SkillAtlasSkill 详情

render-chatgpt-chat

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

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 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:2 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-chatgpt-chat" 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-chatgpt-chat" 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-chatgpt-chat" 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-chatgpt-chat" 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-chatgpt-chat" 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-chatgpt-chat
description: Assemble a ChatGPT chat-reveal video ad from a thread + timeline JSON — one continuous Playwright recording of a ChatGPT mobile chat (user types with the iOS keyboard up → taps send → keyboard slides down + header cluster swaps in one beat → one gray loading dot → the assistant answer streams in word-by-word) crossfaded into a designed end card, with subliminal ChatGPT SFX and an optional ducked music bed. FREE assembly (Playwright + ffmpeg); the recipe supplies the per-brand thread + timeline + end-card config and gates the paid music call to its own capability. The ChatGPT sibling of render-imessage-chat. Use for the chatgpt-chat format.
status: active

render-chatgpt-chat

The free renderer for the chatgpt-chat video ad format — the "I just asked ChatGPT…" creative, where someone asks ChatGPT a question and the streamed assistant answer is the punchline (the brand surfacing as the natural response). Deterministic Playwright + ffmpeg assembly; no generative video of the UI, so the bubble text and streamed answer stay pixel-crisp.

This is the ChatGPT sibling of render-imessage-chat. Reach for this one when ChatGPT is the more credible host for the answer; reach for iMessage when the punchline is a peer's reaction in a DM. The template recipe (DB) supplies the per-brand thread + timeline + end_card config and gates the paid music call (music bed → create-music-elevenlabs) to its own capability.

What it renders

One continuous take — never scene-by-scene (every reload flickers):

  1. User types in the composer with the iOS keyboard up (composer-type).
  2. Send-tap is ONE beat — the user bubble pops, the keyboard slides down, and the header right-cluster swaps (personPlus/dottedCircle → edit/more) all on the same t. Never sequence them across frames.
  3. One gray loading dot holds ~500ms (never three — three reads as iMessage typing, wrong app), silently (no SFX on the dot).
  4. The assistant answer streams in word-by-word (stream-words, ~7 wps) with a soft opacity ramp; the conversation auto-scrolls to keep it in view.
  5. Crossfade to a designed end card (wordmark + ⭐ proof row + trust trio + CTA pill) and mux a ducked music bed → master MP4.

The chat records at the ChatGPT-native ~9:19.5 (default 750×1624) to match a real iPhone screen recording. Never stretch the chat to a different aspect ratio — the end card is scaled-to-fit + padded to the chat's dimensions in stitch, so the chat is never touched.

Run

cd scripts && npm install            # once — installs Playwright
npx playwright install chromium      # once
node record-chat.js     --config config.json --out-dir <work>   # → master-chat.mp4 + .sfx.json
node render-end-card.js --config config.json --out-dir <work>   # → scene-end-endcard.mp4
bash stitch.sh --chat <work>/master-chat.mp4 --end <work>/scene-end-endcard.mp4 \
     --sfx <work>/master-chat.sfx.json --out <work>/master-final.mp4 \
     --pad-color "#ffffff" [--music <work>/music-bed.mp3] [--also-1x1]
  1. record-chat.js — reads config.json (thread + timeline + geometry), renders the bundled create-chatgpt-mockup HTML once with every message pending, walks the timeline on requestAnimationFrame inside the page, records it as one continuous MP4, and emits the deterministic SFX cue list.
  2. render-end-card.js — fills end-card.template.html from config.end_card (wordmark/logo_svg, stars, proof, trust trio, CTA, colors) → still MP4. This is the SAME generic end card as render-imessage-chat (copied verbatim).
  3. stitch.sh — normalizes the end card to the chat's dimensions, crossfades chat → end card, layers the subliminal ChatGPT SFX, optionally ducks a music bed under it, and optionally derives a 1:1 crop. All FREE ffmpeg. Pass --pad-color = end_card.bg (default #ffffff, ChatGPT light mode) so the pad under the end card is seamless.

The chat body: bundled create-chatgpt-mockup

The ChatGPT chat HTML comes from create-chatgpt-mockup (its generate.js + templates/ produce the light-mode ChatGPT iOS HTML — status bar, header, message rows, streaming word-spans, composer, and the inline iOS keyboard). Those files are bundled into scripts/mockup/ so this capability renders the chat body standalone — no sibling fetch of create-chatgpt-mockup is required. record-chat.js does require('./mockup/generate.js').

The keyboard is inlined by the mockup (renderKeyboard) — no separate keyboard atom.

Timeline events (consumed by record-chat.js)

KindMeaning
composer-type{ text, dur_sec } — type into the composer. SFX = one key-tap per word.
composer-clearWipe the composer instantly (fire at send-tap).
keyboard-show / keyboard-hideSlide the iOS keyboard up / down.
send-tapPulse the send button. SFX = send-tap.
pop{ target: <msg-id> } — reveal a message row.
header-swap{ value: "alt" } — swap the header right-cluster.
loading-dot-show / loading-dot-hide{ target: <dot-id> } — the single gray dot.
send-state{ value: "streaming"|"active" } — composer send-button state.
stream-words{ target, dur_sec, wps } — reveal the assistant answer word-by-word. SFX = stream-tick every 12 words + response-done at the end.
scroll-to{ target, dur_ms } — smooth-scroll a row into view.

See scripts/config.example.json for the canonical thread + timeline (the "one beat" send-tap and the streamed list answer are both wired there).

Contract

  • FREE assembly: Playwright record + ffmpeg composite/mux + the bundled SFX. No AI-rendered text — the bubbles, the streamed answer, and the end-card copy are all real HTML/PIL, never invented by a model.
  • The recipe (DB) supplies the per-brand config: the thread (light-mode ChatGPT, assistant message set stream: true), the timeline, the end_card (prefer a real logo_svg wordmark), and an optional music bed.
  • SFX are subliminal by design (ChatGPT has no native chime): key-tap -28dB, send-tap -20dB, stream-tick -32dB, response-done -22dB, and never a cue on the loading dot. Set "sfx": false in the config to ship the chat silent.

Gaps / routing notes

  • Music bed is an input, not generated here — the recipe gates it to create-music-elevenlabs (paid, proxy-routed, billed to the Ads agent) and passes the file into stitch.sh --music.
  • Bundled SFX are synthesized stand-ins. The original four wavs (key-tap/send-tap/stream-tick/response-done) were lost from Git LFS (the objects 404 on the server), so assets/sfx/*.wav are freshly synthesized subliminal clicks/ticks. They work as-is; swap in real wavs (same filenames) for tuned SFX.
  • Portability: everything runs from the fetched /tmp/gooseworks-scripts/render-chatgpt-chat/scripts/… — the chatgpt-mockup generator + templates are bundled under scripts/mockup/, and the generic end card is bundled under scripts/. No /Users/… or repo-relative paths, and no required sibling fetch.
  • Requires ffmpeg/ffprobe on PATH and Playwright Chromium (npx playwright install chromium).

Self-QC (per project rule — always /watch the master)

  • Keyboard is up the whole time the user types, and slides down only on the send-tap beat (never visible while the answer streams).
  • Send-tap is one beat: user bubble + keyboard-down + header-swap on the same frame.
  • Exactly one gray loading dot for ~500ms (not three), and no SFX on the dot.
  • The answer streams word-by-word, left-to-right / top-to-bottom, not all-at-once.
  • No OpenAI spiral logo above any assistant title (the spiral is empty-state only).
  • No micro-flicker / scene cuts; the end-card pad color matches end_card.bg.

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