.agents/skills/product-video-capture in kdlbs/kandev) into .agents/skills/product-video-capture in your project. Codex loads it when a task matches its description.

Can I use Product Video Capture 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 kdlbs/kandev --skill product-video-capture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-video-capture, .gemini/skills/product-video-capture, .github/skills/product-video-capture and .opencode/skills/product-video-capture in your project.

What does Product Video Capture need to run?

Going by SKILL.md and its folder, Product Video Capture needs JavaScript for the scripts in its folder and the command-line tools its instructions call (git). Our summary lists: Node.js.

Does Product Video Capture access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Product Video Capture 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. Review the folder before installing.

What licence does Product Video Capture use?

Product Video Capture is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Video Capture use?

About 2.2k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9k tokens, read only when the agent opens those files.

What are the alternatives to Product Video Capture?

Skills that share tags, products or a category with Product Video Capture: SaaS Video (pexoai/pexo-skills, 804 stars), Scroll Promo Site Builder (kangarooking/kangarooking-skills, 662 stars), Product Demo (Alexwtlf/agentic-product-demo, 387 stars) and Remotion Ad Video (leosssvip-dot/remotion-ad-video-skill, 111 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Video Capture?

kdlbs (a GitHub organization) maintains it in kdlbs/kandev, which has 912 GitHub stars. The repository holds 45 skills in this directory. The repository was last updated on October 10, 2026.

Source: kdlbs/kandev on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.