🎬 Notion Movies Skill

This skill transforms a Notion movie database into an intelligent, searchable, and self-enriching system.

It combines:

Notion (data storage)
TMDB (movie metadata)
Ollama (embeddings)
Qdrant (semantic search)

🧠 Why this skill exists

This is not just about saving and searching movies.

It turns Notion into a semantic knowledge system for movies, allowing agents to:

understand meaning (not just keywords)
enrich data automatically
avoid duplicates
recommend content intelligently

🔥 What you can actually do with it

🎬 1. Smart movie ingestion

Instead of manually filling fields:

"add Interstellar"

The agent will:

create the page
fetch poster
fetch plot
fetch director
fetch genres
fetch actors, runtime, year
format everything properly

🧬 2. Automatic duplicate detection

Prevents:

Interstellar
Interstellar (2014)
Interestellar ❌

Agent behavior:

"This movie already exists. Enriching instead of duplicating."

Detection uses:

fuzzy matching (typos)
normalized titles (removes year)
semantic similarity (vector-based)

🔎 3. Human-like search (semantic + explainable)

Instead of exact filters, you can ask:

"movies about time travel"
"romantic movies in Europe"
"movies like Nolan"
"movies that make you think"

The system understands intent, not just words.

🧠 Explainable results

Each result includes:

similarity score
reasoning

Example:

{
  "title": "Interstellar",
  "score": 0.92,
  "explanation": "Similar because: genre similarity, plot similarity"
}

This makes results transparent and debuggable.

🤖 4. Personal recommendation engine

Example:

"recommend something like Fight Club"

→ returns semantically similar movies

🧠 5. Turn Notion into a knowledge system

From:

Static database

To:

Living, searchable, intelligent system

⚡ 6. Agent workflows (OpenClaw)

Example flow:

User → "I want something like Interstellar"

→ searchMovies
→ rank results
→ explain results
→ suggest options
→ user selects one
→ addMovie
→ enrichMovie
→ index vector

⚙️ Environment Variables

Required:

NOTION_KEY
TMDB_API_KEY

Optional:

QDRANT_URL (default: http://localhost:6333)
OLLAMA_URL (default: http://localhost:11434)

🧩 Actions

➕ addMovie

Creates a movie page (basic structure)

{
  "title": "Interstellar",
  "databaseQuery": "Movies"
}

Behavior:

checks duplicates before creating
prevents near-identical entries

🎬 enrichMovie

Enriches a movie with:

Poster (property + cover)
Plot (Notion blocks)
Director
Genres
Actors (top 5)
Year
Runtime
Emoji 🎬
Embedding (vector index)

{
  "title": "Interstellar",
  "databaseQuery": "Movies"
}

🔎 searchMovies

Semantic search with explainability

{
  "query": "movies about space exploration"
}

Returns:

top matches
similarity score
explanation

🧬 detectDuplicate

Detects duplicates using:

embeddings similarity
fuzzy matching
normalized titles

{
  "title": "Interstellar",
  "databaseQuery": "Movies"
}

🔁 syncDatabase

Sync Notion → Qdrant (vector DB)

{
  "databaseQuery": "Movies"
}

🧠 Embedding Strategy

Embeddings are built from enriched semantic content:

Title
Genres
Director
Plot
Themes (inferred)

✨ Example embedding input

Title: Interstellar
Genres: Science Fiction, Drama
Director: Christopher Nolan
Plot: A team travels through a wormhole in space...

Themes: space, time, futuristic

⚡ Optimization

important fields are repeated (weighting)
themes inferred automatically from plot
improves semantic recall and ranking

🧠 Data Enrichment Logic

🎥 Director

Source: TMDB /credits
Stored in: Director/es

🏷️ Genres

Source: TMDB /movie/{id}
Stored in: Género

🧠 Plot

Source: TMDB overview
Stored as Notion blocks:
- Callout
- Quote

🖼️ Poster

Source: TMDB
Stored as:
- Portada
- Notion cover

🎭 Actors

Source: TMDB /credits
Top 5 stored in Actores

📅 Year

Source: release date
Stored in Año

⏱️ Runtime

Source: TMDB details
Stored in Duración

🔐 Edge Case Handling

❌ Movie not found

{
  "error": "Movie not found in TMDB"
}

⚠️ Ambiguous results

{
  "error": "Multiple matches found",
  "options": [
    { "title": "Dune", "year": "2021" },
    { "title": "Dune", "year": "1984" }
  ]
}

🧠 Embedding fallback

If embedding fails:

returns zero vector
prevents system crashes
keeps pipeline running

🧬 Duplicate handling

System prevents:

typos
alternate titles
same movie with year variations

⚠️ Notion Schema Requirements

Property	Type
Nombre	title
Director/es	rich_text
Género	multi_select
Portada	files
Rating	select
Estado	status

🧩 Extended Schema (Recommended)

Property	Type	Description
Año	number	Release year
Actores	rich_text	Main cast
Duración	number	Runtime

🤖 Agent Guidelines

Agents should:

ALWAYS use databaseQuery
ALWAYS check duplicates before adding
prefer semantic search over filters
explain recommendations when possible
enrich instead of duplicating
only fill missing data
handle ambiguity before proceeding
gracefully handle API failures

⚡ Performance Notes

embeddings generated via Ollama (nomic-embed-text:v1.5)
vectors stored in Qdrant
each movie indexed as semantic document
payload includes metadata for explainability

🚀 Advanced Capabilities

Semantic search (embedding-based)
Explainable recommendations
Hybrid duplicate detection (semantic + fuzzy)
Automatic enrichment pipeline
Robust error handling
Agent-ready design (OpenClaw compatible)

notion-movies