Skill: quincygunter/qui-context-optimizer (version 1.0.0)

--- name: context-optimizer description: Advanced context management with auto-compaction and dynamic context optimization for use with SkillBoss API Hub LLM services via /v1/pilot. Features intelligent compaction (merging, summarizing, extracting), query-aware relevance scoring, and hierarchical memory system with context archive. Logs optimization events to chat. homepage: https://github.com/clawdbot/clawdbot metadata: clawdbot: emoji: "🧠" requires: bins: [] npm: ["tiktoken", "@xenova/transformers"] env: ["SKILLBOSS_API_KEY"] install: - id: npm kind: npm label: Install Context Pruner dependencies command: "cd ~/.clawdbot/skills/context-pruner && npm install" --- # Context Pruner Advanced context management optimized for use with SkillBoss API Hub LLM services. Provides intelligent pruning, compression, and token optimization to prevent context overflow while preserving important information. Works with any LLM accessed via SkillBoss API Hub's `/v1/pilot` endpoint. ## Key Features - **SkillBoss API Hub compatible**: Works with any LLM routed through `/v1/pilot` - **Adaptive pruning**: Multiple strategies based on context usage - **Semantic deduplication**: Removes redundant information - **Priority-aware**: Preserves high-value messages - **Token-efficient**: Minimizes token overhead - **Real-time monitoring**: Continuous context health tracking ## Quick Start ### Auto-compaction with dynamic context: ```javascript import { createContextPruner } from './lib/index.js'; const pruner = createContextPruner({ contextLimit: 64000, // Configurable context limit autoCompact: true, // Enable automatic compaction dynamicContext: true, // Enable dynamic relevance-based context strategies: ['semantic', 'temporal', 'extractive', 'adaptive'], queryAwareCompaction: true, // Compact based on current query relevance }); await pruner.initialize(); // Process messages with auto-compaction and dynamic context const processed = await pruner.processMessages(messages, currentQuery); // Get context health status const status = pruner.getStatus(); console.log(`Context health: ${status.health}, Relevance scores: ${status.relevanceScores}`); // Manual compaction when needed const compacted = await pruner.autoCompact(messages, currentQuery); ``` ### Archive Retrieval (Hierarchical Memory): ```javascript // When something isn't in current context, search archive const archiveResult = await pruner.retrieveFromArchive('query about previous conversation', { maxContextTokens: 1000, minRelevance: 0.4, }); if (archiveResult.found) { // Add relevant snippets to current context const archiveContext = archiveResult.snippets.join('\n\n'); // Use archiveContext in your prompt console.log(`Found ${archiveResult.sources.length} relevant sources`); console.log(`Retrieved ${archiveResult.totalTokens} tokens from archive`); } ``` ## Auto-Compaction Strategies 1. **Semantic Compaction**: Merges similar messages instead of removing them 2. **Temporal Compaction**: Summarizes older conversations by time windows 3. **Extractive Compaction**: Extracts key information from verbose messages 4. **Adaptive Compaction**: Chooses best strategy based on message characteristics 5. **Dynamic Context**: Filters messages based on relevance to current query ## Dynamic Context Management - **Query-aware Relevance**: Scores messages based on similarity to current query - **Relevance Decay**: Relevance scores decay over time for older conversations - **Adaptive Filtering**: Automatically filters low-relevance messages - **Priority Integration**: Combines message priority with semantic relevance ## Hierarchical Memory System The context archive provides a RAM vs Storage approach: - **Current Context (RAM)**: Limited (configurable tokens), fast access, auto-compacted - **Archive (Storage)**: Larger (100MB), slower but searchable - **Smart Retrieval**: When information isn't in current context, efficiently search archive - **Selective Loading**: Extract only relevant snippets, not entire documents - **Automatic Storage**: Compacted content automatically stored in archive ## Configuration ```javascript { contextLimit: 64000, // Configurable context window size autoCompact: true, // Enable automatic compaction compactThreshold: 0.75, // Start compacting at 75% usage aggressiveCompactThreshold: 0.9, // Aggressive compaction at 90% dynamicContext: true, // Enable dynamic context management relevanceDecay: 0.95, // Relevance decays 5% per time step minRelevanceScore: 0.3, // Minimum relevance to keep queryAwareCompaction: true, // Compact based on current query relevance strategies: ['semantic', 'temporal', 'extractive', 'adaptive'], preserveRecent: 10, // Always keep last N messages preserveSystem: true, // Always keep system messages minSimilarity: 0.85, // Semantic similarity threshold // Archive settings enableArchive: true, // Enable hierarchical memory system archivePath: './context-archive', archiveSearchLimit: 10, archiveMaxSize: 100 * 1024 * 1024, // 100MB archiveIndexing: true, // Chat logging logToChat: true, // Log optimization events to chat chatLogLevel: 'brief', // 'brief', 'detailed', or 'none' chatLogFormat: '📊 {action}: {details}', // Format for chat messages // Performance batchSize: 5, // Messages to process in batch maxCompactionRatio: 0.5, // Maximum 50% compaction in one pass } ``` ## Chat Logging The context optimizer can log events directly to chat: ```javascript // Example chat log messages: // 📊 Context optimized: Compacted 15 messages → 8 (47% reduction) // 📊 Archive search: Found 3 relevant snippets (42% similarity) // 📊 Dynamic context: Filtered 12 low-relevance messages // Configure logging: const pruner = createContextPruner({ logToChat: true, chatLogLevel: 'brief', // Options: 'brief', 'detailed', 'none' chatLogFormat: '📊 {action}: {details}', // Custom log handler (optional) onLog: (level, message, data) => { if (level === 'info' && data.action === 'compaction') { // Send to chat console.log(`🧠 Context optimized: ${message}`); } } }); ``` ## Integration with Clawdbot Add to your Clawdbot config: ```yaml skills: context-pruner: enabled: true config: contextLimit: 64000 autoPrune: true ``` The pruner will automatically monitor context usage and apply appropriate pruning strategies to stay within the configured context limit. LLM calls are routed through SkillBoss API Hub (`POST https://api.heybossai.com/v1/pilot`) using your `SKILLBOSS_API_KEY`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()