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Papercut Explainer

Community
0xS4N4TG
papercut-explainer

Make a papercut reel, video or explainer end to end. Short vertical (9:16) cut-paper explainers from a link (repo, product page, announcement), idea to mp4. Also routes partial asks to the stage skills and hands out the style prompt for other tools.

Overview

Publisher0xS4N4TG
Repositorypapercut-explainer
Skill namepapercut-explainer
Stars
106
Forks
10
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by 0xS4N4TG on GitHub. Read the source before you install it.

Installation

Install the Papercut Explainer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/0xS4N4TG/papercut-explainer/tree/main/skills/papercut-explainer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/0xS4N4TG/papercut-explainer.git /tmp/papercut-explainer
mkdir -p .claude/skills
cp -r /tmp/papercut-explainer/skills/papercut-explainer .claude/skills/papercut-explainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Papercut Explainer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Papercut Explainer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Papercut Explainer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Papercut Explainer

A ~20-45 s vertical explainer that looks like layered construction paper brought to life: grainy paper props with drop shadows, rigid cut-out animation, a slow camera push with stop-motion paper boil, a taped headline banner per beat and word-by-word torn-paper caption tags. Built with Remotion, optional AI-generated textured assets, and any TTS.

Every beat runs three tracks:

  • the voiceover tells the story
  • the taped banner adds a hard fact the voiceover skips
  • a fresh paper set illustrates the voiceover's words, with a rough papercut rebuild of the product UI doing the demo

Reuse the style and the storytelling, not the worked examples' content. Every product, company and number in the examples is invented. The style rules are in references/style-guide.md (section 2 is the Style DNA).

Preflight

  • Library. Resolve $PAPERCUT_LIB, else the PAPERCUT_LIB= line in ${XDG_CONFIG_HOME:-~/.config}/papercut-explainer/config.env, else ask once. Never hard-code a machine path. Stages 1 to 4 need no library.
  • Install mode. Probe it before promising a stage (see Install modes below).
  • Series. Find the series folder (it holds episodes/). Read its SERIES.md if it exists.

Run modes (ask first)

  1. Full reel, idea to mp4: stages 1 to 8, then stage 9 after posting.
  2. Script only: strategy, hook, script (stop after the staging table).
  3. Storyboard from an existing script: papercut-storyboard. Ask for the script, the facts and the HOOK block.
  4. Style prompt for another tool: give the right block from references/style-prompt.md and stop.
  5. Fix or rebuild one scene: papercut-build (scenes step), then papercut-qa.

Intake (ask only what is missing)

  • Source: usually a link (repo, product site, announcement). Everything else is researched from it.
  • The one claim the video proves, and the topic.
  • Role framing: who or what does the work, who advises or approves. Confirm it with the user and never invent product behaviour.
  • CTA keyword and what the viewer gets (the deliverable).
  • Length: default 22-30 s.
  • Audience level (from SERIES.md) and any accent colour or mascot.

Write the answers as the BRIEF block at the top of episodes/NN-slug/research/facts.md (format in papercut-strategy).

Stages

#StageSkillReadsWrites
1Strategypapercut-strategySERIES.md, the sourceBRIEF in research/facts.md, SERIES.md
2Hookpapercut-hookBRIEFHOOK block at the top of papercut/STORYBOARD.md
3Scriptpapercut-scriptBRIEF, HOOKresearch/facts.md, narration/script.json, staging table in STORYBOARD.md
4Storyboardpapercut-storyboardscript.json, STORYBOARD.mdCamera/Cut table, scene blocks, SFX plan in STORYBOARD.md
5Scaffold and planpapercut-buildSTORYBOARD.mdthe project, episode.json
6Voice and timingpapercut-voicescript.json, voice/voice.jsonnarration/ takes, public/vo/captions.json, src/plan.json
7Scenes, sound, renderpapercut-buildplan.json, STORYBOARD.mdsrc/scenes/*, tools/cues.json, out/*.mp4
8QApapercut-qastills, STORYBOARD.mdfixes, the delivery summary
9After postingpapercut-strategy (autopsy), papercut-build (harvest)platform numbers, the projectresearch/analytics/, library promotions

Project files

The pipeline lives in files, not in chat. Each stage reads the files above and writes its own, so a new session can resume from any stage.

