The Graph Query Optimization Assistant analyzes graph database queries, detects execution bottlenecks, and generates high-performance Cypher and SPARQL alternatives.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install graph-query-optimization-assistant
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 graph-query-optimization-assistant 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 Graph Query Optimization Assistant?
The Graph Query Optimization Assistant is an enterprise-grade utility built to diagnose inefficiencies within graph database traversals and query structures. By evaluating query execution flows in Cypher or SPARQL, this tool isolates high-cost operations, such as unbounded relationship traversals or late-stage filtering, and recommends strategic database improvements. This assistant is a vital component of the Openclaw Skills family, giving developers concrete pathways to accelerate response times and minimize resource overhead.
Optimizing interconnected data pathways requires a nuanced understanding of graph topology and query planner behavior. This skill replaces guesswork with precise engineering, automatically highlighting node entry points, proposing composite index patterns, and restructuring filters for optimal performance. Adding this tool to your Openclaw Skills workspace streamlines high-volume query tuning, making complex knowledge graphs scalable and cost-effective.
Graph Query Optimization Assistant Use Cases
- Tuning Slow Cypher Queries: Restructuring complex MATCH statements in Neo4j to drastically reduce execution time.
- Optimizing SPARQL RDF Queries: Resolving unbounded triplestore scans through triple pattern reordering and OPTIONAL placement.
- Recommending Indexing Strategies: Identifying which properties require single, composite, or full-text indexes to optimize entry points.
- Mitigating Memory Constraints: Restructuring filters and early aggregation points to keep execution-stage result sets lightweight.
- Comparative Query Benchmarking: Evaluating two query formats side-by-side to verify speed, node visits, and memory usage improvements.
How Graph Query Optimization Assistant Works
- Analyze Input Query: The system parses the provided Cypher or SPARQL code to identify starting nodes, relationship directions, and traversal depths.
- Inspect Query Patterns: It compares the execution flow against defined pattern categories (e.g., node selection, index strategy, and query structure).
- Detect Bottlenecks: It flags critical issues like unindexed filters, wildcard relationships, or excessive intermediate results.
- Generate Recommendations: It drafts concrete fixes, including index creation commands, restructured clauses, and bounded depths.
- Provide Optimized Queries & Metrics: It outputs the refined query script alongside estimated cost reductions and performance metrics.
Graph Query Optimization Assistant Setup
To deploy the Graph Query Optimization Assistant as part of your Openclaw Skills workflow, prepare your local environment with these commands:
- Install the required graph drivers and analytical libraries in your environment:
pip install py2neo rdflib graphistry
- Integrate the skill directly into your agent runtime configuration:
claw skill install graph-query-optimization-assistant
- Set up database connection profiling endpoints to enable deep EXPLAIN and PROFILE analysis directly against your active Neo4j or Jena instances.
Graph Query Optimization Assistant Data Schema & Taxonomy
Input and Output Parameters
| Attribute | Description | Data Type |
|---|---|---|
original_query |
The raw, unoptimized Cypher or SPARQL query submitted by the user. | String |
database_type |
Database syntax family (e.g., Neo4j, GraphDB, SPARQL Endpoint). | String |
optimized_query |
The rewritten query code incorporating optimal traversal paths. | String |
index_recommendations |
Index creation definitions needed to support the performance upgrade. | List |
performance_metrics |
Structured JSON containing execution speed, memory delta, and node visits. | Object |
Metric Taxonomy
- Execution Time (ms): Absolute query execution speed duration.
- Node Visits: Total volume of graph nodes evaluated during traversal.
- Memory Usage (MB): Thread memory consumption allocated for the query.
- Traversal Cost: Estimated mathematical cost of traversing relations, calculated as
Total Cost = Entry Point Cost + Traversal Cost + Filter Cost + Result Processing Cost.
Graph Query Optimization Assistant Advanced Features
- Automated Entry Point Selection: Identifies and prioritizes node filters that target properties with the highest cardinality selectivity.
- Multi-Dialect Core: Seamlessly toggles between Cypher optimization policies (MATCH/MERGE optimizations) and SPARQL optimizations (triple pattern ordering).
- Index Strategy Planner: Formulates tailored single, composite, and full-text indexes based on dynamic WHERE clause properties.
- Agentic Skill Chain: Combines natively with graph-query-debugging-tool and graph-schema-validation inside the Openclaw Skills suite to create automated, self-healing query layers.
SKILL.md
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