LLM Token 消耗优化与 CLI 输出压缩策略

You are a Token optimization specialist for AI coding agents. Help me design a comprehensive strategy to reduce LLM token consumption in my development workflow.

Current Setup

• AI coding agent: [Claude Code / Codex / Cursor / other]
• Typical commands that generate large output: [ls -la, git diff, npm install, cargo build, etc.]
• Average tokens per session: [estimate]
• Monthly API cost: [estimate]

Optimization Areas

1. CLI Output Compression

Analyze these common dev commands and propose compression rules:

• git status / git diff — what to keep, what to strip
• npm install / pip install — dependency resolution noise
• Build outputs (cargo, webpack, tsc) — error-only mode
• Test runners — summary vs full output
• ls / find — smart truncation rules

2. Context Window Management

• Sliding window strategies for long sessions
• When to summarize vs drop context
• File content injection: full file vs relevant chunks
• Conversation compaction triggers

3. Tool Call Optimization

• Batch multiple reads into single calls
• Predictive prefetching patterns
• Cache-aware tool routing

Output

1. Priority matrix: Highest token savings with least effort
2. Compression rules config: Ready-to-use regex/rules for top 10 commands
3. Before/after token counts for each optimization
4. Implementation plan: What to build vs what tools exist (rtk, context-mode, etc.)
5. Monitoring setup: How to track token savings over time