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Corey HainesCommunityCreated by Corey Haines

  • Skill
  • Content generator
  • Design and creative
  • MIT
  • No issues found

From the author

When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'record a product demo,' 'feature demo video,' 'in-app demo,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.
— the description by Corey Haines

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to video production by leveraging AI tools and programmatic frameworks. It covers the end-to-end process of creating marketing videos, product demos, and social content. The workflow emphasizes choosing the right production method—programmatic rendering, AI footage generation, or AI avatars—based on the specific needs of the project. It includes guidance on script preparation, technical setup, and the integration of various AI models and frameworks to automate video creation.

What it does

  • Generates video content using AI models for B-roll and hero visuals.
  • Creates programmatic videos using code-based frameworks like Remotion and Hyperframes.
  • Produces talking-head videos using AI avatar services.
  • Provides workflows for repurposing long-form content into short social clips.
  • Offers techniques for reverse-engineering the editing style of reference videos.
  • Supports the creation of repeatable product demo pipelines.

When to use it

  • When you need to produce product demos, explainers, or social media clips.
  • When you require a scalable, template-based video production process.
  • When you need to generate video footage from text or image prompts.
  • When you want to create talking-head content without filming a human presenter.
  • When you need to automate the creation of personalized videos for multiple accounts.

Advantages

  • Enables programmatic video production for data-driven or batch content.
  • Allows for the creation of consistent video assets without manual filming.
  • Facilitates the reuse of components and templates across multiple videos.
  • Provides a clear path for integrating AI agents into the video production pipeline.
  • Supports efficient repurposing of long-form content into platform-specific formats.

Usage examples

“Make me a product demo video for our new dashboard feature.”
The agent will ask for the product marketing context, target platform, and desired length, then guide you through scripting, screen recording, and selecting a programmatic or AI-based production approach.
“Generate a 30-second explainer video using an AI avatar.”
The agent will help you draft a script, select an avatar provider like HeyGen or Synthesia, and configure the necessary parameters for the final video output.
“How can I copy the editing style of this viral video?”
The agent will help you decompose the reference video into a beat sheet, identifying signature moves, cut rhythms, and caption treatments to apply to your own footage.
“Create a programmatic video template for our weekly changelog.”
The agent will assist in setting up a Remotion or Hyperframes project to render videos from your data, ensuring consistent branding and layout across all episodes.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/coreyhaines31/video/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip video.zip -d .agents/skills

Documentation by Corey Haines

Video

You are an expert video producer who helps create marketing videos using AI generation models, AI avatars, and programmatic video frameworks. Your goal is to help users produce professional video content efficiently — from product demos and explainers to social clips and ads.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Video Goal
  • What type of video? (Product demo, explainer, testimonial, social clip, ad, tutorial)

  • What's the target platform? (YouTube, TikTok/Reels/Shorts, website, ads, sales deck)

  • What's the desired length?

2. Production Approach
  • Do you need a human presenter? (AI avatar vs. voiceover vs. screen recording)

  • Do you have existing footage or assets? (Screenshots, logos, product UI)

  • Do you need generated footage? (AI-generated scenes, B-roll)

  • Is this a one-off or a template for repeated use?

3. Technical Context
  • What's your tech stack? (Node.js, Python, etc.)

  • Do you have API keys for any video tools?

  • Budget constraints? (Some tools charge per minute of video)


Choosing Your Approach

Pick the right tool for the job:

ApproachBest ForToolsWhen to Use
ProgrammaticTemplated, data-driven, batch videoRemotion, HyperframesProduct updates, personalized videos, recurring content
AI GenerationOriginal footage from text/image promptsVeo 3, Runway, Kling, SeedanceB-roll, hero shots, creative visuals you can't film
AI AvatarsTalking-head presenter without filmingHeyGen, SynthesiaExplainers, tutorials, multilingual content
Editing/RepurposingCutting long-form into short clipsDescript, Opus Clip, CapCutPodcast/webinar → social clips

Programmatic Video

Build videos with code. Best for repeatable, templated, or data-driven video at scale.

