context-management

Tag milestones, monitor usage, and compress conversation history.

273|21|Updated Feb 8, 2026
One-click install
npx skills add https://github.com/ttttmr/pi-context --skill context-management-ttttmr
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: context-management
Source: https://github.com/ttttmr/pi-context/tree/main/skills/context-management
Command: npx skills add https://github.com/ttttmr/pi-context --skill context-management-ttttmr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Conversation history grows and noise accumulates, degrading reasoning; this skill provides a structured approach to manage memory by tagging, monitoring, and compressing context.

Core Features & Use Cases

  • Proactive context structuring: use context_tag to bookmark stable points.
  • Visibility of the chat graph: use context_log to monitor usage, segment size, and identify pollution.
  • Privacy-preserving cleanup: use context_checkout to squash finished tasks into concise summaries to reclaim context window space.
  • Use Case: During long research chats or multi-step debugging, tag milestones and compress past discussions to maintain a clean working memory.

Quick Start

Tag a milestone before a major step and squash the completed discussion into a concise summary.

Frequently Asked Questions about context-management

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

FAQPage Schema
How do I manage AI conversation context and prevent memory degradation in long chats?

Manage AI conversation context by tagging stable milestones, monitoring usage, and compressing finished tasks into concise summaries to prevent noise from degrading reasoning quality.

What is the best way to compress conversation history and reclaim context window space?

The best way to compress conversation history is squashing completed discussions into concise summaries to reclaim context window space and maintain a clean working memory.

How does tagging conversation milestones improve multi-step debugging sessions?

Tagging conversation milestones bookmarks stable points before major steps, allowing you to isolate completed debugging phases and keep the active context crisp.

Can I monitor context usage and segment size during complex planning sessions?

Yes, you can monitor context usage and segment size during complex planning sessions to identify context pollution and track how much memory the dialogue consumes.

When should I compress past discussions to maintain clean working memory?

Compress past discussions when finished tasks accumulate and noise degrades reasoning, squashing them into summaries to proactively maintain clean working memory.

Does this context management approach require external dependencies or tools?

No, this approach requires no external dependencies, utilizing internal context tagging, logging, and checkout mechanisms to structure memory and clean up history.