Agent skill

Memory Lancedb Pro Openclaw

by LeoYeAI in LeoYeAI/openclaw-master-skills

Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart…

MITAuto-check: notesAI & LLM Engineering

Install Memory Lancedb Pro Openclaw

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill memory-lancedb-pro-openclaw -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills memory-lancedb-pro-openclaw --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/memory-lancedb-pro-openclaw .claude/skills/memory-lancedb-pro-openclaw && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
memory-lancedb-pro-openclaw
GitHub stars
2.2k
Token cost
~4.1k tokens
SKILL.md length
796 words
Files
2
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart…

  • Works in 5 steps: Vector search — semantic similarity via… → BM25 full-text search — keyword matching… → Score fusion — results merged with… → …
  • Tasks that involve Agent memory
  • SKILL.md covers Installation, Minimal Configuration…, Full Production Configuration and CLI Reference, plus 9 more sections
  • Calls bash, curl and npm; reaches github.com and raw.githubusercontent.com; needs OPENAI_API_KEY and JINA_API_KEY

What it does

Memory Lancedb Pro Openclaw is an agent skill from LeoYeAI/openclaw-master-skills. Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart auto-capture.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

It sits in AI & LLM Engineering, covering Agent memory and Retrieval-augmented generation. It works with Ollama and OpenAI. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve Agent memory
  • Tasks that involve Retrieval-augmented generation

Example prompts

  • “/memory-lancedb-pro-openclaw”

Requirements

  • Node.js
  • A credential in OPENAI_API_KEY
  • A credential in JINA_API_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Vector search — semantic similarity via embeddings (weight: vectorWeight, default 0.7)
  2. BM25 full-text search — keyword matching (weight: bm25Weight, default 0.3)
  3. Score fusion — results merged with weighted RRF (Reciprocal Rank Fusion)
  4. Cross-encoder rerank — top candidatePoolSize candidates reranked by a cross-encoder model
  5. Score filtering — results below hardMinScore are dropped

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • bash
    • curl
    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • raw.githubusercontent.com
    • api.openai.com

    Also links to:

    • ara.so
    • lancedb.com
    • npmjs.com
    • youtu.be
    • bilibili.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY
    • JINA_API_KEY
    • SILICONFLOW_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Memory Lancedb Pro Openclaw loads about 4.1k tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 796 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:513
    the shell that runs OpenClaw, or use a `.env` file loaded by your process manager. The `${VAR}` syntax in `openclaw.jso

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 796 words, ~4,087 tokens.

Download SKILL.mdSave it as .claude/skills/memory-lancedb-pro-openclaw/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
memory-lancedb-pro-openclaw
description
Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart auto-capture.
triggers
help me set up long-term memory for my OpenClaw agent, configure memory-lancedb-pro plugin, my agent keeps forgetting things between sessions, enable hybrid…

memory-lancedb-pro OpenClaw Plugin

Skill by ara.so — Daily 2026 Skills collection.

memory-lancedb-pro is a production-grade long-term memory plugin for OpenClaw agents. It stores preferences, decisions, and project context in a local LanceDB vector database and automatically recalls relevant memories before each agent reply. Key features: hybrid retrieval (vector + BM25 full-text), cross-encoder reranking, LLM-powered smart extraction (6 categories), Weibull decay-based forgetting, multi-scope isolation (agent/user/project), and a full management CLI.


Installation

bash
curl -fsSL https://raw.githubusercontent.com/CortexReach/toolbox/main/memory-lancedb-pro-setup/setup-memory.sh -o setup-memory.sh
bash setup-memory.sh

Flags:

bash
bash setup-memory.sh --dry-run       # Preview changes only
bash setup-memory.sh --beta          # Include pre-release versions
bash setup-memory.sh --uninstall     # Revert config and remove plugin
bash setup-memory.sh --selfcheck-only  # Health checks, no changes

The script handles fresh installs, upgrades from git-cloned versions, invalid config fields, broken CLI fallback, and provider presets (Jina, DashScope, SiliconFlow, OpenAI, Ollama).

