SkillAtlasSkill 详情

render-split-screen-creator

Put your AI agent on the growth team.

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项目 README

来源文件:README.md

抓取于 2026年8月20日
CleanShot 2026-07-13 at 20 15 47@2x CleanShot 2026-07-13 at 20 16 54@2x

AI Skills for Brand Growth

Put your AI agent on the growth team.

Research customers and competitors, analyze what is working, create the next campaign, and learn from the result. Goose Skills gives Claude Code, Cursor, Codex, and other coding agents ready-to-use workflows for ads, social media, content, competitive intelligence, SEO, lead generation, and GTM.

Browse all skills at https://skills.gooseworks.ai

Works with Claude Code · Cursor · Codex

npm version License: MIT Skills


Contents


Quick Start

AI Coding Agents (Claude Code, Cursor, Codex, etc)

Paste this into your coding agent (Claude Code, Cursor, or Codex) and it'll set everything up:

Install the Gooseworks skills:

In the terminal, run `npx gooseworks install --all`.

Then run `npx gooseworks login` and it'll open a browser to sign in and set up the tools, then confirm it worked.

The skills can be used with /gooseworks <prompt>

Claude Cowork

Run this command in a terminal first:

npx gooseworks install --all

Then authenticate:

npx gooseworks login

Then make sure you're working inside a local folder on your machine, and then you can use the skills in Cowork like this:

Use /gooseworks skill to generate some ad creatives

Install manually

Prefer to run it yourself? Use the command directly:

npx gooseworks install --all       # All detected agents

This gives your coding agent access to the full catalog of 200+ skills. After installing, just ask your agent to use any skill by name.

If you want a cloud-based AI coworker that already knows all these skills and more, sign up to Gooseworks


Brand Growth collection

The Brand Growth collection is a curated path through the normal Goose Skills catalog for consumer and ecommerce brand work. It is not a separate package or command: install GooseWorks once, then ask /gooseworks for the outcome you need.

StageWhat your agent can doExample skills
ResearchUnderstand the brand, customers, competitors, audiences, creators, trends, comments, and product demandbrand-research, audience-research, comment-mining, competitor-social-research, influencer-prospecting, trend-discovery, product-demand-research
AnalyzeDiagnose ads, creator profiles, transcripts, policy risk, landing-page message match, and unusual social performancecompetitor-ad-intelligence, creator-profile-teardown, transcript-intelligence, meta-ads-analyzer, meta-ad-policy-checker, ad-to-landing-page-auditor, outlier-post-finder
CreateRepurpose research, remix graphic ads, make product photography and social graphics, and animate static imagescontent-repurposing, remix-graphic-ad-from-reference, product-photoshoot, goose-graphics, animate-image
Learn and iterateBring results back into research and analysis, then decide the next testRe-run the relevant analysis skill with current performance and audience evidence

ScrapeCreators powers structured public social and ad-library research behind several workflows. Signed-in GooseWorks users access it through the managed first-party proxy and do not need a separate ScrapeCreators key. The user-facing skills turn that source data into a brief, shortlist, analysis, or recommendation instead of returning raw API output.

Browse the Brand Growth collection

After installation, start with:

/gooseworks onboard me

The agent will collect the useful company context for future growth work and finish by asking what you want to do first. Existing users can skip onboarding and keep using /gooseworks exactly as they do today.


Commands

npx gooseworks search "reddit scraping"   # Search the skill catalog
npx gooseworks credits                     # Check your credit balance
npx gooseworks update                      # Update to latest skill version

Skills Catalog

200+ skills across the growth stack, grouped by focus area:

CategoryWhat's inside
AdsResearch, build, and analyze paid campaigns across Meta and Google
SEOKeyword research, content gaps, SERP analysis, technical audits
Lead generationFind, enrich, and qualify prospects for your pipeline
OutreachDraft, personalize, and run outbound across email and social
ContentBlog posts, social content, carousels, video scripts, newsletters
ResearchCompany, market, and prospect deep-dives
Competitive intelTrack competitor pricing, launches, positioning, and ads
MonitoringWatch for mentions, signals, and changes across the web
SocialScrape and analyze social platforms and audiences
BrandVoice, positioning, and visual brand assets

Browse and search every skill at skills.gooseworks.ai.


