Context Slim

Name: Context Slim
Availability: InStock
Author: Hanabi

Compress, deduplicate, and budget context usage in the OpenClaw ecosystem.

3|2|Updated Mar 1, 2026

Search Tags:#context management #context optimization #token budgeting #context deduplication #AI compression

Ready to run

npx skills add https://github.com/hanabi-jpn/clawhub-skills --skill context-slim-hanabi-jpn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Context Slim
Source: https://github.com/hanabi-jpn/clawhub-skills/tree/main/archive/context-slim
Command: npx skills add https://github.com/hanabi-jpn/clawhub-skills --skill context-slim-hanabi-jpn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openclaw, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Context Slim addresses the issue of context window bloat in the OpenClaw ecosystem, preventing 'context_length_exceeded' errors by intelligently compressing and managing context.

Core Features & Use Cases

Context Compression: Utilizes three compression strategies (semantic, structural, priority) to reduce the context size while preserving critical information.
Deduplication: Detects and merges duplicate content to further reduce the context size.
Budget Management: Enforces token budgets for each context category and provides automatic optimization suggestions when thresholds are exceeded.
Auto-Compress: Automatically compresses the lowest-priority content when the context usage reaches 90% capacity.

Quick Start

Analyze your context usage with slim analyze to identify areas for optimization.

Frequently Asked Questions about Context Slim

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

FAQPage Schema

How do I optimize context window usage to prevent context_length_exceeded errors?▼

To optimize context window usage and prevent context_length_exceeded errors, use AI-driven context compression and deduplication techniques to reduce bloat. This process intelligently removes redundant information while preserving critical content.

What is context deduplication and how does it reduce token budget consumption?▼

Context deduplication detects and merges duplicate content within the context window. By eliminating redundant text, it significantly reduces token budget consumption and mitigates the risk of context length errors.

Does OpenClaw framework support automatic context compression when capacity is reached?▼

Yes, the OpenClaw framework supports automatic context compression. When context usage reaches 90% capacity, the system automatically compresses the lowest-priority content to prevent exceeding your token budget.

How do I analyze context usage to identify areas for token budgeting optimization?▼

To analyze context usage for token budgeting optimization, run a diagnostic command to evaluate your current context window. This identifies redundant information and provides automatic optimization suggestions when budget thresholds are exceeded.

What are the best strategies for context compression in AI agents?▼

The best strategies for context compression include semantic, structural, and priority-based approaches. These methods reduce context size by intelligently identifying and removing redundant information while preserving critical data for agent performance.

When should I not use automatic context compression for my agent workflows?▼

You should avoid automatic context compression when your agent workflows require every piece of low-priority context to remain fully intact without structural or semantic merging. In such cases, auto-compressing content at 90% capacity risks losing necessary background data.