trends

Render per-function timing, energy, stack, and memory trends from LOCI session context.

1|Updated May 19, 2026
One-click install
npx skills add https://github.com/auroralabs-loci/loci-claude --skill trends
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: trends
Source: https://github.com/auroralabs-loci/loci-claude/tree/main/skills/trends
Command: npx skills add https://github.com/auroralabs-loci/loci-claude --skill trends

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Per-function measurement history on the current branch: timing, energy, stack, and memory trends over time from LOCI analysis. This capability helps you track how each function behaves across commits and branches, enabling early detection of regressions, optimization opportunities, and stability insights.

Core Features & Use Cases

  • Per-function trend charts: visualize timing, energy, stack depth, and memory usage per function across commits.
  • Branch-level comparison: contrast trends between branches to evaluate optimization progress.
  • Drill-down analysis: inspect function-level deltas and hotspots that drive performance changes.

Quick Start

Run LOCI trends to fetch the current branch's per-function history and render it in your console or report.

Frequently Asked Questions about trends

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track per-function performance trends across git branches?

Per-function performance trends across branches are tracked by applying LOCI session context to render timing, energy, stack depth, and memory history for each function.

Can I compare memory and timing measurements between different branches?

Yes, you can compare memory and timing trends between branches to evaluate optimization progress by contrasting per-function measurement history from the LOCI session context.

Do I need a valid LOCI session to monitor function energy and stack depth?

Yes, monitoring function energy and stack depth requires a valid LOCI session context along with post-analysis results to render branch-specific trends.

How do I detect performance regressions in specific functions over commits?

To detect performance regressions in specific functions over commits, visualize per-function timing, energy, stack, and memory trends to inspect function-level deltas.

What measurements are included in the per-function trend analysis?

Per-function trend analysis includes timing, energy, stack depth, and memory usage measurements extracted from the LOCI session context.