Context Shredding (Grade A)セキュリティテスト済みのデータ-AIスキルClaude AIに適しています。Aランク。エージェントがモデルコンテキストの制限を超える Runtime context compression for agents approaching model context limits。

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