Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Enhanced Memory
Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adaptive weighting, pseudo-relevance feedback, salience scoring, and knowledge graph cross-references. Replaces the default memory search with a 4-signal fusion retrieval system. Use when searching memories, indexing memory files, building cross-references, or scoring memory salience. Requires Ollama with nomic-embed-text model.
skill openclawclawhub Free
Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adaptive weighting, pseudo-relevance feedback, salience scoring, and knowledge graph cross-references. Replaces the default memory search with a 4-signal fusion retrieval system. Use when searching memories, indexing memory files, building cross-references, or scoring memory salience. Requires Ollama with nomic-embed-text model.
โฌ 0 downloads โ
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/crossref_memories.py, scripts/embed_memories.py, scripts/memory_salience.py, scripts/search_memory.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- JamesEBall
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Enhanced Memory
Drop-in enhancement for OpenClaw's memory system. Replaces flat vector search with a 4-signal hybrid retrieval pipeline that achieved 0.782 MRR (vs ~0.45 baseline vector-only).
Setup
# Install Ollama and pull the embedding model ollama pull nomic-embed-text # Index your memory files (run from workspace root) python3 skills/enhanced-memory/scripts/embed_memories.py # Optional: build cross-reference graph python3 skills/enhanced-memory/scripts/crossref_memories.py build Re-run embed_memories.py whenever memory files change significantly.
scripts/search_memory.py โ Primary Search
Hybrid 4-signal retrieval with automatic adaptation: python3 skills/enhanced-memory/scripts/search_memory.py "query" [top_n] Signals fused: Vector similarity (0.4) โ cosine similarity via nomic-embed-text embeddings Keyword matching (0.25) โ query term overlap with chunk text Header matching (0.1) โ query terms in section headers Filepath scoring (0.25) โ query terms matching file/directory names Automatic behaviors: Temporal routing โ date references ("yesterday", "Feb 8", "last Monday") get 3x boost on matching files Adaptive weighting โ when keyword overlap is low, shifts to 85% vector weight Pseudo-relevance feedback (PRF) โ when top score < 0.45, expands query with terms from initial results and re-scores
scripts/enhanced_memory_search.py โ JSON-Compatible Search
Same pipeline with JSON output format compatible with OpenClaw's memory_search tool: python3 skills/enhanced-memory/scripts/enhanced_memory_search.py --json "query" Returns {results: [{path, startLine, endLine, score, snippet, header}], ...}.
scripts/embed_memories.py โ Indexing
Chunks all .md files in memory/ plus core workspace files (MEMORY.md, AGENTS.md, etc.) by markdown headers and embeds them: python3 skills/enhanced-memory/scripts/embed_memories.py Outputs memory/vectors.json. Batches embeddings in groups of 20, truncates chunks to 2000 chars.
scripts/memory_salience.py โ Salience Scoring
Surfaces stale/important memory items for heartbeat self-prompting: python3 skills/enhanced-memory/scripts/memory_salience.py # Human-readable prompts python3 skills/enhanced-memory/scripts/memory_salience.py --json # Programmatic output python3 skills/enhanced-memory/scripts/memory_salience.py --top 5 # More items Scores importance ร staleness considering: file type (topic > core > daily), size, access frequency, and query gap correlation.
scripts/crossref_memories.py โ Knowledge Graph
Builds cross-reference links between memory chunks using embedding similarity: python3 skills/enhanced-memory/scripts/crossref_memories.py build # Build index python3 skills/enhanced-memory/scripts/crossref_memories.py show <file> # Show refs for file python3 skills/enhanced-memory/scripts/crossref_memories.py graph # Graph statistics Uses file-representative approach (top 5 chunks per file) to reduce O(nยฒ) to manageable comparisons. Threshold: 0.75 cosine similarity.
Configuration
All tunable constants are at the top of each script. Key parameters: ParameterDefaultScriptPurposeVECTOR_WEIGHT0.4search_memory.pyWeight for vector similarityKEYWORD_WEIGHT0.25search_memory.pyWeight for keyword overlapFILEPATH_WEIGHT0.25search_memory.pyWeight for filepath matchingTEMPORAL_BOOST3.0search_memory.pyMultiplier for date-matching filesPRF_THRESHOLD0.45search_memory.pyScore below which PRF activatesSIMILARITY_THRESHOLD0.75crossref_memories.pyMin similarity for cross-ref linksMODELnomic-embed-textallOllama embedding model To use a different embedding model (e.g., mxbai-embed-large), change MODEL in each script and re-run embed_memories.py.
Integration
To replace the default memory search, point your agent's search tool at these scripts. The scripts expect: memory/ directory relative to workspace root containing .md files memory/vectors.json (created by embed_memories.py) Ollama running locally on port 11434 All scripts use only Python stdlib + Ollama HTTP API. No pip dependencies.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts1 Docs
- SKILL.md
- scripts/crossref_memories.py
- scripts/embed_memories.py
- scripts/memory_salience.py
- scripts/search_memory.py