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zzallirog/weighted-compact

by Various

Substrate-based context compaction for Claude Code. Inspectable signal weights, local-only, MCP-ready. Alternative to /compact + hook-based capture (claude-mem, etc.).

Z

MCP

zzallirog/weighted-compact

Added 7 June 2026

#ai-memory #anthropic-claude #claude-code #claude-code-plugin #context-compaction #context-window #local-first #mcp

Overview

Provides context compaction for Claude Code using a Substrate-based approach. It applies inspectable signal weights to prioritize information, runs entirely locally, and is compatible with the Model Context Protocol (MCP). Serves as an alternative to the built-in /compact command or hook-based methods like claude-mem.

Best for

Best for
Developers using Claude Code who want transparent, local context compaction

Use cases

  • Compacting conversation context for Claude Code sessions
  • Inspecting weight-assigned signals to understand compaction decisions
  • Integrating local context compaction into MCP-compatible workflows

Notes

Provides context compaction for Claude Code using a Substrate-based approach. It applies inspectable signal weights to prioritize information, runs entirely locally, and is compatible with the Model Context Protocol (MCP). Serves as an alternative to the built-in /compact command or hook-based methods like claude-mem.

1 stars on GitHub. Last updated 2026-06-07. Licensed MIT.

Use cases

  • Compacting conversation context for Claude Code sessions
  • Inspecting weight-assigned signals to understand compaction decisions
  • Integrating local context compaction into MCP-compatible workflows

Pros

  • Runs locally with no external service dependencies
  • Inspectable signal weights provide transparency into compression logic
  • MCP-ready enables integration with other tools and interfaces

Cons

  • Single GitHub star indicates minimal adoption and testing
  • Requires familiarity with Substrate concepts for effective use
  • Alternative to established tools, may lack community support and documentation

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Runs locally with no external service dependencies
  • Inspectable signal weights provide transparency into compression logic
  • MCP-ready enables integration with other tools and interfaces

Cons

  • Single GitHub star indicates minimal adoption and testing
  • Requires familiarity with Substrate concepts for effective use
  • Alternative to established tools, may lack community support and documentation

Pairs with

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