agentleFS
hranesshraness/atet/.agents/skills/cinematic-pack-authoring/AGENTS.md

atet / cinematic-pack-authoring

AGENTS.md7 starsChanged 47 days agoMITView on GitHub

1 other project uses a version of this.

The file

We split this AGENTS.md into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep the skill declarative: agents author bounded JSON documents (scene, direction, recipe pack).

    line 8
  2. Very specific:

    Keep every documented command aligned with the actual slopcamera workflows plan|run CLI surface and the SpatialRecipePackSchema contract in …

    line 9

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

Contents

  • SKILL.md – the recipe-pack authoring workflow for the built-in cinematic-world graph.
  • agents/openai.yaml – agent-runner display metadata.

Guidelines

  • Keep the skill declarative: agents author bounded JSON documents (scene, direction, recipe pack); they never register operations or code.
  • Keep every documented command aligned with the actual slopcamera workflows plan|run CLI surface and the SpatialRecipePackSchema contract in src/spatial-scene/recipe-pack.ts.
  • Preserve the explicit selection and promotion boundary: cinematic-world plans and reviews only.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.