# Seek And Analyze Video > Seek and analyze video content using Memories.ai Large Visual Memory Model for persistent video intelligence - Skill: `techwavedev/seek-and-analyze-video` (Agent Skill) - Install (CLI): `npx skillmds@latest add techwavedev/seek-and-analyze-video` - Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/seek-and-analyze-video/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: techwavedev (https://skillmd.com/u/techwavedev) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/techwavedev/seek-and-analyze-video --- ## When to Use Use this skill when the user wants to search for, import, or analyze video content from TikTok, YouTube, or Instagram, summarize meetings or lectures from recordings, build a searchable knowledge base from video content, or research social media trends and creators. # Seek and Analyze Video ## Description This skill enables AI agents to search, import, and analyze video content using Memories.ai's Large Visual Memory Model (LVMM). Unlike one-shot video analysis tools, it provides persistent video intelligence -- videos are indexed once and can be queried repeatedly across sessions. Supports social media import (TikTok, YouTube, Instagram), meeting summarization, knowledge base building, and cross-video Q&A via Memory Augmented Generation (MAG). ## Overview The skill wraps 21 API commands into workflow-oriented reference guides that agents load on demand. A routing table in SKILL.md maps user intent to the right workflow automatically. ## When to Use This Skill - Use when analyzing or asking questions about a video from a URL - Use when searching for videos on TikTok, YouTube, or Instagram by topic, hashtag, or creator - Use when summarizing meetings, lectures, or webinars from recordings - Use when building a searchable knowledge base from video content and text memories - Use when researching social media content trends, influencers, or viral patterns - Use when analyzing or describing images with AI vision ## How It Works ### Step 1: Intent Detection The agent reads the SKILL.md workflow router and matches the user's request to one of 6 intent categories. ### Step 2: Reference Loading The agent loads the appropriate reference file (e.g., video_qa.md for video questions, social_research.md for social media research). ### Step 3: Workflow Execution The agent follows the step-by-step workflow: upload/import -> wait for processing -> analyze/chat -> present results. ## Examples ### Example 1: Video Q&A ``` User: "What are the key arguments in this video? https://youtube.com/watch?v=abc123" Agent: uploads video -> waits for processing -> uses chat_video to ask questions -> presents structured summary ``` ### Example 2: Social Media Research ``` User: "What's trending on TikTok about sustainable fashion?" Agent: uses search_public to find trending videos -> imports top results -> analyzes content patterns ``` ### Example 3: Meeting Notes ``` User: "Summarize this meeting recording and extract action items" Agent: uploads recording -> waits -> gets transcript -> uses chat_video for structured summary with action items ``` ## Best Practices - Always wait for video processing to complete before querying - Use caption_video for quick analysis (no upload needed) - Use chat_video for deep, multi-turn analysis (requires upload) - Use search_audio to find specific moments or quotes in a video - Use memory_add to store important findings for later retrieval ## Common Pitfalls - **Problem:** Querying a video before processing completes **Solution:** Always use the `wait` command after upload before any analysis - **Problem:** Uploading a video when only a quick caption is needed **Solution:** Use `caption_video` for one-off analysis; only upload for repeated queries ## Limitations - Video processing takes 1-5 minutes depending on length - Free tier limited to 100 credits - Social media import requires public content - Audio search only works on processed videos ## Related Skills - Video analysis tools for one-shot analysis - Web search skills for non-video content research --- ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Cache data schemas, transformation rules, and query patterns. BM25 excels at finding specific column names, table references, and SQL patterns. ```bash # Check for prior data engineering context before starting python3 execution/memory_manager.py auto --query "data processing patterns and pipeline configurations for Seek And Analyze Video" ``` ### Storing Results After completing work, store data engineering decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "Data pipeline: ETL from PostgreSQL to Qdrant, 50K records/batch, incremental sync via updated_at" \ --type technical --project \ --tags seek-and-analyze-video data ``` ### Multi-Agent Collaboration Share data schema changes with backend and frontend agents so they update their models accordingly. ```bash python3 execution/cross_agent_context.py store \ --agent "" \ --action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \ --project ```