Usage Examples

After installing, just ask your coding agent naturally:

"/gooseworks Generate static ad creatives for my brand"
"/gooseworks Use the reddit-post-finder skill to search r/startups"
"/gooseworks Use the apollo-lead-finder skill to find CTOs at AI companies"
"/gooseworks Use the competitor-intel skill to research Acme Corp"
"/gooseworks Use the goose-graphics skill to create a LinkedIn carousel about our launch"

Your agent will search the GooseWorks catalog, download the skill, and run it automatically.


Building from Source

git clone https://github.com/gooseworks-ai/goose-skills.git
cd goose-skills
node scripts/validate-skills.js  # Validate SKILL.md + skill.meta.json contract
node scripts/build-index.js      # Generate skills-index.json
node bin/goose-skills.js list    # Test locally

Skill Metadata Contract

Each skill directory must include:

  • SKILL.md — Skill documentation and usage guide
  • skill.meta.json — Machine-readable metadata

skill.meta.json fields:

FieldRequiredDescription
slugYesUnique kebab-case identifier
categoryYescapabilities, composites, or playbooks
tagsYesString array of category tags
installation.base_commandYesInstall command
installation.supportsYesArray: claude, codex, cursor
featuresNoFeature flags
github_urlNoSource repository URL
authorNoSkill author
example_promptNoCopyable prompt shown in the catalog and docs for trying the skill

Security & Trust

These skills run inside your coding agent, so it's worth knowing exactly what they do:

  • Open source & inspectable. Every skill — its SKILL.md instructions and all scripts — lives in this repo under the MIT license. The gooseworks CLI fetches skills at runtime so recipes stay current, but the source you'd run is right here to read, diff, or pin before you run it.
  • Scripts run locally. Skill scripts execute on your machine and write to /tmp/gooseworks-scripts/, never into your project directory. Only API requests go through GooseWorks servers; review any script before letting your agent run it.
  • Your agent stays in control. The skills are a tool your agent reaches for when it fits the task (data at scale, sources behind auth, a specific provider) — not a replacement for its built-in web search or fetch on quick lookups. You can read or edit any installed SKILL.md to tune that behavior.
  • Credentials stay local. Auth is a Bearer token stored at ~/.gooseworks/credentials.json (file mode 0600). Third-party provider keys (Apify, Apollo, etc.) are held server-side — your token never touches them. All network calls are HTTPS.
  • The MCP server is opt-in. Registering the GooseWorks MCP server is off by default; it only happens if you explicitly run gooseworks install --mcp.

Found something that looks off? Open an issue — we'd rather fix it in public.


License

MIT — see LICENSE for details.

The skill files and CLI in this repository are MIT-licensed. The GooseWorks API they connect to is a separate paid service governed by its own terms.

文档与办公

低风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:0 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/ads/capabilities/render-split-screen-creator" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/ads/capabilities/render-split-screen-creator" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/ads/capabilities/render-split-screen-creator" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/ads/capabilities/render-split-screen-creator" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/ads/capabilities/render-split-screen-creator" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: render-split-screen-creator
description: Assemble a split-screen creator ad from a config — a two-zone vertical composite where a supplied AI-creator lip-sync take fills the BOTTOM ~48% while real 16:9 product/demo clips run uncropped in the TOP ~52%, each top clip contain-fit with a darkened blurred cover-scale fill of the same clip (never black bars), a 3px brand-color divider between the zones, the creator slice cover-fit per the per-scene VO timing, scenes hard-concatenated with the body audio being the concatenated creator VO slices, an end card held on the last sharp frame ~3s, then the ASSEMBLED cut transcribed with local Whisper (not the raw VO — concat drops inter-scene silence) and word-level captions burned in the chosen style. This is the FREE deterministic assembly + caption stage (two-zone composite + blurred fill + divider + hard-concat + end card + captions); the VO comes from create-vo-elevenlabs, the anchor from create-image-gpt-image-fal, and the whole-VO lip-sync from a paid VEED Fabric 1.0 take (a no-atom upstream input). Use for the split-screen-creator format.
status: active

render-split-screen-creator

Assemble a split-screen creator ad from a config: a two-zone vertical (1080×1920, 9:16, ~40s) format where an AI creator talking-head anchors the BOTTOM ~48% of the frame and real 16:9 product/demo clips run uncropped in the TOP ~52%, a 3px brand-color divider between the zones. The creator delivers the whole VO cold-to-camera and each top clip proves the claim its VO line makes. This capability is the FREE, deterministic assembly + captions — the two-zone composite (contain-fit + blurred-cover fill + divider + creator slice), the hard-concat, the end card, and the word-level caption burn from the assembled cut.

scripts/config.example.json is the worked example (Perplexity concept-10 "Bloomberg terminal", ~40s 1080×1920 9:16, 6 scenes + an end card); scripts/PIPELINE.md maps every config block to its source step and scripts/README.md documents the free assembly.