Hyperframes (HTML)

Open-source framework from HeyGen. Compositions are plain HTML/CSS/JS files. Docs: hyperframes.heygen.com.

npx hyperframes init my-video && cd my-video
npx hyperframes preview               # live preview
npx hyperframes render -o output.mp4  # render index.html

Key concept: A composition is one HTML file. The root element sets the canvas and length (data-composition-id, data-duration, data-width, data-height), each element on screen is a clip (class="clip" with data-start, data-duration, data-track-index), and a paused GSAP timeline registered in window.__timelines drives the animation. The hyperframes package is CLI-only; to render from code, use @hyperframes/producer (createRenderJob + executeRenderJob). Full syntax and a working example: Hyperframes integration guide.

Best for: Product announcements, changelogs, data-driven reports, personalized outreach videos.

Remotion (React)

Framework where compositions are React components. Docs: remotion.dev.

npx create-video@latest

Key concept: A composition is a React component that reads the current frame number and returns what's on screen. Props drive content.

export const ProductDemo: React.FC<{ title: string; features: string[] }> = ({
  title, features
}) => {
  const frame = useCurrentFrame();
  return (
    <AbsoluteFill style={{ background: "#000", color: "#fff" }}>
      <h1>{title}</h1>
      {features.map((f, i) => (
        <Sequence from={i * 30} key={i}>
          <p>{f}</p>
        </Sequence>
      ))}
    </AbsoluteFill>
  );
};

Best for: Teams that already write React and want to reuse their components.

When to Pick Which

Both render web pages to video in a headless browser. The difference is what you write:

HyperframesRemotion
You writeHTML, CSS, JSReact components (TypeScript)
TimingDeclared in markup; the renderer seeks a paused animation timelineComputed in code from the current frame number

Pick the one that matches what your team and source material already are: existing HTML, CSS, or browser animations point to Hyperframes; an existing React codebase points to Remotion. For rendering options, licensing, and current features, check each project's docs. They change faster than this skill.


AI Video Generation

Generate original footage from text or image prompts. Use for B-roll, hero visuals, and scenes you can't practically film.

Model Comparison
ModelResolutionMax DurationBest ForCost
Veo 3 (Google)Up to 1080p (4K varies)VariableTop overall quality, synced audioAPI-based
Runway Gen-4Up to 4K~10 sec/genMotion control, temporal consistency, edit-style workflows$12-76/mo
Kling 2.5/3.0 (Kuaishou)Up to 1080pUp to 2 minLong-take generation, lower per-second cost~$0.03/sec
Seedance (ByteDance)Up to 1080pShort clipsFast generation, strong motion fidelity at low cost, batch-friendlyPer-credit
Hailuo / MiniMaxUp to 1080pShort clipsCharacter consistency across shotsPer-credit
Pika 2.x1080pShort clipsQuick effects, image-to-video, lower bar to entryPer-credit
Hunyuan Video / Wan 2720p–1080pVariableOpen-source self-hosted; full control, no API feesFree (GPU)

Quick picks:

  • Highest quality + audio: Veo 3

  • Batch / volume / cost: Kling, Seedance

  • Character consistency across multiple shots: Hailuo

  • Self-hosted, brand-controlled: Hunyuan Video or Wan 2 (open weights)

  • Storyboard → video workflow: Runway, LTX Studio

  • Image-to-video from a still you already have: Kling, Pika, Runway

Prompting for Video Models

Good video prompts specify: subject + action + camera + style + mood

A close-up shot of hands typing on a laptop keyboard,
shallow depth of field, warm office lighting,
camera slowly pulls back to reveal a modern workspace,
cinematic color grading, 4K

Common mistakes:

  • Too vague ("a person working") — add specifics

  • Ignoring camera movement — specify dolly, pan, static

  • Forgetting style — "cinematic," "documentary," "commercial"

  • Requesting text in video — AI models struggle with readable text

For detailed prompting guides: See references/ai-video-prompting.md

When to Use AI Generation vs. Stock
Use CaseAI GenerationStock Footage
Exact scene you imaginedYesRarely matches
Consistent style across clipsYesHard to match
Recognizable real locationsNo (hallucinations)Yes
Specific products/brandsNo (use programmatic)No
Quick B-rollEither worksFaster

AI Avatars

Create talking-head videos without filming. An AI avatar delivers your script with realistic lip-sync, expressions, and gestures.

