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tc-tracker

Track technical changes with validated lifecycle states and resumable handoffs.

Updated Apr 24, 2026
Search Tags:#code audit#session handoff#change tracking#state machine#technical changes#json records
One-click install
npx skills add https://github.com/Veloxia-agency/VELOXIA-WEB --skill tc-tracker-veloxia-agency
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tc-tracker
Source: https://github.com/Veloxia-agency/VELOXIA-WEB/tree/main/.claude/skills/engineering/skills/tc-tracker
Command: npx skills add https://github.com/Veloxia-agency/VELOXIA-WEB --skill tc-tracker-veloxia-agency

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill replaces ad hoc change notes with structured technical change records, so code work can be tracked, resumed, and audited without losing context between sessions.

Core Features & Use Cases

  • Change Tracking: Record what changed, why it changed, who changed it, and how it was tested.
  • Lifecycle Management: Move technical changes through a validated state machine from planned to deployed.
  • Session Continuity: Preserve handoff details so a new AI session can resume work quickly and safely.
  • Use Case: A developer finishes part of an authentication feature, writes a handoff, and later resumes the same change with the exact next steps and blockers already captured.

Quick Start

Use the tc-tracker skill to initialize tracking for your project, create a technical change record, update its status and handoff details as work progresses, and check the registry when you need a current summary.

Frequently Asked Questions about tc-tracker

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

FAQPage Schema
How do I track code changes and preserve context for AI session handoffs?▼

You can track code changes by creating structured JSON records with append-only revision histories. This preserves handoff details like next steps and blockers, allowing new AI sessions to resume technical work quickly and safely.

What is the best way to audit code refactoring and bug fixes across a project repository?▼

Auditing code refactoring requires structured technical change records that capture what changed, why it changed, and how it was tested. Sequential identifiers and schema validation ensure reliable tracking across project repositories.

How does a state machine enforce lifecycle management for technical changes?▼

A state machine enforces lifecycle management by validating status transitions from planned to deployed. This ensures technical changes follow strict lifecycle rules, preventing invalid state updates during code edits and refactors.

Can I use JSON records for session continuity without losing context between AI sessions?▼

Yes, JSON records support session continuity by capturing handoff details and next steps. Atomic JSON writes ensure safe updates, so new AI sessions can resume work with exact blockers and progress already documented.

Does this change tracking approach work for release documentation and code audits?▼

Yes, this approach works for release documentation and code audits by maintaining an append-only revision history. It tracks what changed, who changed it, and how it was tested, providing a clear audit trail.

What are the limitations of using atomic JSON writes for technical change tracking?▼

Atomic JSON writes ensure safe updates but require strict schema validation and sequential identifiers. This structured approach replaces ad hoc change notes, meaning all records must conform to the validated state machine without loose text entries.

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