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Agent skill

Product Demo Seeding

by kdlbs in kdlbs/kandev

Seed coherent, disposable Kandev demo scenarios for screenshots, product films, landing-page media, and reproducible UI captures.

AGPL-3.0Auto-check passedFrontend & Design

Install Product Demo Seeding

skills CLI
$ npx skills add kdlbs/kandev --skill product-demo-seeding -a claude-code

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

GitHub CLI
$ gh skill install kdlbs/kandev product-demo-seeding --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-demo-seeding .claude/skills/product-demo-seeding && 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-demo-seeding
GitHub stars
912
Token cost
~1.8k tokens
SKILL.md length
951 words
Files
5 (incl. references)
Skills in repo
45
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Seed coherent, disposable Kandev demo scenarios for screenshots, product films, landing-page media, and reproducible UI captures.

  • Works in 5 steps: Fetch origin/main immediately before… → Create a clean detached capture worktree… → Record both SHAs and prove HEAD equals… → …
  • Media needs believable tasks
  • SKILL.md covers Current-Main Source Gate, Workflow, Safety Contract and Truthfulness Contract, plus 3 more sections
  • Runs JavaScript scripts from its folder; calls git

What it does

Product Demo Seeding is an agent skill from kdlbs/kandev. Seed coherent, disposable Kandev demo scenarios for screenshots, product films, landing-page media, and reproducible UI captures. Use when media needs believable tasks, workflows, agents, executors, integrations, plans, sessions, diffs, reviews, or native-mobile states; invoke before product-video-capture and never use a developer's main instance or data.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `evals/evals.json`, `references/isolation-and-seeding.md` and `references/story-recipes.md`).

It sits in Frontend & Design, covering Landing pages and Video production. 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

  • Media needs believable tasks
  • Native-mobile states
  • Invoke before product-video-capture and never use a developers main instance

Example prompts

  • “/product-demo-seeding”

Requirements

  • Node.js

Workflow steps

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

  1. Fetch origin/main immediately before capture setup.
  2. Create a clean detached capture worktree from origin/main; do not reuse the task worktree or a landing branch.
  3. Record both SHAs and prove HEAD equals origin/main. Require an empty git status --porcelain before building and before capture.
  4. Install dependencies and build frontend/backend from that worktree. Verify it contains scripts/dev-isolated and apps/web/e2e/.
  5. Stage specs, raw masters, configs, proofs, logs, and delivery candidates under a fresh artifact root outside production assets.

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 Demo Seeding loads about 1.8k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 951 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.6k

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). 951 words, ~1,834 tokens.

Download SKILL.mdSave it as .claude/skills/product-demo-seeding/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
product-demo-seeding
description
Seed coherent, disposable Kandev demo scenarios for screenshots, product films, landing-page media, and reproducible UI captures. Use when media needs believable tasks, workflows, agents, executors, integrations, plans, sessions, diffs, reviews, or native-mobile states; invoke before product-video-capture and never use a developer's main instance or data.

Product Demo Seeding

Build a truthful product state before recording pixels. Treat narrative, data, and UI route as one artifact.

Current-Main Source Gate

Product capture represents current Kandev, so old checkouts and old builds are invalid even when their scripts still run.

  1. Fetch origin/main immediately before capture setup.
  2. Create a clean detached capture worktree from origin/main; do not reuse the task worktree or a landing branch.
  3. Record both SHAs and prove HEAD equals origin/main. Require an empty git status --porcelain before building and before capture.
  4. Install dependencies and build frontend/backend from that worktree. Verify it contains scripts/dev-isolated and apps/web/e2e/.
  5. Stage specs, raw masters, configs, proofs, logs, and delivery candidates under a fresh artifact root outside production assets.

Reject stale UI, selector, script, or capture choreography until current source is built and the complete route passes rehearsal. Never substitute an old server, cached bundle, or last month's working selectors for this gate.

Workflow

  1. Read /e2e and the relevant existing specs/page objects under apps/web/e2e/.
  2. Write a one-sentence story: user goal, visible action, visible result.
  3. Identify separate desktop and native-mobile routes. Mobile must use native mobile surfaces, not a desktop crop.
  4. Explore manually with scripts/dev-isolated --web when needed. For reproducible capture, use the worker-scoped E2E fixture and ApiClient.
  5. Create a fresh fictional repository, workspace, workflow, tasks, sessions, and provider state through supported E2E/API methods.
  6. Seed only enough state to make the story legible. Dense believable data beats empty fixtures; excessive data hides the action.
  7. Open the intended UI and verify every visible label, control, and transition before recording.
  8. Rehearse without a recorder, discard rehearsal mutations, then reseed an identical pristine baseline for each take.
  9. Hand the scenario, routes, selectors, semantic target bounds, complete intentional pointer journeys, provenance, and cleanup command to /product-video-capture.

Safety Contract

  • Allocate a fresh temp HOME, KANDEV_HOME_DIR, database, repository/worktree root, unique ports, unique display, unique browser profile, and artifact root. Names and ownership must identify one take.
  • Use deterministic mock GitHub and Jira providers plus a controlled agent. Use fixed IDs and timestamps, authors, titles, bodies, states, checks, and ordering.
  • No credentials or network access to real provider services is permitted. Unset provider tokens and fail closed if mock routing cannot be proven in backend logs.
  • Never copy the developer's database for marketing capture. scripts/dev-isolated --copy-db is outside this workflow.
  • Never query, mutate, stop, or reuse the developer's instance, DB, or data.
  • Keep temporary capture specs or harness code under CAPTURE_ROOT, outside the source worktree. If the runner requires an in-worktree file, write only its exact path to a take-owned excludes file under CAPTURE_ROOT and pass it through command-local core.excludesFile configuration inherited by the capture process. Prove the clean-worktree status gate still returns an empty git status --porcelain, stage a source copy, then remove the file and exclusion during teardown. Never mutate the shared .git/info/exclude; linked worktrees and sibling agents may depend on it.
  • Stop if isolation cannot be proven from process args, ports, paths, and logs.