HeyGen (has MCP server)

Best lip-sync and micro-expressions. 230+ avatars, 140+ languages.

Agent integration: HeyGen has an official MCP server — AI agents can generate avatar videos directly.

PlanVideosDuration
Free3/mo3 min max
CreatorUnlimited5 min
BusinessUnlimited20 min

Check heygen.com/pricing for current prices.

Best for: Product explainers, feature announcements, personalized sales outreach, multilingual content.

Custom avatars: Upload a 2-5 min video of yourself to create a digital twin. Looks and sounds like you, generates videos from text scripts.

Synthesia

Full-body avatars with expressive body language. Built-in script generation from URLs/docs.

Best for: Corporate training, compliance videos, enterprise presentations where professional tone > realism.

When to Use Avatars vs. Other Approaches
ScenarioUse AvatarUse Instead
Recurring content (weekly updates)Yes—
Multilingual versionsYes—
Personalized outreach at scaleYes—
Authentic founder contentNoFilm yourself
Product UI walkthroughNoScreen recording
Creative/artistic videoNoAI generation

Editing & Repurposing Tools

Turn existing content into multiple video formats.

ToolWhat It DoesBest For
DescriptTranscript-based editing — edit video by editing textCleaning up interviews, podcasts, webinars
Opus ClipAuto-clips long videos, scores virality potentialLong-form → short-form at scale
CapCutVisual effects, captions, platform-native stylingTikTok/Reels polish
Captions.aiAuto-captions, eye contact correction, AI dubbingSolo talking-head content
Repurposing Workflow
Long-form content (podcast, webinar, demo)
    ↓
Descript: Clean up, remove filler, polish
    ↓
Opus Clip: Auto-extract 5-10 best moments
    ↓
CapCut: Add captions, effects, platform styling
    ↓
Distribute: TikTok, Reels, Shorts, LinkedIn
Reverse-Engineer a Viral Edit

To replicate the style of a video edit you admire — the cut rhythm, caption treatment, punch-ins, on-screen text, sound design — decompose it into a reusable edit spec (a beat sheet) and apply it to your own footage. Pull the reference with watch-video (visual/multimodal mode extracts frames at the cut points) or social-fetch, extract the edit anatomy beat by beat, and output a per-beat table plus the 3–5 signature moves that make the edit recognizable. Review the beat sheet once before executing it (in Remotion/Hyperframes, CapCut, or an AI restyle tool). Copies the editing grammar, never the reference's footage/script/music. Full method: references/edit-anatomy.md.


Video Production Workflows

Product Demo Video
  1. Script the key features and value props (use copywriting skill)

  2. Screen record the product flow

  3. Programmatic overlay — use Hyperframes/Remotion for titles, callouts, transitions

  4. AI B-roll — generate establishing shots or lifestyle scenes with Veo/Runway

  5. Voiceover — record yourself or use AI avatar for narration

  6. Export at platform-appropriate specs

Scripted in-app recording: for a repeatable demo of a web app flow (re-recorded every release, run against a local build), drive the app with Playwright and add subtitles, a visible cursor, and optional TTS narration. See references/product-demo-recording.md for the workflow, step modes, and local-app gotchas.

Code-rendered product films: for a library of walkthroughs, launch films, and a landing hero that must stay accurate as the UI changes, render every frame from code over real DOM snapshots of a seeded app, with claims checked against the source and a running AGENTS.md of taste rules. See references/code-rendered-product-films.md for the repo setup, UI capture, audio, render, review, and measurement.

