A proactive memory retrieval skill that optimizes search queries to ensure AI agents maintain project continuity and context.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install smart-memory-query
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install smart-memory-query using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Smart Memory Query?
The Smart Memory Query skill is a sophisticated logic layer designed to enforce proactive and query-optimized use of the memory_search function within an AI agent's workflow. By identifying specific triggers—such as references to prior history, the initiation of new tasks, or the appearance of proper nouns—this skill ensures that the agent maintains continuity and honors previous decisions without needing manual prompts. This prevents the common issue of context loss in long-running projects.
At its core, this skill solves the problem of hallucination by omission by ensuring the agent checks its internal database whenever relevant information might exist. It utilizes strict query-building rules to split complex intents into short, 2-4 token queries, preventing the empty results often caused by over-constrained full-text search queries within Openclaw Skills.
Smart Memory Query Use Cases
- Retrieving past architectural decisions when starting a new development sprint.
- Checking for specific user preferences regarding coding styles or documentation formats.
- Looking up historical context when a specific tool, service, or person is mentioned.
- Ensuring that previous task agreements and project milestones are honored during current sessions.
How Smart Memory Query Works
- The agent monitors the conversation for three specific triggers: prior context references, new task starts, or proper noun mentions.
- Once a trigger is identified, the skill mandates a memory_search call instead of relying solely on current session context.
- The skill breaks down the search intent into multiple independent angles to avoid over-filtering results.
- It extracts 2-3 core nouns per angle, prioritizing proper nouns for maximum precision.
- It executes multiple concurrent queries through the memory_search tool, keeping each query between 2-4 tokens.
- The results are merged into the agent's active context, with a fallback retry mechanism using a single key noun if initial results are empty.
Smart Memory Query Setup
To integrate this skill into your workflow, add the skill definition to your agent's configuration path. This skill requires the memory_search tool to be active within your Openclaw Skills environment.
# Copy the skill into your local configuration
cp smart-memory-query.md ~/.openclaw/skills/
Ensure that your backend database is indexed for full-text search to support the short-token query logic effectively.
Smart Memory Query Data Schema & Taxonomy
| Component | Description |
|---|---|
| Trigger Classification | Categorizes input as T1 (Context), T2 (New Task), or T3 (Proper Noun). |
| Token Constraint | Limits query strings to 2-4 tokens to optimize FTS matching. |
| Multi-Query Logic | Executes separate searches for different intent angles. |
| Result Merging | Logic for synthesizing multiple search hits into a single context update. |
Smart Memory Query Advanced Features
- Proactive search enforcement that anticipates the need for historical data before the user requests it.
- Intent splitting technology that prevents the zero-result problem found in complex, multi-word queries.
- Proper noun prioritization to ensure specific project names and tools are correctly identified in Openclaw Skills.
- Automated fallback logic that retries failed searches with broader parameters to ensure no context is missed.
SKILL.md
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