Cinematic Pack Authoring logo

Cinematic Pack Authoring

Organization
hraness
cinematic-pack-authoring

Author a Slopcamera spatial recipe pack — a bounded, inert, content-addressed JSON document that names an admitted scene, one direction document, gallery axes, preview render requests, declared effects, and a temporal audit — then plan or run the built-in cinematic-world workflow. Use when a user asks an agent to direct a 3D scene, iterate camera/look/sequence variants, or review compiled direction before explicit selection.

Overview

Publisherhraness
Repositoryslopcamera
Skill namecinematic-pack-authoring
Stars
7
Forks
1
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Cinematic Pack Authoring 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/hraness/slopcamera/tree/main/.agents/skills/cinematic-pack-authoring
  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/hraness/slopcamera.git /tmp/slopcamera
mkdir -p .claude/skills
cp -r /tmp/slopcamera/.agents/skills/cinematic-pack-authoring .claude/skills/cinematic-pack-authoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cinematic Pack Authoring 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 Cinematic Pack Authoring 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 Cinematic Pack Authoring 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.

Author a cinematic recipe pack

Slopcamera's extension surface is declarative: coding agents extend behavior by authoring bounded JSON documents, never by registering code. The operation registry is closed, and the cinematic-world workflow composes only registered operations. A recipe pack is the complete input contract for that loop.

Documents an agent authors

  1. Scene (slopcamera.spatial-scene, schemaVersion 1) — the admitted world. Usually already admitted through slopcamera scene commands; the pack binds it by digest, it is never embedded twice.
  2. Direction (slopcamera.spatial-direction, schemaVersion 1) — semantic beats, actions, camera coverage, and look intents. projectDigest must equal the scene's canonical SHA-256. Character and subject references are semantic ids (hero), which compile against entity_hero-style scene entities.
  3. Recipe pack (slopcamera.spatial-recipe-pack, schemaVersion 1) — the inert plan document:
json
{
  "kind": "slopcamera.spatial-recipe-pack",
  "schemaVersion": 1,
  "packId": "recipe_<slug>",
  "sceneSha256": "<canonical scene sha-256>",
  "direction": { "<the direction document>" },
  "cameraId": "camera_main",
  "axes": ["camera", "lighting"],
  "previews": [{
    "name": "reel",
    "request": {
      "cameraId": "camera_main",
      "mode": { "kind": "beauty" },
      "selection": { "kind": "frame", "timeUs": 0 }
    }
  }],
  "effects": {
    "renderPlan": {
      "kind": "slopcamera.spatial-render-plan",
      "schemaVersion": 1,
      "quality": {
        "outputBytes": 8000000, "particleCount": 0, "pixelBudget": 2073600,
        "simulationSteps": 0, "texturePixelBudget": 2073600, "tier": "preview"
      }
    },
    "particleSystems": [],
    "simulationBakes": []
  },
  "temporalAudit": { "timesUs": [0, 2000000, 4000000] }
}

Boundaries the pack enforces

  • axes — 1 to 6 unique values from performance | camera | lighting | materials | effects | sequence.
  • previews — at most 4 named render requests; each request is revalidated against the spatial render-request schema at workflow admission.
  • effects — optional; when present it requires renderPlan (a declarative slopcamera.spatial-render-plan document with a quality tier and optional post-process stack) plus bounded particleSystems/simulationBakes. Each preview render first plans an integrity-bound effects document (scene.effects.plan) and renders with it.
  • temporalAudit — optional; requires cameraId; timesUs bounds to 64 samples.
  • packId — recipe_ prefix, at most 64 slug characters.

Invoking the workflow

The workflow input is { "pack": <pack>, "scene": <scene>, "source": { "path": "<repo-relative scene path>" } }. pack.sceneSha256 must equal the canonical scene digest, so compute it first:

sh
slopcamera scene inspect <scene.json> --json   # read sceneSha256

Plan is effect-free — it compiles the graph and reports requirements only:

sh
slopcamera workflows plan cinematic-world --input input.json --json

Run executes the graph: inspection, direction check, compilation, one gallery plan per axis, per-preview effects binding and render, and the temporal audit.

sh
slopcamera workflows run cinematic-world --input input.json --json

What the workflow never does

  • It never selects or promotes a candidate. spatial.project.select-candidate and iteration.select are separate explicit operations a caller invokes after reviewing the plan outputs.
  • Compiled direction stays verified: false; it is a proposal document, not applied state.
  • Planning and graph compilation execute no operations, read no credentials, contact no providers, and mutate no project state.

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 Cinematic Pack Authoring AI skill do?

Author a Slopcamera spatial recipe pack — a bounded, inert, content-addressed JSON document that names an admitted scene, one direction document, gallery axes, preview render requests, declared effects, and a temporal audit — then plan or run the built-in cinematic-world workflow. Use when a user asks an agent to direct a 3D scene, iterate camera/look/sequence variants, or review compiled direction before explicit selection.

Why use Cinematic Pack Authoring on TypingMind?

Because you install it once and use it with any model. Cinematic Pack Authoring 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 Cinematic Pack Authoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hraness/slopcamera/tree/main/.agents/skills/cinematic-pack-authoring. 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 Cinematic Pack Authoring?

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 Cinematic Pack Authoring?

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

Is the Cinematic Pack Authoring AI skill free?

Yes. It is published on GitHub by hraness 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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