context-window-management

name	context-window-management
description	ALWAYS use this when the request matches Context Window Management: Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot

Context Window Management

Selective Reading Rule

Start with:

• references/senior-master-standard.md
• references/usage-routing.md
• references/quality-checklist.md

Then load only the inherited docs, scripts, assets, or examples that match the user's actual task.

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.