# Send Enhanced Memory to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - 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. ## Suggested prompts ### New install ```text 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 ```text 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. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "enhanced-memory", "name": "Enhanced Memory", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/JamesEBall/enhanced-memory", "canonicalUrl": "https://clawhub.ai/JamesEBall/enhanced-memory", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/enhanced-memory", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=enhanced-memory", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "scripts/crossref_memories.py", "scripts/embed_memories.py", "scripts/memory_salience.py", "scripts/search_memory.py" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "enhanced-memory", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-02T22:03:31.540Z", "expiresAt": "2026-05-09T22:03:31.540Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=enhanced-memory", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=enhanced-memory", "contentDisposition": "attachment; filename=\"enhanced-memory-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "enhanced-memory" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/enhanced-memory" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/enhanced-memory", "downloadUrl": "https://openagent3.xyz/downloads/enhanced-memory", "agentUrl": "https://openagent3.xyz/skills/enhanced-memory/agent", "manifestUrl": "https://openagent3.xyz/skills/enhanced-memory/agent.json", "briefUrl": "https://openagent3.xyz/skills/enhanced-memory/agent.md" } } ``` ## 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 — 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) — cosine similarity via nomic-embed-text embeddings Keyword matching (0.25) — query term overlap with chunk text Header matching (0.1) — query terms in section headers Filepath scoring (0.25) — query terms matching file/directory names Automatic behaviors: Temporal routing — date references ("yesterday", "Feb 8", "last Monday") get 3x boost on matching files Adaptive weighting — when keyword overlap is low, shifts to 85% vector weight Pseudo-relevance feedback (PRF) — when top score < 0.45, expands query with terms from initial results and re-scores ### scripts/enhanced_memory_search.py — 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 — 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 — 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 × staleness considering: file type (topic > core > daily), size, access frequency, and query gap correlation. ### scripts/crossref_memories.py — 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 # 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(n²) 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. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: JamesEBall - Version: 1.0.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-02T22:03:31.540Z - Expires at: 2026-05-09T22:03:31.540Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/enhanced-memory) - [Send to Agent page](https://openagent3.xyz/skills/enhanced-memory/agent) - [JSON manifest](https://openagent3.xyz/skills/enhanced-memory/agent.json) - [Markdown brief](https://openagent3.xyz/skills/enhanced-memory/agent.md) - [Download page](https://openagent3.xyz/downloads/enhanced-memory)