Option B: OpenClaw CLI
bash
openclaw plugins install memory-lancedb-pro@beta
Option C: npm
bash
npm i memory-lancedb-pro@beta

Critical: When installing via npm, you must add the plugin's absolute install path to plugins.load.paths in openclaw.json. This is the most common setup issue.


Minimal Configuration (openclaw.json)

json
{
  "plugins": {
    "load": {
      "paths": ["/absolute/path/to/node_modules/memory-lancedb-pro"]
    },
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "embedding": {
            "provider": "openai-compatible",
            "apiKey": "${OPENAI_API_KEY}",
            "model": "text-embedding-3-small"
          },
          "autoCapture": true,
          "autoRecall": true,
          "smartExtraction": true,
          "extractMinMessages": 2,
          "extractMaxChars": 8000,
          "sessionMemory": { "enabled": false }
        }
      }
    }
  }
}

Why these defaults:

  • autoCapture + smartExtraction → agent learns from conversations automatically, no manual calls needed
  • autoRecall → memories injected before each reply
  • extractMinMessages: 2 → triggers in normal two-turn chats
  • sessionMemory.enabled: false → avoids polluting retrieval with session summaries early on

Full Production Configuration

json
{
  "plugins": {
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "embedding": {
            "provider": "openai-compatible",
            "apiKey": "${OPENAI_API_KEY}",
            "model": "text-embedding-3-small",
            "baseURL": "https://api.openai.com/v1"
          },
          "reranker": {
            "provider": "jina",
            "apiKey": "${JINA_API_KEY}",
            "model": "jina-reranker-v2-base-multilingual"
          },
          "extraction": {
            "provider": "openai-compatible",
            "apiKey": "${OPENAI_API_KEY}",
            "model": "gpt-4o-mini"
          },
          "autoCapture": true,
          "captureAssistant": false,
          "autoRecall": true,
          "smartExtraction": true,
          "extractMinMessages": 2,
          "extractMaxChars": 8000,
          "enableManagementTools": true,
          "retrieval": {
            "mode": "hybrid",
            "vectorWeight": 0.7,
            "bm25Weight": 0.3,
            "topK": 10
          },
          "rerank": {
            "enabled": true,
            "type": "cross-encoder",
            "candidatePoolSize": 12,
            "minScore": 0.6,
            "hardMinScore": 0.62
          },
          "decay": {
            "enabled": true,
            "model": "weibull",
            "halfLifeDays": 30
          },
          "sessionMemory": { "enabled": false },
          "scopes": {
            "agent": true,
            "user": true,
            "project": true
          }
        }
      }
    }
  }
}
Provider Options for Embedding
Providerprovider valueNotes
OpenAI / compatible"openai-compatible"Requires apiKey, optional baseURL
Jina"jina"Requires apiKey
Gemini"gemini"Requires apiKey
Ollama"ollama"Local, zero API cost, set baseURL
DashScope"dashscope"Requires apiKey
SiliconFlow"siliconflow"Requires apiKey, free reranker tier
Deployment Plans

Full Power (Jina + OpenAI):

json
{
  "embedding": { "provider": "jina", "apiKey": "${JINA_API_KEY}", "model": "jina-embeddings-v3" },
  "reranker": { "provider": "jina", "apiKey": "${JINA_API_KEY}", "model": "jina-reranker-v2-base-multilingual" },
  "extraction": { "provider": "openai-compatible", "apiKey": "${OPENAI_API_KEY}", "model": "gpt-4o-mini" }
}

Budget (SiliconFlow free reranker):

json
{
  "embedding": { "provider": "openai-compatible", "apiKey": "${OPENAI_API_KEY}", "model": "text-embedding-3-small" },
  "reranker": { "provider": "siliconflow", "apiKey": "${SILICONFLOW_API_KEY}", "model": "BAAI/bge-reranker-v2-m3" },
  "extraction": { "provider": "openai-compatible", "apiKey": "${OPENAI_API_KEY}", "model": "gpt-4o-mini" }
}

Fully Local (Ollama, zero API cost):

json
{
  "embedding": { "provider": "ollama", "baseURL": "http://localhost:11434", "model": "nomic-embed-text" },
  "extraction": { "provider": "ollama", "baseURL": "http://localhost:11434", "model": "llama3" }
}