<series>/
  SERIES.md                  optional: audience, voice, palette, mascot, keyword log
  voice/voice.json           optional: voice settings (copy library/voice/voice.example.json)
  papercut-assets/           optional: overlay textures and props
  research/analytics/        optional: competitor studies and autopsies
  episodes/NN-slug/
    research/facts.md        BRIEF + facts with sources
    narration/script.json    [{"id": "l1_hook", "text": "..."}, ...]
    papercut/
      episode.json           scenes, banners, hook, gaps, bed, cuts
      STORYBOARD.md          HOOK, staging table, Camera/Cut table, scene blocks

Library catalogs: library/papercut/CATALOG.md (every kit component) and library/audio/CATALOG.md (every SFX and bed). They are generated, so a fresh checkout may lack them or have stale ones. Run python3 $PAPERCUT_LIB/tools/catalog.py to rebuild. Agents read the catalogs, not the kit source.

Reuse before writing: a kit component, a props set or a scene template beats new drawing code. Pick SFX and beds by name.

Install modes

  • Minimal (skills only): strategy, hook, script, storyboard, style prompts. No rendering.
  • Standard: Minimal + build and render with a placeholder voice (silent, or macOS say) via library/tools/placeholder_vo.py, stills QA, SFX and bed audio, the full mp4.
  • Full: Standard + local Qwen3-TTS voiceover and word-timed captions from real audio.

Probe (read-only):

. "$(grep '^PAPERCUT_LIB=' "${XDG_CONFIG_HOME:-$HOME/.config}/papercut-explainer/config.env" | cut -d= -f2-)/env.sh"   # fills PAPERCUT_PY, QWEN_TTS_PY
node -v; ffmpeg -version | head -1
"$PAPERCUT_PY" -c 'import numpy, PIL, soundfile'
"$QWEN_TTS_PY" -c 'import qwen_tts'

The first three lines passing means Standard. The qwen_tts import passing as well means Full. If a stage needs more than the install provides, say so in one line, run ./install.sh --doctor from the repo root, and hand over to papercut-setup instead of improvising. Find the root with readlink on the installed skill folder (under .claude/skills, .agents/skills, .config/opencode/skills or another harness's skills folder), or PAPERCUT_HOME in the config file.

Rules across stages

  • Strategy notes win over style notes: 22-30 s, and no news-framed hooks.
  • Parallelise independent work (scene groups, backdrops and props, re-renders) with agents in one go.
  • Render only when asked. Preview in the Remotion Studio.

Shared references

  • references/style-guide.md: Style DNA, drawing in papercut, palette, fonts, texture, camera and jitter, motion timings, captions, sound.
  • references/style-prompt.md: portable prompts for other agents and generators.
  • references/motion-principles.md: the 12 principles of animation mapped to kit components and helpers, with a use-in-each-stage note.
  • references/remotion-recipes.md: Remotion component sketches, for when there is no library.
  • references/asset-prompts.md: what to generate versus build in code, with prompt templates.
  • references/frame-breakdown.md: a worked ~28 s example at 0.2 s resolution. Copy its event density, not its content.

Hand-off

Start at stage 1 for a full reel, or at the stage the run mode names. After intake, run papercut-strategy; it reads SERIES.md and the BRIEF and hands to papercut-hook. Each stage skill names the next one.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Papercut Explainer AI skill do?

Make a papercut reel, video or explainer end to end. Short vertical (9:16) cut-paper explainers from a link (repo, product page, announcement), idea to mp4. Also routes partial asks to the stage skills and hands out the style prompt for other tools.

Why use Papercut Explainer on TypingMind?

Because you install it once and use it with any model. Papercut Explainer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Papercut Explainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/0xS4N4TG/papercut-explainer/tree/main/skills/papercut-explainer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Papercut Explainer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Papercut Explainer?

As many as you like. As long as a model supports skills, you can use Papercut Explainer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Papercut Explainer AI skill free?

Yes. It is published on GitHub by 0xS4N4TG under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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