technical-change-tracker – Coding skill for AI agents

Technical Change Tracker

Overview

Track every code change with structured JSON records and accessible HTML output. Ensures AI bot sessions can resume seamlessly when previous sessions expire or are abandoned.

When to Use This Skill

• Use when you need structured change tracking across AI coding sessions
• Use when a bot session expires mid-task and the next session needs full context to resume
• Use when onboarding a project with undocumented change history

How It Works

State Machine

planned -> in_progress -> implemented -> tested -> deployed
             |
             +-> blocked

Commands

/tc init | /tc create | /tc update | /tc status | /tc resume | /tc close | /tc export | /tc dashboard | /tc retro

Session Handoff

Each TC stores: progress summary, next steps, blockers, key context, and files in progress — so the next bot session picks up exactly where the last left off.

Non-Blocking

This entrypoint bundles no runtime, commands or background agents. Use an already reviewed installation only after inspecting its current interface; delegation and scheduling require explicit authorization.

Features

• Structured JSON records with append-only revision history
• Test cases with log snippet evidence
• WCAG AA+ accessible HTML output (dark theme, rem-based fonts)
• CSS-only dashboard with status filters
• Python stdlib only — zero external dependencies
• Retroactive bulk creation from git history via /tc retro

Full Repository

https://github.com/Elkidogz/technical-change-skill — MIT License

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Execution boundaries

The listed features describe the external project, not capabilities installed by this file. Do not invent /tc execution or accessibility certification. Track only observed status transitions; implemented, tested and deployed require different evidence. Keep private paths and logs out of shared reports, and preserve existing records.