# Meta:media

> Multimodal memory — ingest, embed, and search media (images, video, audio, files) with Gemini Embedding 2 + ChromaDB

- **Type:** Skill
- **Install:** `agentstack add skill-coco-research-coco-media-memory`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [coco-research](https://agentstack.voostack.com/s/coco-research)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [coco-research](https://github.com/coco-research)
- **Source:** https://github.com/coco-research/coco/tree/main/skills/media-memory

## Install

```sh
agentstack add skill-coco-research-coco-media-memory
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# /media-memory — Multimodal Memory System

You have access to a persistent multimodal memory system at `~/.claude/media-memory/`. It stores every piece of media (images, video, audio, files) with rich metadata and Gemini Embedding 2 vectors in ChromaDB.

## Directory Layout
```
~/.claude/media-memory/
  assets/          # stored media files
  chroma/          # ChromaDB vector store
  metadata.db      # SQLite structured metadata
  scripts/
    ingest.py      # ingestion + embedding
    search.py      # search with filters
    schema.py      # metadata models
```

## Commands

All commands run from `~/.claude/media-memory/` using `uv run`.

### Ingest (store + embed)
```bash
cd ~/.claude/media-memory && uv run scripts/ingest.py "" \
  --source "user|generated|url|ingested" \
  --description "Natural language description of the media" \
  --tags "tag1,tag2,tag3" \
  --type "image|video|audio|document|file" \
  --text "Extracted text or transcript content"
```

### Search (hybrid: semantic + metadata)
```bash
cd ~/.claude/media-memory && uv run scripts/search.py "search query" \
  --type image \
  --source user \
  --tags "architecture,diagram" \
  --from "2026-03-01" \
  --to "2026-03-28" \
  --limit 10 \
  --mode hybrid|semantic|metadata \
  --json
```

### Recent items
```bash
cd ~/.claude/media-memory && uv run scripts/search.py --recent --limit 10
```

### Stats
```bash
cd ~/.claude/media-memory && uv run scripts/search.py --stats
```

## Behavior Rules

### On Ingest (when user sends or generates media)
1. Copy the file to `assets/` via `ingest.py`
2. ALWAYS provide `--description` with a rich natural language description of the content
3. ALWAYS provide relevant `--tags` for semantic categorization
4. Set `--source` accurately: `user` (user sent it), `generated` (Claude/AI created it), `url` (downloaded), `ingested` (bulk import)
5. For screenshots: describe what's visible (UI elements, text, code, diagrams)
6. For documents: extract key text into `--text`
7. Report the result to the user: "Saved to media memory: {description}"

### On Search (when user asks about past media)
1. Use `--mode hybrid` by default (combines semantic + metadata)
2. Add `--type` filter when user specifies media kind
3. Add `--tags` filter when user mentions categories
4. Add date filters when user references timeframes ("last week", "this month")
5. Show results with descriptions and asset paths
6. Offer to open/display the asset if it's an image

### Proactive Recall
When a conversation topic overlaps with stored media:
1. Run a quick semantic search with the current topic
2. If relevant results found (similarity > 0.7), mention: "I found a related {type} in media memory: {description}"
3. Don't be noisy — only surface genuinely relevant assets

## Environment
- **No API key needed** — uses ChromaDB's built-in local embeddings (all-MiniLM-L6-v2 via onnxruntime)
- Everything runs locally, zero external calls
- ChromaDB: local persistent storage, cosine similarity
- Model cached at `~/.cache/chroma/onnx_models/` (downloaded once on first use)

## Metadata Schema
| Field | Type | Description |
|-------|------|-------------|
| id | string | Auto-generated: `{type}_{hash}_{stem}` |
| filename | string | Original filename |
| type | string | image, video, audio, document, file |
| timestamp | ISO 8601 | When ingested |
| source | string | user, generated, url, ingested |
| description | string | Natural language description |
| extracted_text | string | OCR / transcript / content |
| tags | JSON array | Semantic tags |
| original_path | string | Where it came from |
| asset_path | string | Path in assets/ |
| embedded | boolean | Whether vector is in ChromaDB |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [coco-research](https://github.com/coco-research)
- **Source:** [coco-research/coco](https://github.com/coco-research/coco)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-coco-research-coco-media-memory
- Seller: https://agentstack.voostack.com/s/coco-research
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.