mm

name	mm
description	Use this skill when the user wants to work with multimodal files (images, video, audio, PDFs, binary formats) — find, inspect, extract content, search, or count files using UNIX-style commands with multimodal powers.

mm Plugin

Fast, multimodal context for agents. Familiar UNIX CLI tools like find, grep, cat — with multimodal powers. Rust core for speed, Python for dev-ex, UNIX philosophy for composability.

mm enables agents to work with file types that LLMs can't natively read, including images, video, audio, PDFs, and other binary formats. Indexing is implicit — every command auto-builds a metadata index on first use.

Commands

Discovery

• mm find list --path <dir> — Find/list files with multimodal metadata (tree, tabular, schema, JSON)
• mm find list --path <dir> --tree — Hierarchical tree view with sizes
• mm find list --path <dir> --kind image — Filter by kind (image, video, audio, document, code, text)
• mm wc count --path <dir> --by_kind — Count files, bytes, lines, tokens by kind
• mm peek metadata --path <file> — Raw file metadata (dimensions, EXIF, codec, duration, mime, hash)

Content extraction

• mm cat extract --path <file> — Extract content (PDF text, image captions, video descriptions, audio transcripts)
• mm cat extract --path <file> --mode accurate — Full LLM pipeline (requires configured profile)
• mm cat extract --path <file> --lines 20 — First N lines (head)

Search

• mm grep search --pattern <query> --path <dir> — Text + semantic content search
• mm grep search --pattern <query> --path <dir> --semantic — Semantic (vector) search
• mm sql query --query "SELECT * FROM files WHERE kind='image'" — SQL on file metadata

Config

• mm config show — Show configuration and diagnostics
• mm profile list — List LLM provider profiles
• mm self version — Print mm version

Passthrough

• mm _ _ — Passthrough to mm CLI with full argument access

Usage Examples

• "Find all images in ~/Downloads and show their sizes"
• "Extract text from invoice.pdf"
• "Search for 'invoice' across all PDFs in ~/docs"
• "Count files by type in ~/data"
• "Get a caption for photo.jpg using the accurate LLM pipeline"
• "Run SQL queries on file metadata"

Installation

pip install mm-ctx

Alternative methods:

# with uv
uv pip install mm-ctx

# run directly without installing
uvx --from mm-ctx mm --help

# macOS / Linux shell installer
curl -LsSf https://vlm-run.github.io/mm/install/install.sh | sh

Examples

Find files in a directory

mm find ~/data --tree --depth 2
mm find ~/data --kind image --sort size --reverse --limit 20
mm find ~/data --format json

Extract content from files

mm cat document.pdf                    # PDF text (fast pipeline)
mm cat photo.jpg -m accurate           # LLM caption + tags + objects
mm cat video.mp4 -m accurate           # Keyframe mosaic → LLM description
mm cat audio.mp3 -m accurate           # Whisper transcript

Search across files

mm grep "invoice" ~/data/
mm grep "revenue forecast" ~/data/ -s  # semantic search
mm grep "TODO" ~/data/ --kind code

Inspect file metadata

mm peek photo.jpg                      # image dimensions, EXIF, hash
mm peek video.mp4                      # video resolution, duration, codecs
mm peek doc.pdf --full                 # include author, title, page count

Count files by kind

mm wc ~/data --by-kind
mm wc ~/data --by-kind --format json

SQL queries on indexed files

mm sql "SELECT kind, COUNT(*) as n FROM files GROUP BY kind ORDER BY n DESC" --dir ~/data
mm sql --list-tables

Key Features

• UNIX-style commands (find, grep, cat, peek, wc, sql, bench) with multimodal semantics
• Rust core for speed — metadata commands run in ~60ms on 700 files
• Python API with mm.Context for building VLM-ready prompts incrementally
• Auto-indexing on first use — no manual setup needed
• Supports images, video, audio, PDFs, DOCX, PPTX, and text files
• Two extraction modes: fast (default) and accurate (full LLM pipeline)
• Semantic (vector) search across file contents
• SQL queries on file metadata via SQLite
• OpenAI and Gemini message format output
• Optional audio transcription backends: MLX (Apple Silicon), ctranslate2 (GPU), OpenAI-compatible