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 Original source: 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.

# 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:

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 <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.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \
  --project <project>