Explainer Video
  1. Script the problem → solution → CTA arc

  2. Choose presenter — AI avatar (HeyGen) or voiceover + visuals

  3. Build visuals — programmatic slides, screen recordings, AI-generated scenes

  4. Add captions — always, for accessibility and engagement

  5. Export — landscape for YouTube/website, vertical for social

Batch Social Clips
  1. Create master template in Hyperframes/Remotion

  2. Feed data — product features, testimonials, stats

  3. Render batch — one template, many variations

  4. Add platform-specific captions via CapCut or Captions.ai

  5. Schedule across platforms

Personalized Video per Account

A tailored product film for each target account, made by agents. One-off prompting gets a decent film after 15–20 minutes of back-and-forth; hundreds need a foundation built first.

  1. Product component library — agents walk the app and rebuild key screens as reusable components, one source of truth that every film pulls from instead of redrawing.

  2. Motion primitives — a library of reusable animated parts (camera pushes, screen handoffs, click rhythm, product moves). Fix one and every film inherits the fix.

  3. A menu of demo flows — company-agnostic moments buyers ask to see, each a short list of beats.

  4. Per-account script — an agent drafts it from call notes, CRM win/loss data, and account research, with a fictional company on screen. Voiceover from a cloned narrator voice, with the narrator's consent.

  5. A render engine — code that turns a beat list into a film: camera, transitions, captions, pointer timing, end card.

  6. Automated QA — render contact sheets and flag clipped text, small type, and stray pointers before a person watches.

  7. One tracker — a row per film (account, flow, status, links) that agents update as they build, export, and deliver.

Start with a storyboard for the first film: it sits between the brief and production the way a PRD sits between a spec and code. Expect tool limits at volume (one agent per file, daily API caps), so run films in parallel lanes and batch overnight. Works with any programmatic stack (Hyperframes, Remotion) or a design tool with agent access.

Adapted from Harvey's write-up of its agent-built launch and per-account films (Shawn Farsai and Gabe Pereyra, October 2026).


Agent-Native Video Pipeline

The most powerful setup combines tools that agents can control directly:

Agent writes script (from product context)
    ↓
Hyperframes: Generate templated video (HTML → MP4)
    and/or
HeyGen MCP: Generate avatar video from script
    and/or
Veo/Runway API: Generate B-roll footage
    ↓
Agent assembles final cut
    ↓
Output: Ready-to-publish video

What makes this agent-native:

  • Hyperframes uses HTML — any coding agent can generate it

  • HeyGen MCP server — agents call it directly

  • Video model APIs — standard HTTP requests

  • No manual editing step required


Common Mistakes

  1. Starting with tools, not strategy — decide what video you need before picking tools

  2. AI-generated text in video — models can't reliably render readable text; use programmatic overlays instead

  3. Uncanny valley avatars — if avatar quality matters, invest in HeyGen Creator+ tier

  4. No captions — 85% of social video is watched without sound

  5. Wrong aspect ratio — 9:16 for social, 16:9 for YouTube/website, 1:1 for feeds

  6. Over-producing — authentic often outperforms polished, especially on TikTok


Task-Specific Questions

  1. What type of video do you need? (Demo, explainer, social clip, ad, tutorial)

  2. Do you need a human presenter or can it be voiceover/text?

  3. Is this a one-off or a repeatable template?

  4. What platform is it for? (This determines aspect ratio and length)

  5. Do you have existing assets to work with? (Screenshots, footage, scripts)

  6. What's your budget for video tools?


Tool Integrations

ToolTypeMCPGuide
HeyGenAI avatarsYesheygen.md
HyperframesProgrammatic video-hyperframes.md
RemotionProgrammatic video-remotion.dev
RunwayAI generation-runwayml.com/docs

  • social: For video content strategy, hooks, and what to post

  • ad-creative: For paid video ad creative and iteration

  • copywriting: For video scripts and messaging

  • marketing-psychology: For hooks and persuasion in video

Files

references/code-rendered-product-films.md

Code-Rendered Product Films

How to make a library of product videos (landing hero, launch films, feature walkthroughs for app and docs) entirely in code, with agents doing the production and a human giving taste feedback. No screen recording, no video editor.

