AI Editing in Premiere and DaVinci

Set up a Python environment on Windows and run a provided automation script.

Use PowerShell to navigate project folders and pass input arguments to a script.

Create and activate a Python virtual environment and install dependencies from a requirements file.

Edit and validate JSON or YAML configuration values without altering the required schema.

Give an AI coding assistant a precise specification for generating or modifying a small automation script.

Use an error traceback and log output to ask an AI assistant for a targeted code fix.

Create and restore a local Git checkpoint before accepting AI-generated code changes.

Store API keys in environment variables and exclude secrets from version control.

Locate the Media, Cut, Edit, Fairlight, Color, and Deliver pages and choose the appropriate page for each stage of a reel workflow.

Configure a Resolve project and timeline from a selected vertical or horizontal output profile.

Organize footage, audio, graphics, and exports in bins using a reusable project structure.

Generate and use proxy or optimized media suitable for a Windows computer with 16 GB RAM and a 4 GB GPU.

Build a reusable DaVinci Resolve reel template containing bins, tracks, titles, and export settings.

Distinguish the roles of a job manifest, SRT file, FCPXML timeline, and Resolve automation script.

Connect a Python script to the DaVinci Resolve Studio scripting interface and confirm basic control.

Choose and apply the appropriate blade, ripple, roll, or slip edit for a specified timing problem.

Install FFmpeg and verify that FFmpeg and ffprobe run from PowerShell.

Scan an input folder and create a manifest containing media paths and file types.

Extract duration, resolution, frame-rate, and audio metadata from media with ffprobe.

Transcode unsupported or variable-frame-rate media into an automation-friendly intermediate file.

Split a long video into overlapping analysis chunks while preserving source timestamps.

Generate a timestamped transcript with a resource-efficient local speech-to-text model.

Detect scene boundaries and export candidate shot ranges from a video.

Create a multimodal content summary from transcript evidence, representative frames, and media metadata using budgeted AI.

Distinguish evergreen audience-retention principles from temporary platform trends.

Gather date-stamped, cited guidance from official Instagram and YouTube sources using a research assistant.

Retrieve permitted trend or keyword data from a configured API or exported report.

Convert current platform evidence into a structured trend brief with an expiry date.

Build an Instagram Reels profile containing current format, safe-area, pacing, caption, and hook rules.

Build a YouTube Shorts profile containing current format, safe-area, pacing, caption, and hook rules.

Build a YouTube long-form profile containing intro, chapter, re-hook, pacing, and call-to-action rules.

Filter a trend recommendation for audience fit, content relevance, and evidential credibility.

Segment a long-form transcript and scene map into coherent chapters.

Segment a chapter or short-form input into coherent candidate moments.

Design a highlight-scoring rubric covering relevance, clarity, novelty, emotion, and payoff.

Score candidate moments with AI against the user’s instructions and selected output profile.

Combine dialogue, visual, and audio evidence into a configurable candidate score.

Reject candidate moments that lack necessary context or contain unsupported claims.

Apply duration and diversity constraints to select non-repetitive moments within and across chapters.

Produce a ranked highlight report containing source timecodes and a rationale for each score.

Convert natural-language editing instructions into a structured edit brief.

Generate hook options grounded in the source footage and current platform trend brief.

Evaluate AI-generated hooks for specificity, curiosity, credibility, and fit with the reel’s payoff.

Verify factual claims in an AI-generated script and remove or replace unsupported content.

Select a hook and arrange chosen material into a short-form setup, value sequence, and payoff.

Arrange a long-form outline into an introduction, ordered sections, re-hooks, transitions, and conclusion.

Generate context-appropriate B-roll, meme, and pattern-interrupt cues for an edit plan.

Create a machine-readable edit-decision manifest containing source ranges, sequence order, text, and asset cues.

Map source in-and-out points to sequence positions without introducing timecode drift.

Generate a valid FCPXML timeline from an edit-decision manifest.

Import a generated timeline into Resolve and verify media links, clip order, and duration.

Automate timeline naming, track creation, and bin placement through the Resolve scripting interface.

Remove silence and tighten speech automatically while preserving sentence boundaries.

Use Smart Reframe to convert landscape footage into a vertical composition and correct tracking errors.

Insert B-roll, meme, title, and caption placeholders from an edit-decision manifest.

Apply reusable title and transition settings and flag unsupported effects for review.

Generate a timed SRT file from the approved transcript and edit-decision manifest.

Correct AI transcription errors, timing, punctuation, and line breaks against the spoken audio.

Format captions for fast mobile reading using appropriate size, contrast, placement, and line length.

Keep essential captions and graphics inside platform-safe areas.

Use AI Animated Subtitles to create selective word-emphasis caption animation.

Use Voice Isolation to improve dialogue while avoiding unnatural processing artifacts.

Use AI Audio Assistant to create a first-pass mix and correct its dialogue and music levels.

Choose copyright-safe music that supports the reel’s tone and pacing.

Design a versioned configuration schema for platform, content type, editing style, budget, and safety settings.

Build an adapter that can create long-form edits or short extracts from talking-head and podcast footage.

Build an analysis and editing adapter for a folder of mixed video clips.

Build an analysis and editing adapter for gaming or screen-recording footage.

Design a meme and media-asset database schema containing file paths, media types, content tags, tone, rights, and usage constraints.

Ingest and index a meme and media-asset library with searchable metadata and semantic embeddings.

Retrieve and rank database assets for a scene according to context, tone, audience, platform, and user instructions.

Insert a selected database asset at a planned cue and record its provenance in the job report.

Run automated checks for missing media, clip overlaps, black frames, subtitle overflow, and audio clipping.

Evaluate a generated short-form or long-form edit against a pacing, continuity, repetition, and context-preservation rubric.

Evaluate AI-generated or stock assets for relevance, visual defects, usage rights, and disclosure needs.

Run a pre-export quality check for pacing, spelling, safe areas, audio, color, and asset rights.

Check an exported reel on a phone for compression, caption readability, framing, and audio playback.

Implement logs, retries, and checkpoint-based resume for a failed processing job.

Enforce per-job API token and cost caps and produce a spending report.

Redact or delete sensitive files and transcripts according to a user-selected privacy setting.

Launch the pipeline with one command containing an input folder, instruction prompt, and output profile.

Review ranked clips and the edit plan at an approval gate and request a targeted regeneration.

Export a high-quality file using settings selected from a short-form or long-form output profile.

Create two hook variants from one finished reel without rebuilding the full edit.

Use retention and engagement data from a posted reel or sample analytics dashboard to choose one testable change for the next version.

Update scoring weights and prompt rules from performance results without overwriting the baseline profile.

Run the system on a prepared input and diagnose one automation failure.

Process a prepared long-form source video from written instructions to an editable, exported video.