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Known Errors

CommunityPopular
0xsline
known-errors

Use when a OpenChatCut tool call fails or returns an unexpected shape.

Overview

Publisher0xsline
RepositoryOpenChatCut
Skill nameknown-errors
Stars
1.9K
Forks
277
Bundled files
Instructions only
LicenseAGPL-3.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Known Errors 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.
  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/0xsline/OpenChatCut.git /tmp/OpenChatCut
mkdir -p .claude/skills
cp -r /tmp/OpenChatCut/src/agent/skills/known-errors .claude/skills/known-errors
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Known Errors 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 Known Errors 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 Known Errors 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.

Known Errors

edit_item update raw shape:

  • Wrong: { "id": "abc", "fromFrame": 30 }
  • Right: { "json": "{\"updates\":[{\"id\":\"abc\",\"fromFrame\":30}]}" }
  • Use this same updates shape for common moves, trims, and track changes.

edit_item add raw shape:

  • The new item goes inside the adds array of the json transaction: { "json": "{\"adds\":[{...}]}" }.
  • Use edit_item for simple video placement, for example { "json": "{\"adds\":[{\"type\":\"video\",\"assetId\":\"...\",\"fromFrame\":0}]}" }.

Timeline overlap:

  • Error text: Overlap: updated item at ... would overlap existing item at ... on this track.
  • Do not force the write or delete the conflicting item silently.
  • Retry the edit_item transaction with an explicit available trackId, for example an update containing "trackId":"V2", or ask the user which layer should win.

Workspace path restrictions:

  • push_asset on the external MCP only accepts public http(s) URLs as filePath. It rejects local paths, workspace paths, and chat attachment paths.
  • For motion-graphic assets, pass the JSX source via create_motion_graphic_from_code({ code:"...", name, width, height, durationInFrames }). push_asset no longer accepts an inline code argument.
  • Copying local media into the workspace is not the fix for video/audio/image/GIF imports; use asset-import and import_media instead.
  • Use import_media action=create_session, then run the OpenChatCut media import helper once with the returned token for client-held files.

Browser video conversion failure:

  • Error text often includes Unable to convert video without dropping audio/video tracks or unknown_source_codec.
  • Rerun the OpenChatCut media import helper; it owns frontend-aligned conversion and will surface a user-actionable error if conversion is impossible.
  • Do not ask the user to re-import the same file through the editor UI as a workaround — the conversion path is the same, the error will repeat. Fix the source (re-encode locally with ffmpeg) or pick a different file.
  • After the replacement asset is uploaded/transcribed, delete the failed original asset if it is unused. The clean final media pool should look like a successful import, not a failed import plus a replacement.

Motion Graphic requirements:

  • push_asset(type:"motion-graphic") requires width, height, and duration or durationInFrames.
  • MG code must pass the OpenChatCut validator.
  • Root AbsoluteFill is not valid for generated MG code; use a scaling root div.
  • Avoid declaring a top-level local named scale inside MG code. The validator/runtime may already reserve that identifier; use a specific name such as uiScale.

Local dev Zero caveat:

  • When backend runs on a non-default port, use a matching Zero view-syncer configuration.
  • In this POC, backend 3010, editor 5177, and view-syncer 4850 are intentionally isolated from the older 3000/5173/4848 stack.

Timeline frame renderer caveat:

  • If view_timeline_frames fails for one frame, the project write path can still be healthy — retry with fewer frames or a different time before concluding anything.
  • Assets still on blob: placeholders (upload in flight) render as empty; wait for track_progress target=upload before treating a blank frame as a bug.
  • Do not report visual proof success unless the tool returns image content that visibly confirms the target frame.

Frequently asked questions

What does the Known Errors AI skill do?

Use when a OpenChatCut tool call fails or returns an unexpected shape.

Why use Known Errors on TypingMind?

Because you install it once and use it with any model. Known Errors 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 Known Errors in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/0xsline/OpenChatCut/tree/main/src/agent/skills/known-errors. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Known Errors?

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 Known Errors?

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

Is the Known Errors AI skill free?

Yes. It is published on GitHub by 0xsline under the AGPL-3.0 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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