Agent skill

Seek And Analyze Video

by LeoYeAI in LeoYeAI/openclaw-master-skills

Video intelligence and content analysis using Memories.ai LVMM.

MITAuto-check passedKnowledge Management

Install Seek And Analyze Video

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill seek-and-analyze-video -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills seek-and-analyze-video --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/seek-and-analyze-video .claude/skills/seek-and-analyze-video && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
seek-and-analyze-video
GitHub stars
2.2k
Token cost
~3.5k tokens
SKILL.md length
1,248 words
Files
5 (incl. references, assets)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Video intelligence and content analysis using Memories.ai LVMM.

  • Works in 3 steps: Current State → Goals → Video-Specific Context
  • Competitor content analysis
  • SKILL.md covers Before Starting, How This Skill Works, Core Workflows and Command Reference, plus 6 more sections
  • Runs Python scripts from its folder; calls claude and git; reaches github.com and youtube.com; needs MEMORIES_API_KEY

What it does

Seek And Analyze Video is an agent skill from LeoYeAI/openclaw-master-skills. Video intelligence and content analysis using Memories.ai LVMM. Discover videos on TikTok, YouTube, Instagram by topic or creator. Analyze video content, summarize meetings, build searchable knowledge bases across multiple videos. Use for video research, competitor content analysis, meeting notes, lecture summaries, or building video knowledge libraries.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `_meta.json`, `assets/example-workflow.py` and `references/api-commands.md`).

It sits in Knowledge Management, covering Knowledge bases and Meeting notes and agendas. It works with Instagram, TikTok and YouTube. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Competitor content analysis
  • Lecture summaries
  • Building video knowledge libraries

Example prompts

  • “/seek-and-analyze-video”

Requirements

  • Python 3
  • A credential in MEMORIES_API_KEY

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Current State
  2. Goals
  3. Video-Specific Context

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • claude
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • youtube.com

    Also links to:

    • memories.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • MEMORIES_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Seek And Analyze Video loads about 3.5k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 1,248 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,248 words, ~3,458 tokens.

Download SKILL.mdSave it as .claude/skills/seek-and-analyze-video/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
seek-and-analyze-video
description
Video intelligence and content analysis using Memories.ai LVMM. Discover videos on TikTok, YouTube, Instagram by topic or creator. Analyze video content, summarize meetings, build searchable knowledge bases across multiple videos. Use for video research, competitor content analysis, meeting notes, lecture summaries, or building video knowledge libraries.
license
MIT
metadata.version
1.0.0
metadata.author
Kenny Zheng
metadata.category
marketing-skill
metadata.updated
2026-03-09
triggers
analyze video, video content analysis, summarize video, meeting notes from video, search TikTok videos, search YouTube videos, video knowledge base…

Seek and Analyze Video

You are an expert in video intelligence and content analysis. Your goal is to help users discover, analyze, and build knowledge from video content across social platforms using Memories.ai's Large Visual Memory Model (LVMM).

Before Starting

Check for context first: If marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

API Setup Required: This skill requires a Memories.ai API key. Guide users to:

  1. Visit https://memories.ai to create an account
  2. Get API key from dashboard (free tier: 100 credits, Plus: $15/month for 5,000 credits)
  3. Set environment variable: export MEMORIES_API_KEY=your_key_here

Gather this context (ask if not provided):

1. Current State
  • What video content do they need to analyze?
  • What platforms are they researching? (YouTube, TikTok, Instagram, Vimeo)
  • Do they have existing video libraries or starting fresh?
2. Goals
  • What insights are they extracting? (summaries, action items, competitive analysis)
  • Do they need one-time analysis or persistent knowledge base?
  • Are they analyzing individual videos or building cross-video research?
3. Video-Specific Context
  • What topics, hashtags, or creators are they tracking?
  • What's their use case? (competitor research, content strategy, meeting notes, training materials)
  • Do they need organized namespaces for team collaboration?

How This Skill Works

This skill supports 5 primary modes:

Mode 1: Quick Video Analysis

When you need one-time video analysis without persistent storage.

  • Use caption_video for instant summaries
  • Best for: ad-hoc analysis, quick insights, testing content
Mode 2: Social Media Research

When discovering and analyzing videos across platforms.

  • Search by topic, hashtag, or creator
  • Import and analyze in bulk
  • Best for: competitor analysis, trend research, content inspiration
Mode 3: Knowledge Base Building

When creating searchable libraries from video content.

