Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/aaronnat23/disp8ch/memory-lancedbnpx skills add aaronnat23/disp8ch --skill memory-lancedbgit clone --depth 1 https://github.com/aaronnat23/disp8chWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aaronnat23/disp8ch/memory-lancedb)<a href="https://agentmods.dev/skills/aaronnat23/disp8ch/memory-lancedb"><img src="https://agentmods.dev/badge/skills/aaronnat23/disp8ch/memory-lancedb.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00219 |
| Opus 5 | $0.00000 | $0.00110 |
| Sonnet 5 | $0.00000 | $0.00044 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
memory-lancedb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Memory LanceDB
LanceDB-backed vector memory for high-volume embedding and retrieval workloads.
- LanceDB stores embeddings as Lance columnar format on disk — significantly faster for large collections (>100k vectors) than sqlite-vec.
- Use this backend when the default sqlite-vec backend shows degraded search performance under heavy indexing load.
- All standard memory operations (
memory_search,memory_store,memory_get) work identically — the backend switch is transparent. - Configure
dbPathin the extension settings to point to the desired LanceDB directory (defaults todata/lancedb/). - For auto-capture mode: the agent automatically stores conversation context chunks without explicit
memory_storecalls. - For auto-recall mode: relevant memories are automatically injected into context at session start without explicit
memory_searchcalls. - When switching from sqlite-vec to LanceDB: run a memory backfill to migrate existing embeddings to the new backend.
- Monitor embedding batch health via
/api/memory?action=embedding-statusto confirm the LanceDB backend is active.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 13 lines · 0 tokens per session scan A 6459d95eaa7e
memory-lancedb is a skill published in the GitHub repository aaronnat23/disp8ch (99 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 219 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
memory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous…
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
pinecone-research
Agent RAG and long-term memory with Pinecone.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.