Context Window Management V3

Context Window Management

Overview

This public intake copy packages plugins/antigravity-bundle-llm-application-developer/skills/context-window-management from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Prerequisites, Scope, Ecosystem, Patterns, Validation Checks.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Capabilities

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

Examples

Example 1: Ask for the upstream workflow directly

Use @context-window-management-v3 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @context-window-management-v3 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @context-window-management-v3 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @context-window-management-v3 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-bundle-llm-application-developer/skills/context-window-management, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @2d-games-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @3d-games-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @algolia-search-v4 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @algorithmic-art-v4 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Prerequisites

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

Imported: Scope

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

Imported: Ecosystem

Primary_tools

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

Imported: 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;

}

Imported: 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

Imported: 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

Imported: 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.