Does this skill work with any AI repository?

Yes, while optimized for Violet AI 'brain repos,' its structured phases and exit templates can be applied to any project managed via Claude Code.

What is a 'convo-exit' protocol?

It is a structured debrief at the end of a session where the AI reviews goals, lists outcomes, captures user feedback, and proposes repository improvements to be captured in a dedicated log.

How does this skill prevent topic drift?

The skill monitors for unrelated topics or context window bloat, proactively offering to start a new conversation thread to maintain focus and processing efficiency.

Why is the alignment phase important?

The alignment phase ensures the AI and user agree on the specific steps, agents, and patterns to be used before execution begins, reducing errors and wasted effort.

When should I skip using this skill?

You should skip these protocols for quick one-off questions, simple commands, or when you explicitly need to bypass process for immediate, urgent tasks.

Conversation Management

Name: Conversation Management
Author: violetio

byvioletio

Productivity & Workflow

Optimizes AI interaction workflows through structured conversation phases and exit protocols for continuous project improvement.

About

This skill provides a rigorous framework for managing complex AI-human interactions, ensuring clarity from the initial request to the final debrief. By implementing specific phases for understanding, alignment, and execution, it prevents topic drift and ensures that every session concludes with a comprehensive 'convo-exit' report. This protocol captures decisions made and files modified, and it identifies future tasks, making it essential for developers who want to maintain a clean context and systematically improve their repository's intelligence and documentation over time.

Key Features

Five-phase structured conversation workflow from Understanding to Exit
Standardized 'convo-exit' protocol for documenting outcomes and decisions
0 GitHub stars
Integrated task tracking to minimize unnecessary interruptions during execution
Proactive topic drift detection to maintain context focus and clarity
Automated feedback loops for continuous repository and agent improvement

Use Cases

Systematically documenting project outcomes and agent patterns after tasks
Maintaining high context quality in long-running AI coding conversations
Managing complex feature development sessions that require multi-step alignment

About

Key Features

Five-phase structured conversation workflow from Understanding to Exit
Standardized 'convo-exit' protocol for documenting outcomes and decisions
0 GitHub stars
Integrated task tracking to minimize unnecessary interruptions during execution
Proactive topic drift detection to maintain context focus and clarity
Automated feedback loops for continuous repository and agent improvement

Use Cases

Systematically documenting project outcomes and agent patterns after tasks
Maintaining high context quality in long-running AI coding conversations
Managing complex feature development sessions that require multi-step alignment