skills/ai-video-production-master/SKILL.md
Expert in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).
npx skillsauth add curiositech/windags-skills ai-video-production-masterInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Expert in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, style consistency, and motion graphics generation.
Script Analysis:
├── Content Type = Educational/Corporate/Documentary
│ ├── Budget < $50/month → Stock Footage Assembly (InVideo-style)
│ └── Budget > $50/month → Stock + T2V Hybrid
├── Content Type = Creative/Artistic/Abstract
│ ├── Timeline < 2 days → Sora/Runway T2V
│ └── Timeline > 2 days → Custom I2V with LoRA training
└── Content Type = Brand/Character-focused
├── Existing assets available → I2V animation pipeline
└── No assets → Commission artists → I2V pipeline
Quality vs. Cost Decision:
├── Professional deliverable needed
│ ├── Use Sora/Runway Gen-3 (premium T2V)
│ └── Commission custom artwork for I2V
├── Prototype/test content
│ ├── Use stock footage + free T2V models
│ └── Local ComfyUI I2V generation
└── High volume production
├── Cloud batch processing (Vast.ai)
└── Automated stock footage workflows
Error Recovery Tree:
├── Quality fails at T2V generation
│ ├── Switch to stock footage + motion graphics overlay
│ └── Fallback to I2V with commissioned artwork
├── Cloud GPU timeout/failure
│ ├── Retry with different provider (Vast.ai → RunPod)
│ └── Switch to local processing with extended timeline
└── Style consistency breaks
├── Retrain LoRA with more reference images
└── Use IPAdapter + consistent prompt structure
| Shot Count | Complexity | Budget | Recommendation | |------------|------------|--------|----------------| | 1-5 | Simple | Any | Local M4 Max | | 6-20 | Medium | <$10 | Stock footage | | 6-20 | High | >$10 | Cloud GPU batch | | 20+ | Any | Any | Cloud GPU required |
Symptoms: Inconsistent frame rates, stuttering motion, temporal artifacts Diagnosis: Mismatched FPS settings or insufficient motion guidance Fix: Set consistent 24fps pipeline, add motion strength controls, use higher guidance scale (7-9) Detection Rule: If temporal consistency score < 0.7 or frame delta > 0.3, apply motion stabilization
Symptoms: Character appearance changes between shots, style inconsistency Diagnosis: Insufficient reference conditioning or LoRA overfitting Fix: Retrain LoRA with 15-20 reference images, use IPAdapter for face consistency Detection Rule: If character similarity score < 0.8 between consecutive shots, halt and retrain
Symptoms: Cloud GPU bills exceed budget by >200%, slow iteration cycles Diagnosis: No cost monitoring or inefficient batch sizing Fix: Set hard limits in cloud scripts, optimize batch sizes, use spot instances Detection Rule: If cost-per-minute exceeds $0.50 or iteration time > 15min, switch to local processing
Symptoms: Lip sync drift, audio-visual timing mismatches Diagnosis: Variable generation times affecting audio alignment Fix: Generate all video first, then align audio in post with markers Detection Rule: If audio offset > 200ms from video markers, re-align with FFmpeg
Symptoms: Too many generation options, analysis paralysis, project stalls Diagnosis: Over-optimization without testing simple approaches first Fix: Always start with stock footage proof-of-concept, iterate to complexity Detection Rule: If project planning > 2 hours without generated content, default to stock footage
Input: 3-minute script about quantum computing basics
Decision Process:
Execution:
# Step 1: Generate shot list from script
python scripts/script_analyzer.py --script quantum_script.txt
# Output: 12 shots identified, 8 require stock footage, 4 need motion graphics
# Step 2: Source stock footage
python scripts/stock_video_generator.py --shots_file shots.json --style documentary
# Selected: Laboratory footage, abstract tech visuals, clean backgrounds
# Step 3: Generate motion graphics for complex concepts
python scripts/motion_graphics_generator.py --type data_viz --concept "quantum_states"
# Created: Animated diagrams for superposition, entanglement
# Step 4: Assembly and sync
python scripts/video_assembler.py --footage stock_clips/ --graphics motion/ --audio narration.wav
Expert vs. Novice:
Final output: 3:15 video, cost $0 (free stock), 45 minutes total production time
Video production complete when ALL conditions met:
This skill should NOT be used for:
physics-rendering-expert skillsound-engineer skill for mixing, mastering, sound designphysics-rendering-expert skillHandoff triggers:
physics-rendering-expertsound-engineerdata-ai
license: Apache-2.0 NOT for unrelated tasks outside this domain.
development
Use when designing caching strategies (cache-aside, write-through, write-behind), implementing distributed locks, building rate limiters, leaderboards, real-time streams (XADD/consumer groups), pub/sub, or tuning eviction policies. Triggers: thundering-herd on cache miss, dogpile on key expiry, Redlock vs SET-NX-PX choice, sliding-window rate limiter, hot-key on a single cluster slot, big-key blowup, MULTI/EXEC across slots, KEYS in production. NOT for Redis Cluster operations/admin (different domain), embedded KV (SQLite, leveldb), in-process LRU caches, or Memcached.
tools
Drawing the `'use client'` boundary correctly in React Server Components apps (Next.js App Router, RSC frameworks) — leaf-pushing, slot composition, serialization rules, and environment poisoning prevention. Grounded in react.dev and Next.js 16 docs.
development
Use when designing rate limiting for an API, choosing between token bucket / sliding window / leaky bucket / fixed window, implementing it in Redis, deciding edge (Cloudflare/Upstash) vs origin enforcement, sizing per-user vs per-IP vs per-endpoint quotas, returning the right 429 response with Retry-After, or fixing the boundary-burst bug in fixed-window limiters. Triggers: 429 too many requests, INCR + EXPIRE, ZADD + ZREMRANGEBYSCORE + ZCARD, X-RateLimit-Remaining header, Cloudflare WAF rate limiting rules, Upstash @upstash/ratelimit, leaky bucket shaping vs policing, distributed rate limiter consistency. NOT for DDoS mitigation specifically (different scale), CAPTCHA / bot management, full WAF design, or per-user quota billing.