Claude Code Skill for memory-lancedb-pro — production-grade long-term memory plugin for OpenClaw AI agents.

This skill gives Claude Code deep, accurate knowledge of every feature in memory-lancedb-pro (v1.1.0-beta.8): installation, optimal configuration, Smart Extraction, hybrid retrieval, Weibull decay lifecycle, multi-scope isolation, self-improvement governance, and all MCP tools.

When installed, Claude Code can:

• Guide you through a 7-step optimal configuration workflow — just say "help me enable the best config"
• Present 4 deployment plans (Full Power / Budget / Simple / Fully Local) with provider links and tradeoffs
• Install, configure, and verify the plugin using openclaw plugins install or git clone
• Set up Ollama for fully local, zero-API-cost deployment
• Configure every feature: Smart Extraction, hybrid retrieval, reranking, multi-scope, Weibull decay, session memory, self-improvement governance
• Use all 9 MCP tools correctly: memory_recall, memory_store, memory_forget, memory_update, memory_stats, memory_list, self_improvement_log, self_improvement_extract_skill, self_improvement_review
• Avoid common pitfalls — workspace plugin enablement, autoRecall default-false, jiti cache, env vars, scope isolation, etc.

For Claude Code users:

• Claude Code CLI installed
• memory-lancedb-pro plugin configured as an MCP server

For OpenClaw users:

• OpenClaw gateway running
• memory-lancedb-pro plugin installed via openclaw plugins install memory-lancedb-pro@beta

Note: This is a skill (knowledge file for AI agents), not a plugin. Skills are installed by placing them in your skills directory — there is no openclaw skills install command.

Option A — clone this repo (recommended):

For Claude Code users:

git clone https://github.com/CortexReach/memory-lancedb-pro-skill.git ~/.claude/skills/memory-lancedb-pro

For OpenClaw users:

git clone https://github.com/CortexReach/memory-lancedb-pro-skill.git ~/.openclaw/workspace/skills/memory-lancedb-pro-skill

Option B — download ZIP from GitHub:

1. Click Code → Download ZIP on the repository page
2. Extract and place in your skills directory:

# Claude Code
unzip memory-lancedb-pro-skill-main.zip
mv memory-lancedb-pro-skill-main ~/.claude/skills/memory-lancedb-pro

# OpenClaw
unzip memory-lancedb-pro-skill-main.zip
mv memory-lancedb-pro-skill-main ~/.openclaw/workspace/skills/memory-lancedb-pro-skill

Verify the skill is loaded:

# Claude Code: the skill loads automatically based on trigger conditions
# To test: ask Claude Code "help me configure memory-lancedb-pro"

# OpenClaw: check skill discovery
openclaw skills list

memory-lancedb-pro/
├── SKILL.md                      # Main skill file (loaded into context automatically)
└── references/
    └── full-reference.md         # Deep technical reference (loaded on demand)

Claude Code loads this skill automatically when you mention:

• memory-lancedb-pro, memory pro, lancedb pro
• help me enable the best config / apply optimal configuration
• memory_recall, memory_store, memory_forget, memory_update
• Smart Extraction, autoCapture, autoRecall
• hybrid retrieval, reranker, BM25, Weibull decay
• self_improvement_log, LEARNINGS.md, ERRORS.md

• 3 installation methods: openclaw plugins install, git clone with manual path, existing deployment migration
• Plugin enablement rules: plugins.allow, plugins.entries.<id>.enabled, plugins.slots.memory
• Workspace plugin gotchas (disabled by default, requires explicit allow)
• Custom path env vars: OPENCLAW_HOME, OPENCLAW_CONFIG_PATH, OPENCLAW_STATE_DIR
• Post-installation smoke test checklist

When you say "help me enable the best config", Claude will:

1. Present 4 deployment plans with provider links
2. Ask about your existing API keys and config location
3. Find and read your current openclaw.json
4. Build a merged config block for your chosen plan
5. Apply it with the correct template (Method 1 vs Method 2)
6. Validate and restart the gateway
7. Run a full smoke test

Each plan includes: API key acquisition links, cost notes, RAM requirements (Plan D), and tradeoff explanations.

