How do I install the Local Memory Search skill?

Run npx skillmds@latest add blackbasilisk/local-memory-search in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Local Memory Search free to use?

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Who published Local Memory Search?

blackbasilisk (@blackbasilisk) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Local Memory Search by blackbasilisk

Name: Local Memory Search
Author: blackbasilisk

Local Memory Search

Local, offline semantic search over OpenClaw memory files (MEMORY.md and memory/*.md) using Python embeddings + FAISS (no online LLM/API). Use when Dave asks for "memory search" without cloud billing, wants semantic recall across notes, or wants to share a reusable local-memory semantic search skill.

blackbasilisk 89e0858 4 files · 8.6 KB Updated Sep 17, 2026

File contents

Local Memory Search (offline)

This skill adds a local semantic search workflow for OpenClaw memory files:

MEMORY.md
memory/*.md

It builds a local vector index (FAISS) using a small sentence-transformers embedding model.

Requirements

Python 3.10+ in PATH (python --version)

Choose your backend (user choice)

A) Light (recommended): `lsa` (TF‑IDF + SVD)

No neural model
No HuggingFace downloads
Good “semantic-ish” matching (better than plain keyword search)

cd $env:USERPROFILE\.openclaw\workspace\skills\local-memory-search
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -U pip
.\.venv\Scripts\python.exe -m pip install -r .\scripts\requirements-lsa.txt

B) Heavy (best semantic): `sentence-transformers` (Torch)

True neural embeddings
Bigger install (torch) + model download

cd $env:USERPROFILE\.openclaw\workspace\skills\local-memory-search
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -U pip
.\.venv\Scripts\python.exe -m pip install -r .\scripts\requirements-sentence-transformers.txt

Build / refresh the index

Default (light LSA backend):

.\.venv\Scripts\python.exe .\scripts\index_memory.py --workspace "$env:USERPROFILE\.openclaw\workspace" --backend lsa

Heavy backend example:

.\.venv\Scripts\python.exe .\scripts\index_memory.py --workspace "$env:USERPROFILE\.openclaw\workspace" --backend sentence-transformers --model "sentence-transformers/all-MiniLM-L6-v2"

Default workflow: jump + quote (recommended)

This matches the recommended operational pattern:

semantic jump to the best chunk
open the source file
print exact lines (with a little context)

# Minimal output (default): prints just the best-matching lines
.\.venv\Scripts\python.exe .\scripts\jump_memory.py --query "o365 timezone config" --top 1

# If you want provenance:
.\.venv\Scripts\python.exe .\scripts\jump_memory.py --query "o365 timezone config" --top 1 --show-source --show-line-numbers --context 2

Tip: for cleaner quotes, re-index with default overlap 0 (the default).

Search (semantic only)

.\.venv\Scripts\python.exe .\scripts\search_memory.py --query "o365 timezone config" --top 5

If your index was built with a different backend/model, search_memory.py will automatically use the index metadata. You can override:

.\.venv\Scripts\python.exe .\scripts\search_memory.py --backend fastembed --model "BAAI/bge-small-en-v1.5" --query "..."

Notes

Index is stored under: ~/.openclaw/credentials/local-memory-search/
Re-run indexing after you edit memory files.
This is a local alternative to OpenClaw's built-in memory_search tool.

---name: local-memory-searchdescription: Local, offline semantic search over OpenClaw memory files (MEMORY.md and memory/*.md) using Python embeddings + FAISS (no online LLM/API). Use when Dave asks for "memory search" without cloud billing, wants semantic recall across notes, or wants to share a reusable local-memory semantic search skill.---# Local Memory Search (offline)This skill adds a **local semantic search** workflow for OpenClaw memory files:- `MEMORY.md`- `memory/*.md`It builds a local vector index (FAISS) using a small sentence-transformers embedding model.## Requirements- Python 3.10+ in PATH (`python --version`)## Choose your backend (user choice)### A) Light (recommended): `lsa` (TF‑IDF + SVD)- No neural model- No HuggingFace downloads- Good “semantic-ish” matching (better than plain keyword search)```powershellcd $env:USERPROFILE\.openclaw\workspace\skills\local-memory-searchpython -m venv .venv.\.venv\Scripts\python.exe -m pip install -U pip.\.venv\Scripts\python.exe -m pip install -r .\scripts\requirements-lsa.txt```### B) Heavy (best semantic): `sentence-transformers` (Torch)- True neural embeddings- Bigger install (torch) + model download```powershellcd $env:USERPROFILE\.openclaw\workspace\skills\local-memory-searchpython -m venv .venv.\.venv\Scripts\python.exe -m pip install -U pip.\.venv\Scripts\python.exe -m pip install -r .\scripts\requirements-sentence-transformers.txt```## Build / refresh the indexDefault (light LSA backend):```powershell.\.venv\Scripts\python.exe .\scripts\index_memory.py --workspace "$env:USERPROFILE\.openclaw\workspace" --backend lsa```Heavy backend example:```powershell.\.venv\Scripts\python.exe .\scripts\index_memory.py --workspace "$env:USERPROFILE\.openclaw\workspace" --backend sentence-transformers --model "sentence-transformers/all-MiniLM-L6-v2"```## Default workflow: jump + quote (recommended)This matches the recommended operational pattern:1) semantic jump to the best chunk2) open the source file3) print exact lines (with a little context)```powershell# Minimal output (default): prints just the best-matching lines.\.venv\Scripts\python.exe .\scripts\jump_memory.py --query "o365 timezone config" --top 1# If you want provenance:.\.venv\Scripts\python.exe .\scripts\jump_memory.py --query "o365 timezone config" --top 1 --show-source --show-line-numbers --context 2Tip: for cleaner quotes, re-index with default overlap 0 (the default).```## Search (semantic only)```powershell.\.venv\Scripts\python.exe .\scripts\search_memory.py --query "o365 timezone config" --top 5```If your index was built with a different backend/model, `search_memory.py` will automatically use the index metadata. You can override:```powershell.\.venv\Scripts\python.exe .\scripts\search_memory.py --backend fastembed --model "BAAI/bge-small-en-v1.5" --query "..."```## Notes- Index is stored under: `~/.openclaw/credentials/local-memory-search/`- Re-run indexing after you edit memory files.- This is a local alternative to OpenClaw's built-in `memory_search` tool.

Frequently asked questions