vc-generate-closeout

Generate structured Markdown closeout packets with readiness and drift scores.

Updated Jun 20, 2026

Search Tags:#archive readiness #project closeout #next state recommendation #drift scoring #commit checkpoint

Ready to run

npx skills add https://github.com/tutran27/trust-room-AI --skill vc-generate-closeout-tutran27

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vc-generate-closeout
Source: https://github.com/tutran27/trust-room-AI/tree/main/.agents/skills/vc-generate-closeout
Command: npx skills add https://github.com/tutran27/trust-room-AI --skill vc-generate-closeout-tutran27

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, git, pandas, yaml, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Manages and automates the creation of closeout packets for completed project phases, including archive readiness, drift signal scoring, and next-state recommendations.

Core Features & Use Cases

Post-EXECUTE Closeout Packet Generation: Automatically creates detailed closeout packets after phase completion.
Archive-Readiness Classification: Scores the readiness for archiving based on implementation accuracy and validation evidence.
Drift Signal Scoring: Evaluates the urgency of following the UPDATE PROCESS based on signals from project changes.
Commit Checkpoint Recommendation: Suggests when to commit changes based on whether they are execution-related or process-related.
Move-On Next-State Recommendation: Determines the single best next action or plan after phase completion.

Quick Start

Generate a closeout packet for the phase named 'phase-01' by invoking the skill: 'generate closeout packet for phase-01'

Frequently Asked Questions about vc-generate-closeout

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

FAQPage Schema

How do I automate project closeout packet generation for completed phases?▼

Automate project closeout packet generation by using Python to process conversation context and git history. This produces a structured Markdown report detailing archive readiness, drift scores, and move-on state recommendations for completed project phases.

What is drift signal scoring in project phase closeout?▼

Drift signal scoring in project phase closeout evaluates the urgency of following the UPDATE PROCESS based on signals from actual project changes. It helps determine whether implementation accuracy deviates significantly from the original plan before archiving.

How do I classify archive readiness for a completed project phase?▼

Classify archive readiness for a completed project phase by validating actual git changes against implementation accuracy and validation evidence. The system generates a structured Markdown report scoring the readiness for archiving.

Does generating a closeout packet require git history integration?▼

Generating a closeout packet requires git history integration to validate actual changes made during the project phase. The Python-based system utilizes this git context alongside conversation data to produce accurate drift scores and commit checkpoint recommendations.

Can I use Python and pandas to recommend commit checkpoints for execution and process changes?▼

Use Python and pandas to recommend commit checkpoints by evaluating whether project changes are execution-related or process-related. The system processes session duration and complexity to suggest when to commit changes in a structured Markdown report.

What is the best way to determine the next state recommendation after project phase completion?▼

The best way to determine the next state recommendation after project phase completion is to analyze conversation context and git history. The system outputs a move-on next-state suggestion identifying the single best next action or plan in a Markdown report.