Tencent SkillHub · Communication & Collaboration

Context Optimizer

Advanced context management with auto-compaction and dynamic context optimization for DeepSeek's 64k context window. Features intelligent compaction (merging, summarizing, extracting), query-aware relevance scoring, and hierarchical memory system with context archive. Logs optimization events to chat.

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High Signal

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Quick setup

Download the package from Yavira.
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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: chat-logger.js, SUMMARY.md, INSTALL.md, README.md, package.json, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ad2546
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 10 sections Open source page

Context Pruner

Advanced context management optimized for DeepSeek's 64k context window. Provides intelligent pruning, compression, and token optimization to prevent context overflow while preserving important information.

Key Features

DeepSeek-optimized: Specifically tuned for 64k context window 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

Auto-compaction with dynamic context:

import { createContextPruner } from './lib/index.js'; const pruner = createContextPruner({ contextLimit: 64000, // DeepSeek's 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):

// 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

Semantic Compaction: Merges similar messages instead of removing them Temporal Compaction: Summarizes older conversations by time windows Extractive Compaction: Extracts key information from verbose messages Adaptive Compaction: Chooses best strategy based on message characteristics 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 (64k 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

{ contextLimit: 64000, // DeepSeek's context window 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: // 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: 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 DeepSeek's 64k limit.

Category context

Messaging, meetings, inboxes, CRM, and teammate communication surfaces.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Docs1 Scripts1 Config

SKILL.md Primary doc
INSTALL.md Docs
README.md Docs
SUMMARY.md Docs
chat-logger.js Scripts
package.json Config