Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Memory Search
Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.
skill openclawclawhub Free
Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.
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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
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
- aigentic-net
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Memory Search
You have two tools for recalling information from your memory files. Use them.
memory_search
Semantic vector search across your indexed memory files (MEMORY.md, memory/*.md, and session transcripts). Parameters: ParamTypeRequiredDescriptionquerystringyesNatural language question or topic to search formaxResultsnumbernoMax results to return (default: 6)minScorenumbernoMinimum relevance score threshold (0-1) Example calls: { "query": "what projects is the human working on" } { "query": "preferences about code style", "maxResults": 3 } { "query": "important dates birthdays deadlines", "maxResults": 10, "minScore": 0.3 } Returns: Array of results, each with: snippet โ the matching text chunk path โ relative file path (e.g. MEMORY.md, memory/2026-02-07.md) startLine / endLine โ line range in the source file score โ relevance score citation โ formatted source reference (in direct chats)
memory_get
Read a specific section of a memory file by path and line range. Use this after memory_search to pull more context around a result. Parameters: ParamTypeRequiredDescriptionpathstringyesRelative path from workspace (e.g. MEMORY.md, memory/2026-02-07.md)fromnumbernoStarting line numberlinesnumbernoNumber of lines to read Example calls: { "path": "MEMORY.md" } { "path": "memory/2026-02-07.md", "from": 15, "lines": 30 }
When to Use Memory Search
Always search before answering about: Prior conversations or decisions The human's preferences, habits, or opinions Dates, deadlines, birthdays, events Project status or history Anything the human said "remember this" about Todos, action items, or commitments People, names, relationships The pattern is: Receive a question that might involve past context Call memory_search with a relevant query Review the results If a snippet looks promising but needs more context, call memory_get with the path and line range Answer using what you found (cite sources in direct chats)
When NOT to Use
Purely factual questions with no personal context ("what is Python?") The human explicitly gives you all the context you need in the message You just searched and the results are still in your context
Tips
Be specific in queries. "birthday" works better than "important information about the human." Search multiple angles. If one query returns nothing useful, try rephrasing. "project deadlines" and "what's due soon" might return different results. Don't over-fetch. Start with default maxResults. Only increase if you need more coverage. Use memory_get sparingly. The search snippets are usually enough. Only pull full sections when you need surrounding context. Say when you checked. If you searched and found nothing, tell the human: "I checked my memory and didn't find anything about that." Don't silently guess.
What Gets Indexed
Your memory search covers: MEMORY.md โ your curated long-term memory memory/*.md โ daily notes and raw logs Session transcripts (if enabled) These files are automatically indexed. You don't need to trigger indexing โ just write to the files and the system handles the rest.
Do NOT
Do NOT try to run shell commands like cat or ls to read memory files. Use memory_search and memory_get. Do NOT try to configure or debug the search system. That's operator config, not your job. Do NOT assume memory is empty without searching first. The index may have content even if the memory/ directory looks sparse.
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
1 Docs
- SKILL.md