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Davinci Autoedit Agent

Community
liuluhaixiu
davinci-autoedit-agent

End-to-end, approval-gated video editing workflow for arbitrary user-selected video, audio, and optional image media. Use when Codex must scan footage, review and classify material, develop a topic into a script, optionally generate TTS, create an edit blueprint, or build and audit a DaVinci Resolve timeline without assuming a genre, drive, date, camera, platform, or API provider.

Overview

Publisherliuluhaixiu
RepositoryDaVinci-AutoEdit-Agent
Skill namedavinci-autoedit-agent
Stars
493
Forks
61
Bundled files
13
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.

  • 13 bundled files

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

  • Open source

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

Installation

Install the Davinci Autoedit Agent 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/liuluhaixiu/DaVinci-AutoEdit-Agent/tree/main/skills/davinci-autoedit-agent
  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/liuluhaixiu/DaVinci-AutoEdit-Agent.git /tmp/DaVinci-AutoEdit-Agent
mkdir -p .claude/skills
cp -r /tmp/DaVinci-AutoEdit-Agent/skills/davinci-autoedit-agent .claude/skills/davinci-autoedit-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Davinci Autoedit Agent 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 Davinci Autoedit Agent 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 Davinci Autoedit Agent 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.

DaVinci AutoEdit Agent

Run a source-safe workflow from media intake to an audited edit. Use the bundled viral-video-writer for scripts and davinci-resolve-editor for Resolve operations.

Opening Message

Start by telling the user:

I can inspect your chosen media, build a review and tag map, develop the story and script, optionally prepare TTS, create a shot-by-shot edit blueprint, and then build and audit a DaVinci Resolve timeline. I will show you each creative artifact for approval before writing it or using it in the next stage. Your source files remain unchanged.

Then collect only missing information:

  1. Media path or paths. Accept any accessible video/audio directory or file.
  2. Topic, purpose, audience, platform, target duration, aspect ratio, and language. Do not impose a subject.
  3. Ask: "Do you have your own pacing rules, editing practices, references, or non-negotiables?" If yes, record them. If no, offer the best-practice profile in references/editorial-best-practices.md.
  4. Ask whether narration/TTS is wanted. Do not assume it.
  5. Ask whether Resolve timeline construction is wanted or only a blueprint.

Summarize the brief in chat and obtain approval before writing project-brief.json.

Approval Contract

Read-only inspection does not require repeated confirmation. Before creating or replacing an artifact, show a concise preview, list the intended path, and ask for explicit approval.

Use these gates:

  1. Project brief
  2. Scan scope and exclusions
  3. Material review and taxonomy
  4. Story direction and script
  5. TTS plan, only when enabled
  6. Edit blueprint
  7. Resolve project/timeline write
  8. Color, audio, render, or source-derivative operations
  9. Pickup-shot and missing-material report

Approval for one gate does not authorize later gates. Never overwrite a previous approved artifact; create a versioned file unless replacement was explicitly requested.

Configuration

Before API-backed work, run:

bash
python scripts/check_setup.py

If an API is missing, ask whether the user wants to configure it.

  • If yes, read references/configuration.md, explain the minimum variables, help create .env, test connectivity without printing secrets, and ask before the first billable request.
  • If no LLM API, continue with Codex-native visual inspection where available, or produce a manual review worksheet.
  • If no TTS API or the user declines configuration, set tts.enabled=false and remove TTS from the plan. Do not keep asking.
  • If Resolve MCP is absent, offer blueprint-only delivery or the documented Resolve Python fallback.

All endpoints, keys, models, voices, reference audio, media roots, music libraries, LUTs, and output paths are user configuration. Never ship private defaults.

Workflow

1. Create The Run Folder

After brief approval, create:

text
workspace/runs/<project-slug>/
  project-brief.json
  scan/
  review/
  script/
  tts/
  blueprint/
  resolve/

2. Scan Media

Preview the paths and extension policy, then run:

bash
python scripts/scan_media.py --input "<path>" --output "<run>/scan"

Pass --input repeatedly for multiple roots. Accept common video, audio, and image formats. Use FFprobe metadata when available. Do not filter by date or camera unless the user requests it.

Review media-manifest.json and confirm:

  • file counts and total duration by media type
  • unreadable or missing files
  • source groups/cameras inferred only as provisional labels
  • duplicate candidates
  • explicit exclusions

Obtain approval before analysis derivatives such as extracted frames.

After approval, a provider-neutral fixed-interval extractor is available:

bash
python scripts/extract_frames.py --manifest "<run>/scan/media-manifest.json" --output "<run>/review/frames"

3. Analyze And Review

For video, sample enough frames to represent scene changes and long takes. For audio, inspect duration, channels, loudness when tooling supports it, and transcribe only with permission. Treat images as selectable visual assets.

When the user approved an API-backed batch, run:

bash
python scripts/analyze_frames.py --frames-index "<run>/review/frames/frames-index.json" --output "<run>/review/frame-analysis.jsonl" --topic "<user topic>"

Use evidence-based labels: scene, people, action, dialogue, emotion, quality, continuity, source group, narrative use, and visible text. Never reject an entire camera family because derivative files or early samples look weak.

Produce a review preview with:

  • chronological inventory
  • strongest moments and why
  • weak/duplicate/technical-risk material
  • camera/source counts and usable-duration estimates
  • possible story beats
  • unanswered factual questions

After approval, write review/material-review.json and .md.

4. Write The Story

Invoke viral-video-writer. Give it the approved brief and material review, not imagined footage. Present:

  • one core idea
  • 2-3 story structures
  • intended emotional curve
  • narration policy
  • five title/hook options when relevant

After the user chooses a direction, draft the full script. Show it in chat and write only after approval.