  • Index videos with semantic search
  • Query across multiple videos simultaneously
  • Best for: training materials, research repositories, content archives
Mode 4: Meeting & Lecture Notes

When extracting structured notes from recordings.

  • Generate transcripts with visual descriptions
  • Extract action items and key points
  • Best for: meeting summaries, educational content, presentations
Mode 5: Memory Management

When organizing text insights and cross-video knowledge.

  • Store notes with tags for retrieval
  • Search across videos and text memories
  • Best for: research notes, insights collection, knowledge management

Core Workflows

Workflow 1: Analyze a Video URL

When to use: User provides a YouTube, TikTok, Instagram, or Vimeo URL

Process:

  1. Validate URL format and platform support
  2. Choose analysis mode:
    • Quick analysis: caption_video(url) - instant summary, no storage
    • Persistent analysis: import_video(url) - index for future queries
  3. Extract key information (summary, transcript, action items)
  4. Generate structured output (see Output Artifacts)

Example:

python
# Quick analysis (no storage)
result = caption_video("https://youtube.com/watch?v=...")

# Persistent indexing (builds knowledge base)
video_id = import_video("https://youtube.com/watch?v=...")
summary = query_video(video_id, "Summarize the key points")
Workflow 2: Social Media Video Research

When to use: User wants to find and analyze videos by topic, hashtag, or creator

Process:

  1. Define search parameters:
    • Platform: tiktok, youtube, instagram
    • Query: topic, hashtag, or creator handle
    • Count: number of videos to analyze
  2. Execute search: search_social(platform, query, count)
  3. Import discovered videos for deep analysis
  4. Generate competitive insights or trend report

Example:

python
# Find competitor content
videos = search_social("tiktok", "#SaaSmarketing", count=20)

# Analyze top performers
for video in videos[:5]:
    import_video(video['url'])

# Cross-video analysis
insights = chat_personal("What content themes are working?")
Workflow 3: Build Video Knowledge Base

When to use: User needs searchable library across multiple videos

Process:

  1. Import videos with tags for organization
  2. Store supplementary text memories (notes, insights)
  3. Enable cross-video semantic search
  4. Query entire library for insights

Example:

python
# Import video library with tags
import_video(url1, tags=["product-demo", "Q1-2026"])
import_video(url2, tags=["product-demo", "Q2-2026"])

# Store text insights
create_memory("Key insight from demos...", tags=["product-demo"])

# Query across all tagged content
insights = chat_personal("Compare Q1 vs Q2 product demos")
Workflow 4: Extract Meeting Notes

When to use: User needs structured notes from recorded meetings or lectures

Process:

  1. Import meeting recording
  2. Request structured extraction:
    • Action items with owners
    • Key decisions made
    • Discussion topics
    • Timestamps for important moments
  3. Format as meeting minutes
  4. Store for future reference

Example:

python
video_id = import_video("meeting_recording.mp4")
notes = query_video(video_id, """
Extract:
1. Action items with owners
2. Key decisions
3. Discussion topics
4. Important timestamps
""")
Workflow 5: Competitor Content Analysis

When to use: Analyzing competitor video strategies across platforms

Process:

  1. Search for competitor content by creator handle
  2. Import their top-performing videos
  3. Analyze patterns:
    • Content themes and formats
    • Messaging strategies
    • Production quality
    • Engagement tactics
  4. Generate competitive intelligence report

Example:

python
# Find competitor videos
competitor_videos = search_social("youtube", "@competitor_handle", count=30)

# Import for analysis
for video in competitor_videos:
    import_video(video['url'], tags=["competitor-X"])

# Extract insights
analysis = chat_personal("Analyze competitor-X content strategy and gaps")

Command Reference

Video Operations
CommandPurposeStorage
caption_video(url)Quick video summaryNo
import_video(url, tags=[])Index video for queriesYes
query_video(video_id, question)Ask about specific video-
list_videos(tags=[])List indexed videos-
delete_video(video_id)Remove from library-
CommandPurpose
search_social(platform, query, count)Find videos by topic/creator
search_personal(query, filters={})Search your indexed videos

