openclaw/ai-video-remix

AI Video Remix Skill

Generate styled video compositions from a local ShotAI video library using natural language.

Prerequisites

See references/setup.md for full installation instructions, including:

• ShotAI download and setup
• ffmpeg installation
• yt-dlp installation (for auto music)
• Node.js dependencies

Quick Start

cd /path/to/ai-video-editor
cp .env.example .env    # fill in SHOTAI_URL, SHOTAI_TOKEN, and optionally AGENT_PROVIDER
npm install
npx tsx src/skill/cli.ts "帮我做一个旅行混剪"

Pipeline (8 steps)

1. Agent: parseIntent — LLM extracts theme, selects composition, optionally overrides music style
2. Agent: refineQueries — LLM rewrites per-slot search terms to match library content
3. ShotAI: pickShots — Semantic search per slot, scored by similarity+duration+mood, best shot selected
4. Music: resolveMusic — yt-dlp YouTube search+download, or local MP3 if --bgm provided
5. ffmpeg: extractClip — Each shot trimmed to independent .mp4 clip file
6. Agent: annotateClips — LLM assigns per-clip visual effect params (tone, dramatic, kenBurns, caption)
7. File Server — HTTP server serves clips to Remotion renderer
8. Remotion: render — Composition rendered to final MP4

CLI Usage

npx tsx src/skill/cli.ts "<request>" [options]

Options:
  --composition <id>   Override composition (skip LLM selection)
  --bgm <path>         Local MP3 path (skip YouTube search)
  --output <dir>       Output directory (default: ./output)
  --lang <zh|en>       Output language: zh Chinese (default) / en English
                       Affects: video title, per-clip captions & location labels, attribution line
  --probe              Scan library first, let LLM plan slots from actual content

Compositions

| ID | Label | Best For | |----|-------|----------| | CyberpunkCity | 赛博朋克夜景 | Neon city, night scenes, sci-fi | | TravelVlog | 旅行 Vlog | Multi-city travel with location cards | | MoodDriven | 情绪驱动混剪 | Fast/slow emotion cuts | | NatureWild | 自然野生动物 | BBC nature documentary style | | SwitzerlandScenic | 瑞士风光 | Alpine/scenic travel with captions | | SportsHighlight | 体育集锦 | ESPN-style with goal captions |

Modes

Standard mode (default): LLM picks composition + generates search queries from registry templates.

Probe mode (--probe): Scans library videos first (names, shot samples, mood/scene tags), then LLM generates custom slots tailored to what actually exists.

Choose probe mode when: library content is unknown, user wants "best of my library", or standard slots return low-quality shots.

Environment Variables

See references/config.md for all environment variables and LLM provider setup.

Troubleshooting & Quality Tuning

See references/tuning.md for solutions to:

• Clip boundary flicker / 1–2 frame flash at cuts
• Red flash artifact in CyberpunkCity (GlitchFlicker on short clips)
• Low-quality or off-topic shots
• Music download failures

Recommended .env defaults for best quality:

MIN_SCORE=0.5    # filter short/low-quality shots

Writing ShotAI Search Queries

ShotAI uses semantic search powered by AI-generated tags and embedding vectors. Query quality is the single biggest factor in shot relevance — invest time here.

Query construction rules

Always write full sentences or rich phrases, never bare keywords.

The search engine understands semantic similarity ("ocean" matches "sea", "waves", "shoreline"), so richer context produces better recall.

| Quality | Example | When to use | |---------|---------|-------------| | ⭐ Detailed description | "A white seagull with spread wings gliding smoothly over calm blue ocean water, golden sunset light reflecting on the waves" | Best precision — use for hero shots | | ⭐ Full sentence | "A seagull flying gracefully over the ocean at sunset" | Good balance of precision and recall | | Short phrase | "seagull flying over ocean" | Acceptable fallback | | Single keyword | "seagull" | Avoid — low precision, noisy results |

What to include in a query

Describe the visual content of the ideal shot across these dimensions:

• Subject: what/who is in frame (a lone hiker, city traffic at night, athlete celebrating)
• Action: what is happening (walking slowly through fog, speeding through intersection, jumping with arms raised)
• Environment: location, setting, time of day (rain-soaked Tokyo street, mountain meadow at golden hour, empty stadium under floodlights)
• Mood / atmosphere: emotional tone (melancholic, tense, euphoric, serene)
• Camera feel: implied movement or framing (wide establishing shot, tight close-up, slow pan, handheld shaky)

Not all dimensions are needed every time — include whichever are most distinctive for the shot you want.

The refineQueries step

When the agent runs refineQueries, it rewrites the composition's default slot queries to better match the user's actual library. Apply these principles:

1. Start from the slot's semantic intent — what emotional or narrative role does this shot play in the composition?
2. Incorporate any context from the user's request — location names, event names, specific subjects mentioned
3. Expand synonyms — if the slot says "water", try "river flowing through forest" or "lake reflecting mountains" based on what the library likely contains
4. Avoid negations — "not indoors" does not work; instead describe the positive version ("outdoor daylight scene")
5. One query per slot — make it specific rather than trying to cover multiple scenarios

Examples: slot query → refined query

Slot default: "city at night"
User request: "帮我做一个东京旅行混剪"
Refined:      "Neon-lit Tokyo street at night, pedestrians crossing under glowing signs, rain reflections on pavement"

Slot default: "nature landscape"
User request: "trip to Patagonia last month"
Refined:      "Dramatic Patagonia mountain landscape, snow-capped peaks under stormy clouds, vast open wilderness"

Slot default: "athlete in action"
User request: "basketball highlight from last game"
Refined:      "Basketball player driving to the hoop, explosive movement, crowd in background blurred"

Adding a New Composition

See references/composition-guide.md to add a new Remotion composition to the registry.

Safety and Fallback

• If SHOTAI_URL or SHOTAI_TOKEN is unset, display a warning: "ShotAI MCP server is not configured. Set SHOTAI_URL and SHOTAI_TOKEN in your .env file. Download ShotAI at https://www.shotai.io."
• If the ShotAI MCP server returns an error (connection refused, HTTP 4xx/5xx), display the error message and stop — do not fabricate shot results.
• Never fabricate video file paths, shot timestamps, or similarity scores.
• If music download fails (yt-dlp error or network unreachable), suggest using --bgm <local.mp3> to provide a local audio file instead.
• If Remotion render fails, display the error output and suggest checking Node.js version (18+) and that all clip files were extracted successfully.
• If the LLM provider is unreachable, fall back to heuristic mode: use composition default queries directly without refinement, and skip annotateClips (use composition default effect params).

License

MIT-0 — Free to use, modify, and redistribute. No attribution required. See https://spdx.org/licenses/MIT-0.html