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render-glassy-matte-grwm

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.

文档与办公浏览器与自动化

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-glassy-matte-grwm" 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-glassy-matte-grwm" 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-glassy-matte-grwm" 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-glassy-matte-grwm" 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-glassy-matte-grwm" 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-glassy-matte-grwm
description: Assemble a multi-scene GRWM beauty-demo ad from a config — a locked-identity creator applies ~5 products step by step while a SEPARATE ElevenLabs voiceover narrates and every scene cut is snapped to the VO's product-name word-starts (Whisper word-level timestamps), then ~5 Playwright product overlay cards (real PDP-verified taglines) are composited onto the master each on its product-NAME word-start, the SEPARATE VO is mixed on top of a ducked music bed at loudnorm I=-14, clean-white 3-words/cue captions are burned, and the video closes on a flat-lay end card. This is the FREE deterministic assembly stage (re-cut to the VO word-starts, hard-concat, Playwright card render + card composite, VO plus music mix, caption burn, flat-lay end card); the VO, scene clips, product cutouts, and music come from create-music-elevenlabs / create-image-gpt-image-fal / create-video-fal. Use for the glassy-matte-grwm format.
status: active

render-glassy-matte-grwm

Assemble a multi-scene GRWM beauty-demo ad from a config — a locked-identity creator applies ~5 makeup/skincare products step by step at a vanity, a separate ElevenLabs voiceover narrates the routine, and every scene cut is snapped to the VO's product-name word-starts, with ~5 Playwright product overlay cards on the product-name beats, a ducked music bed, burned captions, and a flat-lay end card. This capability is the FREE, deterministic assembly — the Whisper-driven re-cut + hard-concat, the Playwright card render + card composite, the VO + music mix, the caption burn, and the flat-lay end card.

This is the multi-scene beauty demo, distinct from the single-take apparel outfit-reveal (ugc-grwm, one Seedance reference-to-video call with native lip-sync and minimal post). Here the timeline is driven by a SEPARATE VO and the scenes are re-cut to its word-starts.

scripts/config.example.json is the worked example (DIBS Beauty "5-Step Glassy Matte Routine", ~32s 1080×1920 9:16, 12 VO-snapped cuts + 5 product cards); 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 stage — it spends nothing. The paid inputs are separate capabilities — the SEPARATE narration VO (create-music-elevenlabs, or a user-supplied mp3; word-level Whisper timestamps set the timeline), ~7 Seedance scene clips one per product step (create-video-fal), the ~5 white-bg product cutouts + the flat-lay end-card still (create-image-gpt-image-fal), and the ducked music bed. Given the VO + .words.json + one clip per step + the ~5 product cutouts + the music bed, render-glassy-matte-grwm re-cuts each clip to its VO word-start window, hard-concats on the cut, renders + composites the product cards on the product-name beats, mixes the VO over the ducked music, burns the captions, and appends the flat-lay end card → the master. Re-cuts reuse the existing VO / clips / cutouts and cost $0.

Contract (the free assembly)

  • The SEPARATE VO drives the timeline — Whisper it first. The narration is a separate track (not a native take). Its word-level timestamps set every cut; the atempo'd VO ends shorter than the plan expects (a 1.15× VO landed ~27.5s), so time every window to the word-starts, never to a pre-planned grid.
  • Scene cuts snap to the "step N" word-start; cards snap to the product-NAME word-start. Cut to the next product when its step is announced; the card animates in ~1s later when the NAME is spoken. Both happen. ~12 cuts over ~32s (cuts/10s ≈ 3.75).
  • Hard-concat with a re-encode. Hard cuts on the VO word-starts, no dissolves; re-encode the concat -c:v libx264 -crf 20 — -c copy corrupts the duration when zoompan/PNG clips are in the chain.
  • Product cards — Playwright, real cutout, PDP-verified tagline. Playwright renders the card template at 2× scale (real white-bg cutout thumb + name + PDP tagline). The cutout must match the REAL product, not the Seedance scene's hallucinated barrel; the tagline is verified against the brand PDP (AI flat-lays hallucinate sublines). Composite each card onto the master snapped to its product-NAME word-start, 1s fade-in, held until the next product is named. PNG overlay inputs need -loop 1 -t <dur> — without it the PNG emits one frame at t=0 and the fade/enable filters silently no-op (cards go invisible).
  • VO leads the ducked music bed. Mix the SEPARATE VO on top of the ducked music (the VO is the lead), loudnorm I=-14. If the host ffmpeg lacks a filter, apad/atrim to length before the mix.
  • Captions — clean-white, override the preset. Clean-white captions from the VO's Whisper words, overridden to 3 words/cue, ~3.0% font, ~20% margin, NO pill, NO shadow (the default 5-words/4.5%/18% reads too dense). Burn last. If the host ffmpeg lacks libass, render the cues as timed PIL PNG overlays composited with ffmpeg overlay=…:enable='between(t,st,en)' at the same placement.
  • Flat-lay end card. Append the flat-lay still (ken-burns hold ~4s) — a gpt-image-2 flat-lay of the ~5 products; do NOT trust its AI-rendered sublines for the card taglines.
  • FFmpeg composite, deterministic, FREE. Re-cut, hard-concat, render + composite the cards, mix the VO over the ducked music, burn the captions, append the end card → a 1080×1920 30fps h264+aac master (~32s). No paid calls, no keys.

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