When is the best time to use Chat Compactor?

You should use Chat Compactor when finishing a task, reaching the limit of your context window, or preparing to switch between different projects to ensure all progress is documented.

What is included in a session summary?

Each summary includes a context snapshot, accomplished tasks, key decisions with rationale, current working/broken states, failed approaches (dead ends), and prioritized next steps.

Does this skill help with long-term memory?

Yes, by saving these summaries as markdown files in your repository, you provide a permanent reference that any future AI agent can read to understand the project history.

How does this prevent 'dead ends'?

The skill specifically prompts for failed approaches and why they didn't work, which prevents the next AI agent from wasting time and tokens on the same unsuccessful methods.

Chat Compactor

Name: Chat Compactor
Author: ZhanlinCui

byZhanlinCui

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Productivity & Workflow

Generates structured, machine-readable session summaries to preserve context and ensure seamless continuity between AI agent interactions.

About

Chat Compactor is a specialized utility designed to solve the problem of context loss in long-running AI development cycles. By transforming complex chat histories into agent-optimized handoff documents, it captures critical decision rationale, documents 'dead ends' to prevent redundant troubleshooting, and maps out the current project state. This skill is ideal for developers who need to switch tasks, take breaks, or manage large context windows without losing the nuanced technical insights gained during a session.

Key Features

Tracks 'Dead Ends' to prevent the agent from re-attempting failed solutions
Produces prioritized next-step lists for immediate session resumption
Maps project state including working features, blockers, and modified files
Generates structured markdown handoff documents for AI-to-AI continuity
Automatically triggers when context windows reach capacity limits
17 GitHub stars

Use Cases

Creating a technical audit trail of decisions and architectural choices for a project
Compacting a long conversation to stay within LLM context window limits
Wrapping up a complex debugging session to resume later without loss of context

About

Key Features

Tracks 'Dead Ends' to prevent the agent from re-attempting failed solutions
Produces prioritized next-step lists for immediate session resumption
Maps project state including working features, blockers, and modified files
Generates structured markdown handoff documents for AI-to-AI continuity
Automatically triggers when context windows reach capacity limits
17 GitHub stars

Use Cases

Creating a technical audit trail of decisions and architectural choices for a project
Compacting a long conversation to stay within LLM context window limits
Wrapping up a complex debugging session to resume later without loss of context