context-optimizer
暂无简介
- License 许可
- MIT
- 最近更新
- 2026-04-05
- Stars
- ★ 7
- Forks
- 1
02 资源文件 2
- 脚本context-optimizer.js6.5 KB
- 参考文档README.md2.7 KB
03 SKILL.md 正文
Context Pruner / Context Optimizer
Advanced context management for DeepSeek's 64k token limit
Overview
A skill module for Clawdbot that provides intelligent pruning, compression, and token optimization to prevent context overflow while preserving important information.
Primary Components
Core Features
- Multiple compaction strategies: semantic, temporal, extractive, adaptive
- Query-aware relevance scoring
- Hierarchical memory with archive retrieval
- Real-time context health monitoring
- Chat event logging
Architecture
The tool implements a two-tier memory system:
| Tier | Capacity | Speed | Behavior |
|---|---|---|---|
| Current Context (RAM) | 64k tokens | Fast | Auto-compacted |
| Archive (Storage) | 100MB | Slower | Searchable |
Pruning Strategies
1. Semantic Pruning
Removes semantically similar/duplicate messages using embeddings.
2. Temporal Pruning
Removes older messages while preserving recent conversation.
3. Extractive Pruning
Summarizes message groups to reduce token count.
4. Adaptive Pruning
Automatically selects strategy based on context usage level.
Configuration
context_optimizer:
context_limit: 64000
auto_prune: true
thresholds:
warning: 0.70 # 70% - show warning
prune: 0.80 # 80% - trigger pruning
emergency: 0.95 # 95% - aggressive pruning
relevance_decay: 0.05
batch_size: 5
max_compaction_ratio: 0.50
archive_search_limit: 10
Health Status Levels
| Status | Usage | Action |
|---|---|---|
| Healthy | < 70% | Normal operation |
| Warning | 70-80% | Alert user |
| Prune | 80-95% | Auto-compact |
| Emergency | > 95% | Aggressive pruning |
Installation
npm install context-optimizer
# Or via ClawHub
clawdhub install context-optimizer
Dependencies
- tiktoken
- @xenova/transformers
Usage
const { ContextOptimizer } = require('context-optimizer');
const optimizer = new ContextOptimizer({
contextLimit: 64000,
strategy: 'adaptive'
});
// Check health
const health = optimizer.getHealth();
// Prune if needed
if (health.status === 'prune') {
const pruned = await optimizer.prune(messages);
}
CLI Usage
# Check status
context-optimizer status
# Test pruning
context-optimizer test --file conversation.json
# Process file
context-optimizer process --input chat.json --output optimized.json
Chat Logging Format
? Context optimized: Compacted 15 messages → 8
License
MIT
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