Context Shredding (Grade A) logo

Context Shredding (Grade A)Security-tested data-ai skill for Claude AI. Grade A. Runtime context compression for agents approaching model context limits. Defines 3 compression tiers (full/summarized/minimal) with per-artifact t

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Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

Context Shredding is a runtime context compression system for AI agents approaching model context limits. It defines three compression tiers (full, summarized, minimal) with per-artifact templates to reduce artifact loading size at runtime and manage context budgets. The system estimates context usage, selects the appropriate compression tier, and applies compression templates. It's suitable for reducing context usage when loading large artifacts or when conversation length exceeds 60% of the model context.

Key features

  • Runtime context compression
  • Three compression tiers (full, summarized, minimal)
  • Per-artifact templates
  • Context usage estimation

Pricing

Model
Free
Category
Skills
Rating
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Use cases

Reducing Context Usage

Use Context Shredding when conversation length suggests >60% of model context is used or when loading multiple large artifacts.

Managing Context Budgets

Apply Context Shredding to manage context budgets and prevent exceeding model context limits.

Pros & Cons

Pros

  • Reduces artifact loading size at runtime
  • Helps manage context budgets
  • Improves performance when loading large artifacts

Cons

  • May not be suitable for diagnostic context auditing
  • Limited to specific use cases, e.g., not for Azure infrastructure

Reviews

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Q&A

What are the primary use cases and limitations of Context Shredding?

It’s ideal for agents that need to load large artifacts or when conversation length exceeds ~60 % of the model context, helping manage budgets and improve performance. It’s not suited for diagnostic context auditing or for managing Azure infrastructure.

Asked by Youssef El-Sayed · Jan 23, 2026

Which model context limits does Context Shredding support?

The skill includes thresholds for models such as Anthropic Opus (200 K tokens) and OpenAI GPT‑5.3 Codex (128 K tokens), using the same tier logic based on the percentage of those limits that are in use.

Asked by Bartek Adamski · Nov 22, 2025

How does Context Shredding decide which compression tier to apply to an artifact?

Before loading an artifact, the system estimates current token usage (1 token ≈ 4 characters), calculates the usage percentage of the model’s limit, and selects a tier: full (< 60 % usage), summarized (60‑80 %), or minimal (> 80 %). It then applies the corresponding per‑artifact compression template.

Asked by Otto Berg · Oct 31, 2025

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