Read isolation-and-seeding.md before starting an instance.

Show full SKILL.md (444 more words)Show less

Truthfulness Contract

  • Seed real records and drive real controls. Do not fabricate menu items, executor families, integrations, checks, or agent support in the DOM.
  • Prefer coherent API-seeded labels over capture-time text replacement. If fixture sanitation is unavoidable, document exact substitutions; never add controls, hide product bugs, or change behavior.
  • Use current product capabilities. Inspect selectors and API helpers again instead of copying an old capture spec blindly.
  • If a responsive surface is broken, report the product bug and choose another truthful native route only when it demonstrates the same capability.
  • Keep local paths, test directives, generic mock responses, fixture names, and host identity out of visible frames.
  • Give capture-only mock executors a logical command label such as mock-agent; never expose the temporary executable's absolute host path in a visible command, profile, or session label.

Per-Take State Contract

Rehearsal changes product state: integration flows create tasks. A successful rehearsal therefore cannot share recording state.

  • Start from a fresh database for every recorded take. An equivalent reset is acceptable only when it deletes all scenario records and reseeds one canonical baseline with a verified seed hash.
  • Use a rehearsal reset or discard its entire temp root before RECORD. Restart backend/browser ownership as needed so cached client state cannot survive.
  • Before each take, assert exact workspace/workflow/task/provider counts, unique IDs, unique visible task titles, and expected ordering. Reject duplicate or accumulated tasks, fixtures, sessions, or provider-created records.
  • Give desktop GitHub, mobile GitHub, desktop Jira, and mobile Jira independent state ownership. One failed or repeated take must not contaminate another.
  • Never clean visible state with DOM deletion, CSS hiding, or capture-time text replacement. Fix the seed or start fresh.

Story Selection

Use story-recipes.md for Plan, Coordinate, Prepare, Run, Review, integrations, editor/terminal, and mobile recipes. Recipes are patterns, not frozen scripts.

Acceptance Gate

Before capture, verify:

  • Story has a clear initial state, action, and result. Treat 7-11 seconds as a target, never a reason to omit an honest route step, rush readability, or shorten the settled loop.
  • Desktop and mobile each have a native script and safe composition.
  • Visible data forms one fictional project narrative.
  • No production endpoint, credential, local path, fixture copy, or unsupported control is visible.
  • Current build and all story-critical routes passed a recorder-free rehearsal; rehearsal state was reset or discarded.
  • Exact baseline record counts prove no duplicate fixture accumulation before RECORD.
  • Seed teardown removes temporary profiles, specs, processes, ports, database, and repository.

Report seed name, story, source SHA, seed hash, fixed provider fixture version, separate desktop/native-mobile routes, process args, ports, display, browser profile, database/temp/artifact roots, mock-routing proof, any sanitation, and teardown result. This provenance must map every delivered take to its isolated baseline and current-main source.

© 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 4 other files (references) in .agents/skills/product-demo-seeding of kdlbs/kandev.

  • SKILL.md
  • evals/contract.test.mjs
  • evals/evals.json
  • references/isolation-and-seeding.md
  • references/story-recipes.md

Open the folder on GitHubat commit 53a00c2

Compare with similar skills

Product Demo Seeding 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.

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Product Demo Seeding this skillkdlbs/kandev912—~1.8kAutomated safety check: PassAGPL-3.0
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App Preview Videodotnetdreamer/open-screenshot-generator127—~3.3kAutomated safety check: PassMIT
SaaS Videopexoai/pexo-skills804—~1.3kAutomated safety check: PassMIT
Impeccablebestofjs/bestofjs3.1k26 repos~2.6kAutomated safety check: PassMIT

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Categories

Questions about Product Demo Seeding

What does Product Demo Seeding do?

Seed coherent, disposable Kandev demo scenarios for screenshots, product films, landing-page media, and reproducible UI captures. Product Demo Seeding is an agent skill from kdlbs/kandev. Seed coherent, disposable Kandev demo scenarios for screenshots, product films, landing-page media, and reproducible UI captures.

When should I use Product Demo Seeding?

Product Demo Seeding fits situations like: media needs believable tasks; native-mobile states; invoke before product-video-capture and never use a developers main instance.

How do I install Product Demo Seeding in Claude Code?

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

How do I install Product Demo Seeding in Codex?

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

Can I use Product Demo Seeding 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-demo-seeding -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-demo-seeding, .gemini/skills/product-demo-seeding, .github/skills/product-demo-seeding and .opencode/skills/product-demo-seeding in your project.

What does Product Demo Seeding need to run?

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

Does Product Demo Seeding 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 Demo Seeding 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 Demo Seeding use?

Product Demo Seeding 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 Demo Seeding use?

About 1.8k tokens (SKILL.md is roughly 7.3k 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 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Product Demo Seeding?

Skills that share tags, products or a category with Product Demo Seeding: Brainstorm Experiments New (phuryn/pm-skills, 27k stars), Aesthetic Usability Effect (hashgraph-online/awesome-codex-plugins, 1.3k stars), App Preview Video (dotnetdreamer/open-screenshot-generator, 127 stars) and SaaS Video (pexoai/pexo-skills, 804 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Demo Seeding?

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.