Platforms: tiktok, youtube, instagram

Memory Management
CommandPurpose
create_memory(text, tags=[])Store text insight
search_memories(query)Find stored memories
list_memories(tags=[])List all memories
delete_memory(memory_id)Remove memory
Cross-Content Queries
CommandPurpose
chat_personal(question)Query across ALL videos and memories
chat_video(video_id, question)Focus on specific video
Vision Tasks
CommandPurpose
caption_image(image_url)Describe image using AI vision
import_image(image_url, tags=[])Index image for queries
Show full SKILL.md (511 more words)Show less

Proactive Triggers

Surface these issues WITHOUT being asked when you notice them in context:

  • User requests video analysis without API key → Guide them to memories.ai setup
  • Repeated similar queries across videos → Suggest building knowledge base instead
  • Analyzing competitor content → Recommend systematic tracking with tags
  • Meeting recording shared → Offer structured note extraction
  • Multiple one-off analyses → Suggest import_video for persistent reference
  • Large video libraries without tags → Recommend tag organization strategy

Output Artifacts

When you ask for...You get...
"Analyze this video"Structured summary with key points, themes, action items, and timestamps
"Competitor content research"Competitive analysis report with content themes, gaps, and recommendations
"Meeting notes from recording"Meeting minutes with action items, decisions, discussion topics, and owners
"Video knowledge base"Searchable library with semantic search across videos and memories
"Social media video research"Platform research report with top videos, trends, and content insights

Communication

All output follows the structured communication standard:

  • Bottom line first — answer before explanation
  • What + Why + How — every finding has all three
  • Actions have owners and deadlines — no "we should consider"
  • Confidence tagging — 🟢 verified / 🟡 medium / 🔴 assumed

Example output format:

BOTTOM LINE: Competitor X focuses on product demos (60%) and customer stories (30%)

WHAT:
• 18/30 videos are product demos with detailed walkthroughs — 🟢 verified
• 9/30 videos are customer success stories with ROI metrics — 🟢 verified
• Average video length: 3:24 (demos), 2:15 (stories) — 🟢 verified
• Consistent posting: 2-3 videos/week on Tuesday/Thursday — 🟢 verified

WHY THIS MATTERS:
They're driving bottom-of-funnel conversions with proof over awareness content.
Your current 80% thought leadership leaves conversion gap.

HOW TO ACT:
1. Create 10 product demo videos → [Owner] → [2 weeks]
2. Record 5 customer case studies → [Owner] → [3 weeks]
3. Test demo video performance vs current content → [Owner] → [4 weeks]

YOUR DECISION:
Option A: Match their demo focus — higher conversion, lower reach
Option B: Hybrid approach (50% demos, 50% thought leadership) — balanced

Technical Details

Repository: https://github.com/kennyzheng-builds/seek-and-analyze-video

Requirements:

  • Python 3.8+
  • Memories.ai API key (free tier or $15/month Plus)
  • Environment variable: MEMORIES_API_KEY

Installation:

bash
# Via Claude Code
claude skill install kennyzheng-builds/seek-and-analyze-video

# Or manual
git clone https://github.com/kennyzheng-builds/seek-and-analyze-video.git
export MEMORIES_API_KEY=your_key_here

Pricing:

  • Free tier: 100 credits (testing and light use)
  • Plus: $15/month for 5,000 credits (power users)

Supported Platforms:

  • YouTube (all public videos)
  • TikTok (public videos)
  • Instagram (public videos and reels)
  • Vimeo (public videos)

Key Differentiators

vs ChatGPT/Gemini Video Analysis:

  • Persistent memory (query anytime, not just during upload)
  • Cross-video search (query 100s of videos simultaneously)
  • Social media discovery (find videos, don't just analyze provided URLs)
  • Knowledge base building (organize with tags, semantic search)

vs Manual Video Research:

  • 40x faster video analysis
  • Automatic transcript + visual description
  • Semantic search across libraries
  • Scalable to hundreds of videos

vs Traditional Video Tools:

  • AI-native queries (ask questions vs manual review)
  • Cross-platform support (TikTok, YouTube, Instagram unified)
  • Zero-dependency Python client (works across Claude Code, OpenClaw, HappyCapy)
  • Workflow automation (upload → analyze → store in one command)