• 6-category LLM-powered classification: Profile → fact, Preferences → preference, Entities → entity, Events → decision, Cases → fact, Patterns → other
• L0/L1/L2 layered storage (Abstract / Overview / Full Content)
• Two-stage deduplication: vector pre-filter (≥ 0.7) + LLM decision (CREATE | MERGE | SKIP | SUPPORT | CONTEXTUALIZE | CONTRADICT)
• Config: smartExtraction, extractMinMessages, extractMaxChars, llm.*

• Fusion: (vectorScore × 0.7) + (bm25Score × 0.3) via RRF
• Pipeline: RRF → Cross-Encoder Rerank → Lifecycle Decay Boost → Length Norm → Hard Min Score → MMR Diversity
• BM25 keyword preservation (score ≥ 0.75 bypasses semantic filter — protects API keys, ticket numbers)
• 4 reranker providers: Jina, SiliconFlow, Voyage AI, Pinecone

• 3 tiers: Core (β=0.8, floor=0.9) / Working (β=1.0, floor=0.7) / Peripheral (β=1.3, floor=0.5)
• Promotion/demotion rules based on access count, composite score, importance, age
• Composite score: Recency 40% + Frequency 30% + Intrinsic 30%
• Access reinforcement: frequently recalled memories decay more slowly

• Scope formats: global, agent:<id>, custom:<name>, project:<id>, user:<id>
• scopes.agentAccess mapping for multi-scope agents
• Disable memory entirely: { "plugins": { "slots": { "memory": "none" } } }

Core (auto-registered): memory_recall, memory_store, memory_forget, memory_update Management (opt-in): memory_stats, memory_list Self-improvement (opt-in): self_improvement_log, self_improvement_extract_skill, self_improvement_review

• LEARNINGS.md (IDs: LRN-YYYYMMDD-XXX) and ERRORS.md (IDs: ERR-YYYYMMDD-XXX)
• Entry lifecycle: pending → resolved → promoted_to_skill
• Skill scaffold generation from learning entries

Full coverage of all openclaw memory-pro commands: list, search, stats, delete, delete-bulk, export, import, reembed, upgrade, migrate

• Step-by-step model pull commands
• Ollama health check and embedding endpoint verification
• JSON output reliability notes per model
• Remote Ollama host configuration
• Fallback when Smart Extraction fails with local LLM

• 5 Iron Rules for AI agents (dual-layer storage, LanceDB hygiene, recall-before-retry, etc.)
• /lesson and /remember custom slash command templates for AGENTS.md

Deep technical content loaded only when needed:

• Database schema: LanceDB memories table fields and metadata keys
• Source file map: All 31 source files with sizes and responsibilities
• Retrieval pipeline: Full scoring formula chain with all 9 parameters and defaults
• Weibull decay formulas: recency = exp(-lambda × daysSince^beta) with tier-specific parameters
• Embedding config interface: All EmbeddingConfig options
• Document chunking: 5-level splitting hierarchy, smart chunking math
• Smart metadata system: Three-tier content fields, bounded array limits, normalization functions
• LLM client internals: Temperature, response parsing, error recovery strategy
• Noise filter details: 5 built-in noise categories, auto-learning prototypes (bank cap: 200)
• Adaptive retrieval full logic: Skip/force conditions with CJK equivalents
• Access tracking & reinforcement: Debounce timer, logarithmic reinforcement curve
• Reflection storage subsystem: 4 storage types, importance weights, dedup threshold

The underlying plugin is maintained at CortexReach/memory-lancedb-pro.

Key specs:

• Version: 1.1.0-beta.8
• Storage: LanceDB (embedded, no separate server)
• Retrieval: Hybrid vector + BM25 with RRF fusion
• Node.js: 22.16+ required, 24 recommended
• License: MIT

MIT