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by championswimmer · Agent Tool · ★ 213
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pi-context-prune A Pi coding-agent extension that summarizes completed tool-call batches, prunes raw tool outputs from future LLM context, and exposes a escape hatch to recover any original output on demand. Related Extensions Here are some other Pi extensions that work well alongside context pruning: pi-context-usage (npm) What it does: Visualizes the current size of your LLM context and breaks it down to show exactly what is taking up space (system prompt, user messages, tool calls, tool results, etc.). Why use it: It's the perfect way to see why you need pruning. You can use it to inspect your context before and after a prune to see exactly how much space the extension just saved you. pi-cache-graph (npm) What it does: Plots your provider's prefix cache hits and misses as a live graph inside the TUI. Why use it: Pruning context directly impacts cache re-use. This extension lets you see the real-time effect of your chosen mode on cache stability. Why 📖 For a deep dive into how pruning works, how prefix caching interacts with it, and the research behind summarization-based context management, se
| Stars | 213 |
| Forks | 19 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 65.1510916080782/100 |
| Open Issues | 9 |
| Last Updated | 2026-08-15 |
| Created | 2026-04-20 |
| Platforms | node |
| Est. Tokens | ~18k |
These tools work well together with pi-context-prune for enhanced workflows:
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pi-context-prune is Pi coding-agent extension for pruning tool-call trees. It is categorized as a Agent Tool with 213 GitHub stars.
pi-context-prune is primarily written in TypeScript.
You can find installation instructions and usage details in the pi-context-prune GitHub repository at github.com/championswimmer/pi-context-prune. The project has 213 stars and 19 forks, indicating an active community.
The top alternatives to pi-context-prune on Agent Skills Hub include awesome-android-agent-skills, claude-code-skill-factory, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.