agentleFS
mocchaleramocchalera/video-os-v2/AGENTS.md

video-os-v2

AGENTS.md4 starsChanged 2 months agoMITView on GitHub

RoughCut Agent — artifact-driven video editing agent for turning source footage and a short brief into analysis, retrieval, rough/fine planning, compiled timelines, rendered outputs, and QA feedback loops. Required local toolchain: Project Loop Harness and the project-control-loop skill are temporarily paused for this repository. Do not invoke the skill or run pcl commands unless the user explicitly asks to re-enable or use them. The installed .project-loop/ state and pcl.yaml are retained unchanged so the harness can be re-enabled later. Never…

  • Reads credentials
  • Installs packages

The file

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

3 rules. About 0 are common, 1 is distinctive, and 2 are 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.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  • could be automatic: a setting could enforce it
  1. Very specific:

    Treat canonical artifacts as truth: creative_brief.yaml, selects_candidates.yaml, edit_blueprint.yaml, timeline.json, render-report.json, …

    line 33
  2. Distinctive:

    Keep Qwen3-VL, CLAP, Marlin, and other local-model paths fail-open when model cache or optional dependencies are absent.

    line 34
  3. Very specific:

    Do not commit .env.local, source footage, rendered media, or generated project outputs unless explicitly requested.

    line 35

≈ “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

AGENTS.md

Project

RoughCut Agent — artifact-driven video editing agent for turning source footage and a short brief into analysis, retrieval, rough/fine planning, compiled timelines, rendered outputs, and QA feedback loops.

Setup

npm install
cp .env.example .env.local

Required local toolchain:

  • Node.js 22.x LTS (CI parity; see .nvmrc / .node-version)
  • npm 10.x

Common Commands

TaskCommand
Demonpm run demo
Testnpm test
Typechecknpx tsc --noEmit
Buildnpm run build
Verifynpm run verify
Full verifynpm run verify -- --full

Hard Rules

  • Preserve unrelated dirty worktree changes.
  • Treat canonical artifacts as truth: creative_brief.yaml, selects_candidates.yaml, edit_blueprint.yaml, timeline.json, render-report.json, QA reports, and footage.db.
  • Keep Qwen3-VL, CLAP, Marlin, and other local-model paths fail-open when model cache or optional dependencies are absent.
  • Do not commit .env.local, source footage, rendered media, or generated project outputs unless explicitly requested.
  • For review-only or docs-only tasks, do not modify runtime code.

Where Agents Should Look

  • North Star: goal.md
  • Durable plan and handoff: docs/project-memory/
  • README and CLI entrypoints: README.md
  • Architecture: ARCHITECTURE.md
  • Planning route guidance: docs/planning-routes.md
  • Source: runtime/, scripts/, apps/, editor/
  • Schemas: schemas/
  • Tests: tests/, editor/tests/
<!-- project-loop-harness:start -->

Project Loop Harness (temporarily paused)

Project Loop Harness and the project-control-loop skill are temporarily paused for this repository. Do not invoke the skill or run pcl commands unless the user explicitly asks to re-enable or use them.

The installed .project-loop/ state and pcl.yaml are retained unchanged so the harness can be re-enabled later. Never hand-edit their generated or database files while the harness is paused.

<!-- project-loop-harness:end -->

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.