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Verification

Skill 0xsline/OpenChatCut/src/agent/skills/verification

Local-first conversational AI video editor with a professional multi-track timeline, Agent Skills, MCP integration, and Remotion-powered rendering.

Install
npx -y skills add 0xsline/OpenChatCut --skill verification

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 22 days oldThe repository was created 22 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.

What its author says it does

Copied from the file, not written here

Use when checking whether agent edits are reflected in the OpenChatCut project and editor.

SKILL.md

3.6 KB, as published. Nobody here has run it

Verification

Prefer two signals:

  1. read_project for structure: assets, tracks, items, frame placement, timeline duration.
  2. A visual capture path for rendered evidence at exact frames.

Use view_timeline_frames for composed timeline proof. This verifies the edited OpenChatCut timeline: trims, layers, captions, effects, markers, placeholders, crops, transitions, and layout.

For raw source-asset frame inspection, choose the cheapest path based on where the bytes live:

  • The agent in this build has no local filesystem access; all source bytes live in the project media store (/media/uploads/). Use view_asset_frames with the project asset id — the server takes an ffmpeg contact-sheet fast path automatically, so it is already the cheapest source-frame route.
  • view_timeline_frames renders the composed timeline (the editor-truth check); view_asset_frames samples raw source frames. Pick by what you are verifying.
  • There is no separate get_contact_sheet tool in this build — the contact sheet is what view_asset_frames / view_timeline_frames already return.

Use local/remote source-frame artifacts only for source understanding, moment selection, and rough trim decisions, not as edited output or timeline proof.

For local-only or upload-in-progress media, composed timeline proof may be blocked until the asset has bytes available to the renderer. Source-frame inspection via view_asset_frames still works as long as the asset's bytes are on disk (/media/uploads/).

If both visual proof paths are blocked, ask the user to inspect the OpenChatCut editor directly and note the blocker explicitly.

Useful checks:

  • After import: read_project({ "view": "assets", "assetId": "<prefix>" })
  • After move/trim: read_project({ "view": "timeline" })
  • After visual overlay or MG on any timeline media: view_timeline_frames({ "frames": [30, 45, 75] }), then look at the returned frames.
  • For user-requested source selection or visual moment picking: sample stills with view_asset_frames and inspect them. Use that only to choose source files, moments, and rough trims. Build the visible edit as OpenChatCut timeline items. Do not treat raw source inspection as timeline verification or as permission to produce the edited video elsewhere.
  • For source-frame inspection: call view_asset_frames({"assetId":"...","sourceTimesMs":[...]}) after read_project({"view":"assets"}) confirms the asset id/type. Prefer this over asking the user to reattach the file.
  • For local-only visual verification: upload/register cloud-readable media before relying on connector visual proof.
  • For no-source validation: confirm the tool manifest exposed the parameters you used, then record the visible proof in the trace log.

When talking about seconds, verify the fps from read_project or use adapter tools that resolve fps internally.

When reporting a timeline item location, use only the latest read_project structure for track alias, item id, start, duration, and asset id. Do not report planned/default tracks or tool-call intent as verified placement.

Do not treat a command-line JSON response alone as sufficient when the user asks whether the editor reflects the result. Use the editor URL or visual proof when practical.

If verification fails, classify the gap before changing tools:

  • tool description or schema was insufficient
  • skill instructions were missing a step
  • read_project did not expose enough state
  • editor authorization did not complete
  • media/transcription pipeline failed
  • cloud render/editor observation was blocked

Keep looking

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