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levelup-trace

Generate and validate AI session execution traces from implementation metadata and feature artifacts.

Updated Apr 21, 2026
Search Tags:#documentation#spec-kit#levelup#trace-generation#ai-traces#trace-validation
One-click install
npx skills add https://github.com/chaimt/GarminShabbatMode --skill levelup-trace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: levelup-trace
Source: https://github.com/chaimt/GarminShabbatMode/tree/main/.cursor/skills/levelup-trace
Command: npx skills add https://github.com/chaimt/GarminShabbatMode --skill levelup-trace

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generating and validating AI session traces is time-consuming and often inconsistent, limiting reproducibility and learning from AI-driven workflows.

Core Features & Use Cases

  • Generate human-friendly session traces from implementation metadata and feature artifacts.
  • Validate trace completeness, quality gates, and evidence for audits and knowledge transfer.
  • Use Case: After completing /implement, run levelup.trace to produce a specs trace for the current feature and enable /levelup.specify to extract CDRs.

Quick Start

Run the levelup.trace command after completing /implement to capture a full trace for the current feature.

Frequently Asked Questions about levelup-trace

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

FAQPage Schema
What is the best way to validate AI session trace completeness and decision logging?▼

Generate AI session traces by running the levelup.trace command after completing implementation. It processes implementation metadata and feature artifacts to produce a human-friendly trace document capturing execution evidence for the current feature.

Do I need a spec-kit directory structure to generate session execution traces?▼

Yes, generating session execution traces requires the spec-kit project structure with a .specify/ directory. The trace generation process reads plan, spec, and commit artifacts from this structure to build the execution evidence log.

How does trace validation improve reproducibility in AI-driven workflows?▼

Trace validation improves AI-driven reproducibility by auditing evidence collection and decision logs against quality gates. This ensures implementation metadata is consistent and complete, enabling reliable knowledge transfer across feature artifacts.

Where are validated AI session traces saved after running the trace generation command?▼

Validated AI session traces are saved to the specs/{BRANCH}/trace.md file path. This output captures the generated execution evidence and decision logging specific to the current feature branch within the spec-kit structure.

Can I use trace generation for feature audits without manual evidence collection?▼

Yes, you can use trace generation for audits without manual evidence collection because it automatically aggregates implementation metadata and feature artifacts. It validates the gathered evidence against quality gates to produce an audit-ready trace document.

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