CLI Reference

Validate config and restart after any changes:

bash
openclaw config validate
openclaw gateway restart
openclaw logs --follow --plain | grep "memory-lancedb-pro"

Expected startup log output:

memory-lancedb-pro: smart extraction enabled
memory-lancedb-pro@1.x.x: plugin registered
Memory Management CLI
bash
# Stats overview
openclaw memory-pro stats

# List memories (with optional scope/filter)
openclaw memory-pro list
openclaw memory-pro list --scope user --limit 20
openclaw memory-pro list --filter "typescript"

# Search memories
openclaw memory-pro search "coding preferences"
openclaw memory-pro search "database decisions" --scope project

# Delete a memory by ID
openclaw memory-pro forget <memory-id>

# Export memories (for backup or migration)
openclaw memory-pro export --scope global --output memories-backup.json
openclaw memory-pro export --scope user --output user-memories.json

# Import memories
openclaw memory-pro import --input memories-backup.json

# Upgrade schema (when upgrading plugin versions)
openclaw memory-pro upgrade --dry-run   # Preview first
openclaw memory-pro upgrade             # Run upgrade

# Plugin info
openclaw plugins info memory-lancedb-pro

MCP Tool API

The plugin exposes MCP tools to the agent. Core tools are always available; management tools require enableManagementTools: true in config.

Core Tools (always available)
memory_recall

Retrieve relevant memories for a query.

typescript
// Agent usage pattern
const results = await memory_recall({
  query: "user's preferred code style",
  scope: "user",        // "agent" | "user" | "project" | "global"
  topK: 5
});
memory_store

Manually store a memory.

typescript
await memory_store({
  content: "User prefers tabs over spaces, always wants error handling",
  category: "preference",   // "profile" | "preference" | "entity" | "event" | "case" | "pattern"
  scope: "user",
  tags: ["coding-style", "typescript"]
});
memory_forget

Delete a specific memory by ID.

typescript
await memory_forget({ id: "mem_abc123" });
memory_update

Update an existing memory.

typescript
await memory_update({
  id: "mem_abc123",
  content: "User now prefers 2-space indentation (changed from tabs on 2026-03-01)",
  category: "preference"
});
Management Tools (requires enableManagementTools: true)
memory_stats
typescript
const stats = await memory_stats({ scope: "global" });
// Returns: total count, category breakdown, decay stats, db size
memory_list
typescript
const list = await memory_list({ scope: "user", limit: 20, offset: 0 });
self_improvement_log

Log an agent learning event for meta-improvement tracking.

typescript
await self_improvement_log({
  event: "user corrected indentation preference",
  context: "User asked me to switch from tabs to spaces",
  improvement: "Updated coding-style preference memory"
});
self_improvement_extract_skill

Extract a reusable pattern from a conversation.

typescript
await self_improvement_extract_skill({
  conversation: "...",
  domain: "code-review",
  skillName: "typescript-strict-mode-setup"
});
self_improvement_review

Review and consolidate recent self-improvement logs.

typescript
await self_improvement_review({ days: 7 });

Smart Extraction: 6 Memory Categories

When smartExtraction: true, the LLM automatically classifies memories into:

CategoryWhat gets storedExample
profileUser identity, background"User is a senior TypeScript developer"
preferenceStyle, tool, workflow choices"Prefers functional programming patterns"
entityProjects, people, systems"Project 'Falcon' uses PostgreSQL + Redis"
eventDecisions made, things that happened"Chose Vite over webpack on 2026-02-15"
caseSolutions to specific problems"Fixed CORS by adding proxy in vite.config.ts"
patternRecurring behaviors, habits"Always asks for tests before implementation"

Hybrid Retrieval Internals

With retrieval.mode: "hybrid", every recall runs:

