skill-depot: A Local-First Memory and Skill System for MCP-Compatible AI Agents

What skill-depot Does

skill-depot addresses the problem of AI agent skills and knowledge piling up across scattered directories. Instead of loading everything into context (wasting tokens) or loading nothing (forgetting learned material), it provides a retrieval system that stores agent knowledge as Markdown files and uses vector embeddings to semantically search and selectively load only what's relevant.

How It Works

Agents interact with skill-depot through three levels of detail:

• skill_search("query") returns search results with name, score, and snippet
• skill_preview("skill-name") returns a structured overview with headings and first sentence per section
• skill_read("skill-name") returns the full Markdown content

The skill_learn tool allows agents to create or append knowledge on the fly, returning actions like "created" or "appended" with tags merged.

Technical Implementation

• Embeddings: Uses local transformer model all-MiniLM-L6-v2 via ONNX (384-dim vectors, ~80MB one-time download)
• Storage: SQLite + sqlite-vec for vector search
• Fallback: BM25 term-frequency search when the model isn't available
• Protocol: MCP with 9 tools (search, preview, read, learn, save, update, delete, reindex, list)
• Format: Standard Markdown + YAML frontmatter (same format Claude Code and Codex use)

Setup and Use Case

Setup is simple: npx skill-depot init. The tool is designed for local-first, zero-config, MCP-native use with no API keys to manage, no server to run, and no framework lock-in. The tradeoff is a narrower scope—it doesn't do session management or automatic memory extraction (yet).

Comparison with Other Tools

• mem0: Good for managed memory layer with polished API, but has cloud dependency
• OpenViking: Full context database with session management, multi-type memory, and automatic extraction from conversations
• LangChain/LlamaIndex memory modules: Solid if already in those ecosystems

Future Considerations

The developer is considering adding:

• Memory types (distinguishing between skills, memories, and resources)
• Deduplication to detect near-duplicate entries
• TTL/expiration for temporary knowledge auto-cleanup
• Confidence scoring where memories reinforced across multiple sessions rank higher