context-window-management

name	context-window-management
description	Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
risk	unknown
source	vibeship-spawner-skills (Apache 2.0)
date_added	"2026-02-27T00:00:00.000Z"

Context Window Management

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot

Capabilities

• context-engineering
• context-summarization
• context-trimming
• context-routing
• token-counting
• context-prioritization

Prerequisites

• Knowledge: LLM fundamentals, Tokenization basics, Prompt engineering
• Skills_recommended: prompt-engineering

Scope

• Does_not_cover: RAG implementation details, Model fine-tuning, Embedding models
• Boundaries: Focus is context optimization, Covers strategies not specific implementations

Ecosystem

Primary_tools

• tiktoken - OpenAI's tokenizer for counting tokens
• LangChain - Framework with context management utilities
• Claude API - 200K+ context with caching support

Patterns

Tiered Context Strategy

Different strategies based on context size

When to use: Building any multi-turn conversation system

interface ContextTier { maxTokens: number; strategy: 'full' | 'summarize' | 'rag'; model: string; }

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' } ];

async function selectStrategy(messages: Message[]): ContextTier { const tokens = await countTokens(messages);

for (const tier of TIERS) {
    if (tokens <= tier.maxTokens) {
        return tier;
    }
}
return TIERS[TIERS.length - 1];

}

async function prepareContext(messages: Message[]): PreparedContext { const tier = await selectStrategy(messages);

switch (tier.strategy) {
    case 'full':
        return { messages, model: tier.model };

    case 'summarize':
        const summary = await summarizeOldMessages(messages);
        return { messages: [summary, ...recentMessages(messages)], model: tier.model };

    case 'rag':
        const relevant = await retrieveRelevant(messages);
        return { messages: [...relevant, ...recentMessages(messages)], model: tier.model };
}

}

Serial Position Optimization

Place important content at start and end

When to use: Constructing prompts with significant context

// LLMs weight beginning and end more heavily // Structure prompts to leverage this

function buildOptimalPrompt(components: { systemPrompt: string; criticalContext: string; conversationHistory: Message[]; currentQuery: string; }): string { // START: System instructions (always first) const parts = [components.systemPrompt];

// CRITICAL CONTEXT: Right after system (high primacy)
if (components.criticalContext) {
    parts.push(`## Key Context\n${components.criticalContext}`);
}

// MIDDLE: Conversation history (lower weight)
// Summarize if long, keep recent messages full
const history = components.conversationHistory;
if (history.length > 10) {
    const oldSummary = summarize(history.slice(0, -5));
    const recent = history.slice(-5);
    parts.push(`## Earlier Conversation (Summary)\n${oldSummary}`);
    parts.push(`## Recent Messages\n${formatMessages(recent)}`);
} else {
    parts.push(`## Conversation\n${formatMessages(history)}`);
}

// END: Current query (high recency)
// Restate critical requirements here
parts.push(`## Current Request\n${components.currentQuery}`);

// FINAL: Reminder of key constraints
parts.push(`Remember: ${extractKeyConstraints(components.systemPrompt)}`);

return parts.join('\n\n');

}

Intelligent Summarization

Summarize by importance, not just recency

When to use: Context exceeds optimal size

interface MessageWithMetadata extends Message { importance: number; // 0-1 score hasCriticalInfo: boolean; // User preferences, decisions referenced: boolean; // Was this referenced later? }

async function smartSummarize( messages: MessageWithMetadata[], targetTokens: number ): Message[] { // Sort by importance, preserve order for tied scores const sorted = [...messages].sort((a, b) => (b.importance + (b.hasCriticalInfo ? 0.5 : 0) + (b.referenced ? 0.3 : 0)) - (a.importance + (a.hasCriticalInfo ? 0.5 : 0) + (a.referenced ? 0.3 : 0)) );

const keep: Message[] = [];
const summarizePool: Message[] = [];
let currentTokens = 0;

for (const msg of sorted) {
    const msgTokens = await countTokens([msg]);
    if (currentTokens + msgTokens < targetTokens * 0.7) {
        keep.push(msg);
        currentTokens += msgTokens;
    } else {
        summarizePool.push(msg);
    }
}

// Summarize the low-importance messages
if (summarizePool.length > 0) {
    const summary = await llm.complete(`
        Summarize these messages, preserving:
        - Any user preferences or decisions
        - Key facts that might be referenced later
        - The overall flow of conversation

        Messages:
        ${formatMessages(summarizePool)}
    `);

    keep.unshift({ role: 'system', content: `[Earlier context: ${summary}]` });
}

// Restore original order
return keep.sort((a, b) => a.timestamp - b.timestamp);

}

Token Budget Allocation

Allocate token budget across context components

When to use: Need predictable context management

interface TokenBudget { system: number; // System prompt criticalContext: number; // User prefs, key info history: number; // Conversation history query: number; // Current query response: number; // Reserved for response }

function allocateBudget(totalTokens: number): TokenBudget { return { system: Math.floor(totalTokens * 0.10), // 10% criticalContext: Math.floor(totalTokens * 0.15), // 15% history: Math.floor(totalTokens * 0.40), // 40% query: Math.floor(totalTokens * 0.10), // 10% response: Math.floor(totalTokens * 0.25), // 25% }; }

async function buildWithBudget( components: ContextComponents, modelMaxTokens: number ): PreparedContext { const budget = allocateBudget(modelMaxTokens);

// Truncate/summarize each component to fit budget
const prepared = {
    system: truncateToTokens(components.system, budget.system),
    criticalContext: truncateToTokens(
        components.criticalContext, budget.criticalContext
    ),
    history: await summarizeToTokens(components.history, budget.history),
    query: truncateToTokens(components.query, budget.query),
};

// Reallocate unused budget
const used = await countTokens(Object.values(prepared).join('\n'));
const remaining = modelMaxTokens - used - budget.response;

if (remaining > 0) {
    // Give extra to history (most valuable for conversation)
    prepared.history = await summarizeToTokens(
        components.history,
        budget.history + remaining
    );
}

return prepared;

}

Validation Checks

No Token Counting

Severity: WARNING

Message: Building context without token counting. May exceed model limits.

Fix action: Count tokens before sending, implement budget allocation

Naive Message Truncation

Severity: WARNING

Message: Truncating messages without summarization. Critical context may be lost.

Fix action: Summarize old messages instead of simply removing them

Hardcoded Token Limit

Severity: INFO

Message: Hardcoded token limit. Consider making configurable per model.

Fix action: Use model-specific limits from configuration

No Context Management Strategy

Severity: WARNING

Message: LLM calls without context management strategy.

Fix action: Implement context management: budgets, summarization, or RAG

Collaboration

Delegation Triggers

• retrieval|rag|search -> rag-implementation (Need retrieval system)
• memory|persistence|remember -> conversation-memory (Need memory storage)
• cache|caching -> prompt-caching (Need caching optimization)

Complete Context System

Skills: context-window-management, rag-implementation, conversation-memory, prompt-caching

Workflow:

1. Design context strategy
2. Implement RAG for large corpuses
3. Set up memory persistence
4. Add caching for performance

Related Skills

Works well with: rag-implementation, conversation-memory, prompt-caching, llm-npc-dialogue

When to Use

• User mentions or implies: context window
• User mentions or implies: token limit
• User mentions or implies: context management
• User mentions or implies: context engineering
• User mentions or implies: long context
• User mentions or implies: context overflow

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.