qlik-performance
Performance optimization for Qlik Sense: three-metric framework (Base RAM, Peak RAM, Reload Time), key encoding hierarchy, QVD optimized load rules, expression performance (If vs set analysis, Aggr caution, master items for caching), memory choreography, and data reduction techniques. Load when optimizing or reviewing performance-sensitive artifacts.
Install
With your agent
Install OMGSkills skill "pupfish-llc/qlik-agents:skills/qlik-performance" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/Pupfish-LLC/qlik-agents /tmp/qlik-agents && ln -s /tmp/qlik-agents/skills/qlik-performance ~/.claude/skills/qlik-performanceFrom README
Qlik Performance Optimization Core Principle Qlik Sense stores all loaded data in RAM. Performance optimization targets three metrics that often conflict: | Metric | What It Measures | Why It Matters | |--------|-----------------|----------------| | Base RAM | Memory footprint after reload completes | Must stay under app size limits, which vary by tenant and space configuration | | Peak RAM | Highest memory consumption during reload | Must stay under reload memory limits, which vary by tenant and space configuration | | Reload Time | Wall-clock duration of full reload | Must stay under reload duration limits, which vary by tenant and space configuration | Trade-off awareness is mandatory. Strategies that reduce one metric often increase another: AutoNumber() reduces Base RAM (integer keys replace strings) but increases Peak RAM and Reload Time (engine must hold original + autonumbered values simultaneously during encoding).