5. Decide TTS

If narration is disabled, skip this stage and design around dialogue, natural sound, music, captions, or silence.

If enabled, confirm provider, endpoint, model, voice/reference audio, language, segmenting, pronunciation, and output directory. Preview tts-plan.json before any request. Generate only after approval:

bash
python scripts/generate_tts.py --script "<approved.json>" --output "<run>/tts"

Audit every output for duration, clipping, empty files, pronunciation, and segment order. Never bundle or publish a user's voice samples.

6. Build The Blueprint

Create a source-grounded JSON blueprint following references/blueprint-schema.md. Every clip must identify source path, source in/out, timeline in/out, purpose, audio policy, and confidence.

Apply the user's editing practice. If none was supplied, use references/editorial-best-practices.md.

Audit before presenting:

  • target duration and pacing
  • source range within media duration
  • no accidental adjacent repetition across section boundaries
  • meaningful source/camera diversity where available
  • dialogue/narration synchronization
  • still-image duration handling
  • music and LUT authorization

Write the blueprint only after approval.

Validate it before Resolve:

bash
python scripts/validate_blueprint.py "<run>/blueprint/edit-blueprint.json"

7. Build In Resolve

Invoke davinci-resolve-editor. Confirm Resolve is open and the current project/timeline identity. Prefer the registered MCP tools; configured files alone do not prove MCP availability.

Before writing, state the exact new project/timeline name and affected tracks. Create a new project or duplicated timeline for every substantial revision. Do not modify user audio, grades, BGM, or existing tracks without approval.

When MCP is unavailable but the official Python API is configured, use:

bash
python scripts/build_resolve_timeline.py "<run>/blueprint/edit-blueprint.json"

8. Audit Delivery

Read back the final Resolve state. Verify:

  • actual project and timeline names
  • expected item counts by track
  • timeline frame origin
  • gaps/overlaps and one-frame boundary errors
  • source paths and source range bounds
  • source/camera ratios and zero-use available groups
  • adjacent repeated source
  • LUT write and readback, when authorized
  • audio writes actually succeeded, when authorized
  • report belongs to the currently delivered timeline

A failed build report is not a delivery. Save the report under resolve/ and summarize unresolved manual work honestly.

9. Recommend Pickup Shots

After the edit audit, compare three sources of truth:

  1. The approved script, including every factual, emotional, and explanatory beat it asks the viewer to understand.
  2. The finished timeline, including the actual image and sound covering each beat.
  3. The complete reviewed media inventory, so an unused existing shot is not incorrectly labeled as missing.

Identify all necessary but absent material. A shot is necessary when its absence causes at least one of these problems:

  • a script claim has no visual or audible evidence
  • a person, object, place, process, or result is introduced without orientation
  • an action cannot be understood because setup, key step, reaction, or outcome is missing
  • continuity, geography, chronology, or screen direction becomes confusing
  • dialogue/narration is covered by visibly unrelated filler
  • a transition conceals a structural gap rather than serving the story
  • a required product, safety, legal, tutorial, or factual detail is not shown
  • the ending lacks the promised payoff or proof

Do not call every aesthetic opportunity "necessary." Separate the report into:

  • P0 Required: the cut is misleading, unclear, unsupported, or incomplete without it
  • P1 Strongly Recommended: comprehension or emotional payoff is materially weaker without it
  • P2 Optional Enhancement: texture, polish, rhythm, or alternate coverage

For each recommendation include:

  • related script paragraph/line and finished timeline timecode
  • exact missing information or story function
  • evidence that no adequate existing source covers it
  • concrete subject, action, framing, camera movement, duration, orientation, location, time-of-day, sound, and continuity requirements
  • whether original participants/location/props are required
  • safe and practical capture notes
  • an alternative using existing media, graphics, text, archival material, voiceover rewrite, or script deletion when reshooting is impossible
  • expected editorial placement and what it replaces

Use references/pickup-shot-report.md. Preview the findings in chat and obtain approval before writing:

text
review/pickup-shot-report.md
review/pickup-shot-report.json

If nothing necessary is missing, say so explicitly and list only genuinely useful optional enhancements. Never invent continuity details merely to make a pickup request sound precise.

Source Safety

  • Treat original media as immutable.
  • Write derivatives only under the approved run folder or staging directory.
  • Stop and inspect running FFmpeg/Python processes after interruptions.
  • Never delete original tracks after a partial audio bake.
  • Never expose API keys, private endpoints, personal paths, or voice samples.

References

  • Configuration: references/configuration.md
  • Best-practice edit profile: references/editorial-best-practices.md
  • Blueprint schema: references/blueprint-schema.md
  • Failure lessons and audits: references/resolve-pitfalls.md
  • Pickup-shot report: references/pickup-shot-report.md

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 Davinci Autoedit Agent AI skill do?

End-to-end, approval-gated video editing workflow for arbitrary user-selected video, audio, and optional image media. Use when Codex must scan footage, review and classify material, develop a topic into a script, optionally generate TTS, create an edit blueprint, or build and audit a DaVinci Resolve timeline without assuming a genre, drive, date, camera, platform, or API provider.

Why use Davinci Autoedit Agent on TypingMind?

Because you install it once and use it with any model. Davinci Autoedit Agent 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 Davinci Autoedit Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/liuluhaixiu/DaVinci-AutoEdit-Agent/tree/main/skills/davinci-autoedit-agent. 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 Davinci Autoedit Agent?

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 Davinci Autoedit Agent?

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

Is the Davinci Autoedit Agent AI skill free?

Yes. It is published on GitHub by liuluhaixiu 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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