Adapted from Elie Steinbock's write-up of how Inbox Zero produced its hero, launch films, and about 15 walkthroughs, each well under $1 in API calls (October 2026).

When to Use This Format

NeedUse
A library of walkthroughs that must stay accurate as the UI changesThis method
One repeatable recording of a flow, re-run each releaseproduct-demo-recording.md (Playwright drives the live app)
Personality-led narrated demoLive screen recording
Per-account films at volumeThe Personalized Video per Account workflow in SKILL.md (same foundations)

The upfront cost is the kit (captured UI, engine, briefs). It pays back from the third or fourth video.

Core Idea: Every Frame Is a Function of Time

Each video is an HTML/CSS/JS page where every frame is a pure function of t. Headless Chrome screenshots each frame and ffmpeg encodes them.

  • Copy changes are re-renders, not re-edits.

  • Frames render in parallel, because nothing carries over between frames.

  • Render purity is mandatory: no Date.now(), no randomness without a seed, no state carried between frames. Workers start mid-timeline.

  • Hide scenes with display:none, not visibility:hidden (visible children leak through a hidden parent).

  • Camera moves, cursor paths, typing, and highlights are all CSS transforms computed from t with springs and easing.

  • Preview in a browser with a ?t=17 parameter to jump to 17s.

A small engine (a few hundred lines) is enough; Hyperframes and Remotion use the same model if you'd rather not write one.

Set Up a Private Video Repo

Keep videos in their own private repo, separate from the product.

AGENTS.md     # read-first: pipeline, taste rules, mistakes made
README.md     # catalog: every video, its branch, length, status, where it's used
BRIEF.md      # product truths, fictional cast, kit API, end-card and taste rules
engine.js     # renderAt(t), scenes, easing, word-by-word captions
render.mjs    # parallel Playwright frame renderer → ffmpeg
mix.sh        # voice + music mix
kit/app/      # real app screens captured as DOM snapshots
kit/          # overlays (captions, callouts, cursor, toasts), end card, third-party UI mocks
music/        # shared music beds
  • One branch and one git worktree per video. main holds only shared things (kit, briefs, tools). Commit kit fixes found mid-video separately so they can be cherry-picked to main.

  • Commit each video's web encode, poster, .vtt captions, and a VARIANT.md (script, timings, chapters, claim checks, docs-vs-UI discrepancies). Git-ignore masters and intermediates.

  • Keep AGENTS.md alive. Every reviewer note and every agent mistake becomes a one-line rule. This is what makes the tenth video right on the first try.

Capture the Real UI

  1. Run the app locally on a seeded database of fictional data: one consistent cast (a main user, 4–5 contacts, a fictional company) with realistic fake records. Use local emulators for third-party APIs where you can.

  2. A Playwright script visits each screen and state the videos need and saves the rendered DOM with inlined CSS as a standalone HTML snapshot, plus a PNG for reference.

  3. Missing states are derived from the nearest snapshot by editing the DOM to match the real component's source. Never invent UI.

  4. Audit every snapshot for leaks before committing: real names, emails, avatars, API keys, and the capture machine's timezone and locale. Once committed, a leak is in git history.

Write the Script (Accuracy First)

  • Read the docs page and the app source for the feature. Every label, button, and step on screen must match the product.

  • When docs and UI disagree, the UI wins. Log the discrepancy in VARIANT.md and fix the docs afterward. This turns video production into a steady source of docs fixes and bug reports.

  • Check claims in code, not just docs ("does feature X actually use data Y?").

  • Keep product truths in BRIEF.md: supported platforms, trial terms, what's early access.

  • Walkthrough shape: a 1–2s product payoff up front, numbered steps with one caption each, one result shot, a minimal end card.

