🧠 LLMs & Model APIs

Context Compactorv0.3.8

Name: Context Compactor
Author: emberdesire

context-compactor

emberdesire

Token-based context compaction for local models (MLX, llama.cpp, Ollama) that don't report context limits.

latest

Download Package View on ClawHub

Installs (all time)

Installs (current)

Downloads

2.5K

Stars

CreatedFeb 11, 2026

UpdatedMay 17, 2026

Install & Quick Start

Install via ClawdBot CLI:

clawdbot install emberdesire/context-compactor

Skill Package6 files

📋SKILL.mdmarkdown

Failed to load file.

Quality Score

A65/100

Grade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.

Market Validation9/35

· 1223 downloads (moderate demand)
· 2 installs (very low)

Documentation20/25

· SKILL.md present
· Detailed documentation (≥3000 chars)
· Contains usage examples or trigger description
· Detailed summary

Package Completeness11/15

· skillAssets present (5 files)

Security Analysis

💙 Low Risk

UNSAFE_SHELLmedium

Potentially destructive shell commands in tool definitions

rm -rf ~

UNDOCUMENTED_EXTERNALlow

Calls external URL not in known-safe list

https://github.com/E-x-O-Entertainment-Studios-Inc/openclaw-context-compactor

AI Analysis

The skill's primary function is local context management with no evidence of data exfiltration or credential harvesting. The only external reference is a GitHub repository URL for installation, which aligns with the skill's stated purpose and poses minimal risk.

Audited Apr 17, 2026 · audit v1.0

💡

Usage Guide

Generated Mar 11, 2026

AI developers using local models like MLX or llama.cppEnterprises deploying private AI solutions with OpenClawbeginner

💡 Application Scenarios

Legal Document AnalysisLegal Services

Law firms use local models to analyze lengthy legal documents and case histories. Context Compactor prevents silent truncation, ensuring accurate summaries of older case details while maintaining recent legal arguments in full context for coherent analysis.

Academic Research AssistanceEducation and Research

Researchers employ local LLMs for literature reviews and data synthesis. The plugin proactively summarizes older research papers, allowing models to focus on recent findings and queries without hitting token limits, improving research efficiency.

Customer Support AutomationCustomer Service

Companies deploy local models for handling customer support tickets with long conversation histories. Context Compactor summarizes earlier interactions, enabling the AI to retain key customer details while responding to new issues within token constraints.

Software Development Code ReviewTechnology and Software

Development teams use local models to review extensive codebases and commit histories. The plugin compacts older code discussions, ensuring the AI can analyze recent changes and provide relevant feedback without exceeding context limits.

Healthcare Patient Record ManagementHealthcare

Healthcare providers utilize local LLMs to process patient histories and medical notes. Context Compactor summarizes older records, allowing models to focus on recent symptoms and treatments for accurate diagnostic support while adhering to privacy with local deployment.

💼 Business Models

Open-Source Plugin with Premium SupportSubscription fees from support services

Offer the Context Compactor as a free open-source plugin to drive adoption. Generate revenue through paid support plans, including customization, tuning for specific models, and priority troubleshooting for enterprise clients using local AI deployments.

Integration as a Value-Added ServiceLicensing fees and partnership revenue shares

Partner with local model providers (e.g., MLX, llama.cpp) to bundle Context Compactor as a premium feature. Charge licensing fees per integration or offer it as part of higher-tier packages that enhance model reliability and user experience.

SaaS for AI Development TeamsMonthly or annual SaaS subscriptions

Develop a cloud-based SaaS platform that extends Context Compactor with advanced analytics, multi-model support, and automated tuning. Target AI development teams with subscription tiers based on usage volume and features like real-time monitoring and optimization insights.

💬 Integration Tip

Start by installing via the npx setup command and adjust maxTokens based on your model's context size; use /context-stats to monitor usage and fine-tune settings for optimal performance.