What are the four core patterns in Context Window Management?

The four core patterns are: Tiered Context Strategy (selecting handling approach by token count), Serial Position Optimization (structuring prompts to exploit LLM weighting of beginning/end), Intelligent Summarization (sorting messages by importance score and preserving high-value content), and Token Budget Allocation (splitting context window into fixed percentages for different components).

When should I use summarization instead of just truncating old messages?

You should summarize old messages instead of truncating them because naive message truncation can lose critical context like user preferences and key facts. Intelligent Summarization keeps the highest-value messages up to 70% of a target token budget and summarizes the rest into a single system message, preserving important information while managing context size.

What does the Tiered Context Strategy recommend for conversations over 100K tokens?

For conversations over 100K tokens, the Tiered Context Strategy recommends using RAG (Retrieval-Augmented Generation) with the claude-3-5-sonnet model, rather than attempting to fit all context into the window.

Does this skill cover RAG implementation and embedding models?

No, this skill explicitly does not cover RAG implementation, embedding model selection, or model fine-tuning—those are handled by separate sibling skills (rag-implementation, conversation-memory, and prompt-caching) that compose together with this skill for a complete context system.

Skill

Optimize LLM Context Window Management

Name: Context Window Management
Availability: OnlineOnly
Author: Antigravity

Skill documenting LLM context-window strategies: tiered routing, serial-position optimization, importance-based summarization, and token budgets.

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Works with openai langchainclaude

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Spark score

out of 100

Updated 14 days ago

Source checked Sep 7, 2026

Version 16.9.1

Models

claude 3 5 haikuclaude 3 5 sonnet

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Why it matters

Enhance Large Language Model (LLM) performance by intelligently managing context windows. This asset employs strategies like summarization, trimming, and routing to ensure optimal information flow and prevent context degradation.

Outcomes

What it gets done

Implement tiered context strategies based on message volume.

Apply serial position optimization for prompt construction.

Utilize intelligent summarization based on message importance.

Allocate token budgets for predictable context management.

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Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-context-window-management | bash

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Overview

Context Window Management

A skill documenting four LLM context-window strategies - tiered routing by token count, serial-position prompt layout, importance-based summarization, and fixed-percentage token budgeting - to keep multi-turn conversations within model limits. Use it when building a multi-turn LLM app or constructing prompts with heavy supporting context; it doesn't cover RAG implementation, fine-tuning, or embeddings.

What it does

This skill documents strategies for managing LLM context windows: a tiered strategy that switches between full-context, summarization, and RAG based on token count; serial-position optimization that places system instructions and critical context at the start of a prompt and the current query at the end, since models weight the beginning and end most heavily; importance-based summarization that scores messages by importance, critical-info content, and whether they were referenced later rather than summarizing purely by recency; and token-budget allocation that splits a model's context window into fixed percentages for system prompt, critical context, history, query, and reserved response space, reallocating any unused budget back into history. It also documents validation checks a system should run: flag context built without token counting, catch naive message truncation that drops critical content instead of summarizing it, catch hardcoded token limits that should be model-specific and configurable, and catch LLM calls made without any context-management strategy at all.

When to use - and when NOT to

Use it when building any multi-turn conversation system that needs predictable context management, or when constructing prompts with significant supporting context. It explicitly does not cover RAG implementation details, model fine-tuning, or embedding models - those stay out of scope even though RAG is referenced as one of the context strategies. It also defines delegation triggers to companion skills: a request mentioning retrieval, RAG, or search hands off to rag-implementation; one mentioning memory or persistence hands off to conversation-memory; one mentioning caching hands off to prompt-caching.

Inputs and outputs

Input: a message list plus a target token budget or model's max token count. Output: a prepared context object matching the selected tier (full, summarize, or rag), or a TokenBudget object allocating 10% to system prompt, 15% to critical context, 40% to history, 10% to query, and 25% reserved for the response.

const TIERS: ContextTier[] = [
    { maxTokens: 8000, strategy: 'full', model: 'claude-3-haiku' },
    { maxTokens: 32000, strategy: 'full', model: 'claude-3-5-sonnet' },
    { maxTokens: 100000, strategy: 'summarize', model: 'claude-3-5-sonnet' },
    { maxTokens: Infinity, strategy: 'rag', model: 'claude-3-5-sonnet' }
];

Integrations

Recommends tiktoken for token counting, LangChain for context-management utilities, and the Claude API's 200K+ context window with caching support as reference tools; pairs with the rag-implementation, conversation-memory, and prompt-caching skills to form a complete context system.

Who it's for

Developers building multi-turn LLM applications who need to keep context within a model's limits without silently dropping important information.

FAQ

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