  1. Vector search — semantic similarity via embeddings (weight: vectorWeight, default 0.7)
  2. BM25 full-text search — keyword matching (weight: bm25Weight, default 0.3)
  3. Score fusion — results merged with weighted RRF (Reciprocal Rank Fusion)
  4. Cross-encoder rerank — top candidatePoolSize candidates reranked by a cross-encoder model
  5. Score filtering — results below hardMinScore are dropped
json
"retrieval": {
  "mode": "hybrid",
  "vectorWeight": 0.7,
  "bm25Weight": 0.3,
  "topK": 10
},
"rerank": {
  "enabled": true,
  "type": "cross-encoder",
  "candidatePoolSize": 12,
  "minScore": 0.6,
  "hardMinScore": 0.62
}

Retrieval mode options:

  • "vector" — pure semantic search only
  • "bm25" — pure keyword search only
  • "hybrid" — both fused (recommended)

Show full SKILL.md (310 more words)Show less

Multi-Scope Isolation

Scopes let you isolate memories by context. Enabling all three gives maximum flexibility:

json
"scopes": {
  "agent": true,    // Memories specific to this agent instance
  "user": true,     // Memories tied to a user identity
  "project": true   // Memories tied to a project/workspace
}

When recalling, specify scope to narrow results:

typescript
// Get only project-level memories
await memory_recall({ query: "database choices", scope: "project" });

// Get user preferences across all agents
await memory_recall({ query: "coding style", scope: "user" });

// Global recall across all scopes
await memory_recall({ query: "error handling patterns", scope: "global" });

Weibull Decay Model

Memories naturally fade over time. The decay model prevents stale memories from polluting retrieval.

json
"decay": {
  "enabled": true,
  "model": "weibull",
  "halfLifeDays": 30
}
  • Memories accessed frequently get their decay clock reset
  • Important, repeatedly-recalled memories effectively become permanent
  • Noise and one-off mentions fade naturally after ~30 days

Upgrading

From pre-v1.1.0
bash
# 1. Backup first — always
openclaw memory-pro export --scope global --output memories-backup-$(date +%Y%m%d).json

# 2. Preview schema changes
openclaw memory-pro upgrade --dry-run

# 3. Run the upgrade
openclaw memory-pro upgrade

# 4. Verify
openclaw memory-pro stats

See CHANGELOG-v1.1.0.md in the repo for behavior changes and upgrade rationale.


Troubleshooting

Plugin not loading
bash
# Check plugin is recognized
openclaw plugins info memory-lancedb-pro

# Validate config (catches JSON errors, unknown fields)
openclaw config validate

# Check logs for registration
openclaw logs --follow --plain | grep "memory-lancedb-pro"

Common causes:

  • Missing or relative plugins.load.paths (must be absolute when using npm install)
  • plugins.slots.memory not set to "memory-lancedb-pro"
  • Plugin not listed under plugins.entries
autoRecall not injecting memories

By default autoRecall is false in some versions — explicitly set it to true:

json
"autoRecall": true

Also confirm the plugin is bound to the memory slot, not just loaded.

Jiti cache issues after upgrade
bash
# Clear jiti transpile cache
rm -rf ~/.openclaw/.cache/jiti
openclaw gateway restart
Memories not being extracted from conversations
  • Check extractMinMessages — must be ≥ number of turns in the conversation (set to 2 for normal chats)
  • Check extractMaxChars — very long contexts may be truncated; increase to 12000 if needed
  • Verify extraction LLM config has a valid apiKey and reachable endpoint
  • Check logs: openclaw logs --follow --plain | grep "extraction"
Retrieval returns nothing or poor results
  1. Confirm retrieval.mode is "hybrid" not "bm25" alone (BM25 requires indexed content)
  2. Lower rerank.hardMinScore temporarily (try 0.4) to see if results exist but are being filtered
  3. Check embedding model is consistent between store and recall operations — changing models requires re-embedding
Environment variable not resolving

Ensure env vars are exported in the shell that runs OpenClaw, or use a .env file loaded by your process manager. The ${VAR} syntax in openclaw.json is resolved at startup.

bash
export OPENAI_API_KEY="sk-..."
export JINA_API_KEY="jina_..."
openclaw gateway restart

Telegram Bot Quick Config Import

If using OpenClaw's Telegram integration, send this to the bot to auto-configure:

Help me connect this memory plugin with the most user-friendly configuration:
https://github.com/CortexReach/memory-lancedb-pro

