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SKILL verified MIT Self-run

Memory Upgrade

skill-profbernardoj-morpheus-skill-memory-upgrade · by profbernardoj

Diagnose and fix broken memory search in OpenClaw. Enables local embeddings, hybrid search (BM25+vector), session transcript indexing, MMR diversity, and temporal decay — all running locally with zero API keys. Use when: memory_search returns empty results, agent has poor cross-session recall, user wants to upgrade their memory system, or after a fresh OpenClaw install.

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Install

$ agentstack add skill-profbernardoj-morpheus-skill-memory-upgrade

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Memory Upgrade

Most OpenClaw installs have broken memory search — the memory_search tool returns empty results because no embedding provider is configured. OpenClaw auto-detects OpenAI → Google → Voyage keys; if none exist, embeddings stay disabled silently.

This skill fixes it with fully local inference. No API keys. No data leaves the machine.

Quick Start

# 1. Diagnose
bash scripts/diagnose.sh

# 2. Fix (patches openclaw.json, restart [REDACTED] after)
bash scripts/configure.sh

# 3. Restart [REDACTED]
openclaw [REDACTED] restart

# 4. Verify (waits for indexing, runs test query)
bash scripts/verify.sh

Optional Enhancements

# Organize memory files into clean directory structure
bash scripts/organize.sh

# Add YAML frontmatter tags to untagged files
bash scripts/tag.sh

What Gets Enabled

| Feature | Details | |---------|---------| | Local embeddings | embeddinggemma-300m (~328MB GGUF, auto-downloads) | | Hybrid search | BM25 keyword + vector semantic (70/30 weight) | | Session transcripts | Past conversations become searchable | | MMR diversity | Reduces duplicate/overlapping results (λ=0.7) | | Temporal decay | Recent memories rank higher (30-day half-life) | | Embedding cache | 50k entries, avoids re-embedding unchanged text | | File watcher | Auto-reindexes when memory files change |

How It Works

  • Patches agents.defaults.memorySearch in openclaw.json
  • Uses node-llama-cpp (ships with OpenClaw) for local embeddings
  • Vector search via sqlite-vec (ships with OpenClaw)
  • No external dependencies required

Notes

  • First search after restart may be slow (model loads into memory)
  • Initial indexing takes 30-120s depending on file count
  • Embedding model runs on CPU (ARM/x86), ~768-dim vectors
  • Compatible with existing memory files — no migration needed

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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