复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Put your AI agent on the growth team.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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
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>
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
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
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.
| Stage | What your agent can do | Example skills |
|---|---|---|
| Research | Understand the brand, customers, competitors, audiences, creators, trends, comments, and product demand | brand-research, audience-research, comment-mining, competitor-social-research, influencer-prospecting, trend-discovery, product-demand-research |
| Analyze | Diagnose ads, creator profiles, transcripts, policy risk, landing-page message match, and unusual social performance | competitor-ad-intelligence, creator-profile-teardown, transcript-intelligence, meta-ads-analyzer, meta-ad-policy-checker, ad-to-landing-page-auditor, outlier-post-finder |
| Create | Repurpose research, remix graphic ads, make product photography and social graphics, and animate static images | content-repurposing, remix-graphic-ad-from-reference, product-photoshoot, goose-graphics, animate-image |
| Learn and iterate | Bring results back into research and analysis, then decide the next test | Re-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.
npx gooseworks search "reddit scraping" # Search the skill catalog
npx gooseworks credits # Check your credit balance
npx gooseworks update # Update to latest skill version
200+ skills across the growth stack, grouped by focus area:
| Category | What's inside |
|---|---|
| Ads | Research, build, and analyze paid campaigns across Meta and Google |
| SEO | Keyword research, content gaps, SERP analysis, technical audits |
| Lead generation | Find, enrich, and qualify prospects for your pipeline |
| Outreach | Draft, personalize, and run outbound across email and social |
| Content | Blog posts, social content, carousels, video scripts, newsletters |
| Research | Company, market, and prospect deep-dives |
| Competitive intel | Track competitor pricing, launches, positioning, and ads |
| Monitoring | Watch for mentions, signals, and changes across the web |
| Social | Scrape and analyze social platforms and audiences |
| Brand | Voice, positioning, and visual brand assets |
Browse and search every skill at skills.gooseworks.ai.
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.
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
Each skill directory must include:
SKILL.md — Skill documentation and usage guideskill.meta.json — Machine-readable metadataskill.meta.json fields:
| Field | Required | Description |
|---|---|---|
slug | Yes | Unique kebab-case identifier |
category | Yes | capabilities, composites, or playbooks |
tags | Yes | String array of category tags |
installation.base_command | Yes | Install command |
installation.supports | Yes | Array: claude, codex, cursor |
features | No | Feature flags |
github_url | No | Source repository URL |
author | No | Skill author |
example_prompt | No | Copyable prompt shown in the catalog and docs for trying the skill |
These skills run inside your coding agent, so it's worth knowing exactly what they do:
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./tmp/gooseworks-scripts/, never into your project directory. Only API requests go through GooseWorks servers; review any script before letting your agent run it.SKILL.md to tune that behavior.~/.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.gooseworks install --mcp.Found something that looks off? Open an issue — we'd rather fix it in public.
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.
Built by GooseWorks
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: activeAssemble 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.
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.
top_height ~998. Keep every stacked height EVEN
(998 + 4 divider + 918 = 1920) — libx264 rejects odd dimensions.ugc-walk-and-talk). VEED Fabric handles photoreal fine (unlike Seedance).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).timing.json; the
lip-sync drives the mouth.endcard.clip_end),
not the black tail.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.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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