Requirements:
1. Set it as the only active memory plugin
2. Use Jina for embedding
3. Use Jina for reranker
4. Use gpt-4o-mini for the smart-extraction LLM
5. Enable autoCapture, autoRecall, smartExtraction
6. extractMinMessages=2
7. sessionMemory.enabled=false
8. captureAssistant=false
9. retrieval mode=hybrid, vectorWeight=0.7, bm25Weight=0.3
10. rerank=cross-encoder, candidatePoolSize=12, minScore=0.6, hardMinScore=0.62
11. Generate the final openclaw.json config directly, not just an explanation

Resources

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/memory-lancedb-pro-openclaw of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Memory Lancedb Pro Openclaw next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Memory Lancedb Pro Openclaw compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Install and Run Cogneetopoteretes/cognee32k—~1kAutomated safety check: NotesApache-2.0
Local RAGnigo81/nigo-skills133—~1.4kAutomated safety check: PassMIT
Agent Squad for TypeScript2FastLabs/agent-squad7.8k—~4.3kAutomated safety check: PassApache-2.0
Agent Frameworkjihadkhawaja/Egroo178—~1.9kAutomated safety check: PassApache-2.0

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Works with

Questions about Memory Lancedb Pro Openclaw

What does Memory Lancedb Pro Openclaw do?

Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart…. Memory Lancedb Pro Openclaw is an agent skill from LeoYeAI/openclaw-master-skills. Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart auto-capture.

When should I use Memory Lancedb Pro Openclaw?

Memory Lancedb Pro Openclaw fits situations like: tasks that involve Agent memory; tasks that involve Retrieval-augmented generation.

How do I install Memory Lancedb Pro Openclaw in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill memory-lancedb-pro-openclaw -a claude-code`. Or copy the skill folder (skills/memory-lancedb-pro-openclaw in LeoYeAI/openclaw-master-skills) into .claude/skills/memory-lancedb-pro-openclaw in your project. Claude Code loads it when a task matches its description.

How do I install Memory Lancedb Pro Openclaw in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill memory-lancedb-pro-openclaw -a codex`. Or copy the skill folder (skills/memory-lancedb-pro-openclaw in LeoYeAI/openclaw-master-skills) into .agents/skills/memory-lancedb-pro-openclaw in your project. Codex loads it when a task matches its description.

Can I use Memory Lancedb Pro Openclaw in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill memory-lancedb-pro-openclaw -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-lancedb-pro-openclaw, .gemini/skills/memory-lancedb-pro-openclaw, .github/skills/memory-lancedb-pro-openclaw and .opencode/skills/memory-lancedb-pro-openclaw in your project.

What does Memory Lancedb Pro Openclaw need to run?

Going by SKILL.md and its folder, Memory Lancedb Pro Openclaw needs the command-line tools its instructions call (bash, curl and npm) and credentials named OPENAI_API_KEY, JINA_API_KEY and SILICONFLOW_API_KEY. Our summary lists: Node.js; A credential in OPENAI_API_KEY; A credential in JINA_API_KEY.

Does Memory Lancedb Pro Openclaw access the network?

SKILL.md names 8 domains. In commands or code: github.com, raw.githubusercontent.com and api.openai.com; the agent is likely to contact these when it follows the instructions. As links in the text: ara.so, lancedb.com, npmjs.com, youtu.be and bilibili.com. This is read from the text; nothing was executed.

Is Memory Lancedb Pro Openclaw safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Memory Lancedb Pro Openclaw use?

Memory Lancedb Pro Openclaw is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Memory Lancedb Pro Openclaw use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Memory Lancedb Pro Openclaw?

Skills that share tags, products or a category with Memory Lancedb Pro Openclaw: LanceDB Memory Configuration Guide (CortexReach/memory-lancedb-pro-skill, 229 stars), Install and Run Cognee (topoteretes/cognee, 32k stars), Local RAG (nigo81/nigo-skills, 133 stars) and Agent Squad for TypeScript (2FastLabs/agent-squad, 7.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Memory Lancedb Pro Openclaw?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.