Best Practices

Tagging Strategy
  • Use consistent tag naming (kebab-case recommended)
  • Tag by: content-type, date-range, platform, topic, campaign
  • Example: ["competitor-analysis", "Q1-2026", "tiktok", "product-demo"]
Credit Management
  • Quick analysis (caption_video): ~2 credits per video
  • Import + indexing (import_video): ~5 credits per video
  • Queries (chat_personal, query_video): ~1 credit per query
  • Plan accordingly based on tier (free: 100, Plus: 5,000/month)
Query Optimization
  • Be specific in questions (better results, same credits)
  • Use filtered searches when possible (faster, more relevant)
  • Batch similar queries (analyze pattern, then ask once)
Organization
  • Create namespace strategy for teams (use tags for isolation)
  • Archive old content (delete unused videos to reduce noise)
  • Document video IDs for important content (VI... identifiers)
  • social-media-analyzer: For quantitative social media metrics. Use this skill for qualitative video content analysis.
  • content-strategy: For planning content themes. Use this skill to research what's working in your niche.
  • competitor-alternatives: For competitive positioning. Use this skill for competitor content intelligence.
  • marketing-context: Provides audience and brand context. Use before running video research.
  • content-production: For creating content. Use this skill to research successful formats first.
  • campaign-analytics: For campaign performance data. Combine with this skill for qualitative video insights.

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references, assets) in skills/seek-and-analyze-video of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • assets/example-workflow.py
  • references/api-commands.md
  • references/use-cases.md

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Seek And Analyze Video next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Seek And Analyze Video compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Seek And Analyze Video this skillLeoYeAI/openclaw-master-skills2.2k—~3.5kAutomated safety check: PassMIT
Youtube FetcherJimmySadek/youtube-fetcher-to-markdown485—~3.1kAutomated safety check: PassMIT
Multi Platform Search Searchapigooseworks-ai/goose-skills1.2k1 repos~3.5kAutomated safety check: PassMIT
Company Braincoreyhaines31/makerskills851—~4.9kAutomated safety check: PassMIT
Channel To Kbcoleam00/cole-medin-knowledge-base137—~808Automated safety check: PassNone
Channel To Kb Supadatacoleam00/cole-medin-knowledge-base137—~854Automated safety check: PassNone

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Questions about Seek And Analyze Video

What does Seek And Analyze Video do?

Video intelligence and content analysis using Memories.ai LVMM. Seek And Analyze Video is an agent skill from LeoYeAI/openclaw-master-skills.ai LVMM.

When should I use Seek And Analyze Video?

Seek And Analyze Video fits situations like: competitor content analysis; lecture summaries; building video knowledge libraries.

How do I install Seek And Analyze Video in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill seek-and-analyze-video -a claude-code`. Or copy the skill folder (skills/seek-and-analyze-video in LeoYeAI/openclaw-master-skills) into .claude/skills/seek-and-analyze-video in your project. Claude Code loads it when a task matches its description.

How do I install Seek And Analyze Video in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill seek-and-analyze-video -a codex`. Or copy the skill folder (skills/seek-and-analyze-video in LeoYeAI/openclaw-master-skills) into .agents/skills/seek-and-analyze-video in your project. Codex loads it when a task matches its description.

Can I use Seek And Analyze Video in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill seek-and-analyze-video -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/seek-and-analyze-video, .gemini/skills/seek-and-analyze-video, .github/skills/seek-and-analyze-video and .opencode/skills/seek-and-analyze-video in your project.

What does Seek And Analyze Video need to run?

Going by SKILL.md and its folder, Seek And Analyze Video needs Python for the scripts in its folder, the command-line tools its instructions call (claude and git) and credentials named MEMORIES_API_KEY. Our summary lists: Python 3; A credential in MEMORIES_API_KEY.

Does Seek And Analyze Video access the network?

SKILL.md names 3 domains. In commands or code: github.com and youtube.com; the agent is likely to contact these when it follows the instructions. As links in the text: memories.ai. This is read from the text; nothing was executed.

Is Seek And Analyze Video safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Seek And Analyze Video use?

Seek And Analyze Video is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Seek And Analyze Video use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.2k tokens, read only when the agent opens those files.

What are the alternatives to Seek And Analyze Video?

Skills that share tags, products or a category with Seek And Analyze Video: Youtube Fetcher (JimmySadek/youtube-fetcher-to-markdown, 485 stars), Multi Platform Search Searchapi (gooseworks-ai/goose-skills, 1.2k stars), Company Brain (coreyhaines31/makerskills, 851 stars) and Channel To Kb (coleam00/cole-medin-knowledge-base, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Seek And Analyze Video?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.