  • Hero and launch openers must say what the product is in the first line. Write several candidates and let the reviewer pick, then record the pick.

Starter Taste Rules

Add your reviewer's rules to these:

  • Openers: light and product-native; reach the product payoff within ~2s. No dark gradients, fake app icons with badges, or captions over blurred clutter.

  • No recaps in words or as a closing montage.

  • End cards: centered logo lockup, one line, a quiet URL. Landing cuts can carry one CTA. No dot-joined copy, no text-stuffed buttons.

  • Legibility: body text ≥ 20px at 1080p; no wide shot of unreadable UI for more than ~1s; no words sliced at frame edges; captions settled ≥ 0.4s before the click they describe.

  • Transitions: no long crossfades between two busy UIs. Cut, push the camera, or use a short blur-through.

Audio

  • Voiceover: a TTS model with style instructions, one voice per format. Generate 2–3 takes per line and have an audio-capable model score pacing, mispronunciations, odd stresses, and clipped endings, then pick.

  • Music: one shared bed per format. Music models ignore the requested length, so detect the beat grid (e.g. librosa) and cut or loop at phrase boundaries.

  • Mix: duck music under the voice with a sidechain, then normalize in two passes to −14 LUFS integrated and −1.2 dBTP true peak.

  • Agents can't listen. Flag music endings and loop points for a human ear-check.

Render

  • Split frames across N Playwright workers; each loads the page, sets t, screenshots. Encode a high-quality master (CRF ~12) and a web copy (CRF ~20).

  • Add a screenshot timeout and retry; long renders hit flaky frames.

  • Support rendering a sub-range or one segment, so a fix doesn't need a full render.

  • On a shared machine: cap workers per agent and stop only your own processes by PID. A broad pkill kills other agents' renders.

Review Before Anyone Sees It

  • Sample frames into 2×2 contact sheets and look at them yourself, zooming in on captions, edges, and data. This is the main review.

  • A video-capable model can check timing and caption order across the whole film, but treat its output as hints: video and audio judges report glitches that aren't there. Verify every claim against frames.

  • Checklist: claims match code, fictional cast only, no leaks, legibility rules, end card.

  • Hand the reviewer the finished files along with the VARIANT.md discrepancy list.

Parallel Production

Once the kit and briefs exist, run one subagent per video, each with its own worktree and branch, the brief, the relevant docs page, and an instruction to read AGENTS.md first. Each writes, builds, renders, reviews, and reports back the file path and discrepancies found. A coordinator merges kit fixes into main, uploads, and places the videos.

Publish and Measure

  • Hosting: a video host with a clean player (no recommendations) and auto-captions, rather than YouTube embeds. Record each asset ID in the repo; pass API tokens via env vars. Upload scripts must surface API errors (a plan limit silently swallowed is a missing video).

  • In the app and docs: lazy-load the player so it adds nothing to the page until opened.

  • Landing hero: track click, start, progress, completion, and close events with a video_id. Don't A/B test the hero video on overall signups: only the visitors who click play can be affected, so the test is underpowered. Ship the better cut and compare cuts on watch-through and viewer-to-signup rate by video_id. See ab-testing.

  • YouTube: the API keeps uploads from unaudited projects private, so upload through Studio. If an agent automates Studio, have it read every field back before scheduling.

Mistakes to Skip

MistakeRule
Invented UI in the first cutReal DOM snapshots only
A claim the product doesn't makeCheck claims in code
A tagline the company doesn't useOffer options, record the reviewer's pick
A real timezone leaked into a committed snapshotAudit snapshots before committing
Agents killed each other's rendersStop processes only by your own PID
A judge model "found" glitches that weren't thereVerify against frames
An upload script swallowed a hosting errorScripts surface API errors

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs an API key, Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Corey Haines
Publisher
Corey Haines (Community)
Source
coreyhaines31/marketingskills · skills/video · commit 96798cf
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Corey Haines or Corey Haines.

Download ZIP

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