Run

This is the FREE, deterministic assembly + captions stage — it spends nothing. The paid inputs are separate steps — the VO (create-vo-elevenlabs, ElevenLabs eleven_v3 with-timestamps, sliced into per-scene windows), the photoreal MEDIUM chest-up AI-creator anchor (create-image-gpt-image-fal, gpt-image-2) — shot at a natural webcam distance (headroom + shoulders, real room), not a plain-background close-up headshot (see the anchor note below), and the whole-VO lip-sync (a paid VEED Fabric 1.0 @ 720p take — a no-atom step, image_url = the anchor, audio_url = the vo mp3, ~$0.15/sec, ~$5.90 for a 39s VO; run its calls sequentially, veed/fabric-1.0 storage-auths 403 under parallel load). Given the creator lip-sync take + the per-scene VO timing + one 16:9 top clip per scene + the scene-1 hook graphic + the end-card clip, render-split-screen-creator composites the two zones, hard-concats the scenes, appends the end card, transcribes the assembled cut, and burns the captions → the master. Re-cuts reuse the existing VO / lip-sync / clips and cost $0.

Contract (the free assembly)

  • Two-zone split, top ~52% / creator ~48%. The TOP zone runs the real 16:9 product/demo clip contain-fit (uncropped), the BOTTOM zone is the creator lip-sync framed head-to-shoulders: scale-to-width × a small ZOOM (~1.15–1.2) then crop the zone with a downward offset so the face sits upper-middle and the shoulders enter the bottom. (A plain cover + crop-toward-top shows only the head and cuts the shoulders — and it can't rescue an anchor that was shot too close; fix the anchor distance first.) Tune zoom/offset visually against the source video's creator framing — it's a FREE re-assemble, no VEED re-run. A 3px brand-color divider separates the zones. Canvas 1080×1920, top_height ~998. Keep every stacked height EVEN (998 + 4 divider + 918 = 1920) — libx264 rejects odd dimensions.
  • Anchor = photoreal MEDIUM shot, not a studio headshot. The lip-sync only looks as good as the anchor. It must be photoreal/candid (real lived-in room, natural skin cues), framed chest-up at a natural webcam distance (headroom + shoulders), not a plain-background close-up and not a phone-selfie pose copied from another format (e.g. ugc-walk-and-talk). VEED Fabric handles photoreal fine (unlike Seedance).
  • Blurred-cover fill, never black bars. The top clip's letterbox margins are filled with a darkened blurred cover-scale of the same clip — a flat charcoal/black bar reads cheap.
  • One claim per scene, shown as it's said. Each top clip is windowed (top_start/top_end) to the on-message segment that proves its VO line. Never loop a short clip — set the window and the assembler speed-fits it to the scene (looping replays into a sparse/black tail).
  • The creator VO is the entire audio bed — no separate music. Body audio = the concatenated creator VO slices, timed per the per-scene timing.json; the lip-sync drives the mouth.
  • Hard-concat the scenes; end card on the last SHARP frame. Hard-cut concat (no dissolves); append the end card holding the last sharp frame ~3s. If the end-card clip fades to black, hold the last sharp second (endcard.clip_end), not the black tail.
  • Caption the ASSEMBLED cut, not the raw VO. Concat drops inter-scene silence, so the ad timeline ≠ the VO timeline; only the final cut's audio yields correct caption timing. Transcribe the assembled cut with local Whisper, build word-level cues (sentence-aware chunking), burn the ASS in the chosen style (serif-accent, kinetic-pop, …). Keep the -precaption cut + the .ass sidecar so captions restyle without re-rendering the composite. If the host ffmpeg lacks libass, render the cues as timed PIL PNG overlays (ffmpeg overlay=…:enable='between(t,st,en)') at the same placement.
  • FFmpeg composite, deterministic, FREE. Two-zone composite per scene, hard-concat, append the end card, mux the creator VO, loudnorm I=-14 → a 1080×1920 h264+aac master. No paid calls, no keys — the VEED Fabric lip-sync is a supplied input, produced upstream.

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