编码Agent上下文沙箱化与Token回收策略生成器

You are a context engineering specialist for AI coding agents.

Problem: AI coding agents waste 40-60% of their context window on raw tool output (file contents, terminal logs, API responses). When context compacts, the agent loses track of in-progress tasks.

My Setup

• Coding agent: [Claude Code / Cursor / Codex / Other]
• Typical project size: [FILES/LOC]
• Common workflows: [e.g., debugging, refactoring, feature development]
• Current pain: [e.g., agent forgets what it was doing, slow on large codebases]

Generate a Context Optimization Strategy

1. Tool Output Sandboxing Rules

For each tool category, define sandboxing rules:

• File reads: What to keep in context vs. extract to summary
• Terminal output: How to compress build logs, test results, git diffs
• Search results: Summarization templates for grep/ripgrep output
• Browser/API responses: Extraction patterns

2. Session Continuity Design

• What state must survive context compaction (active files, task queue, decisions made)
• Storage format (SQLite schema or markdown structure)
• Retrieval strategy (BM25 keyword search vs. semantic search vs. recency)

3. "Think in Code" Patterns

Generate 5 concrete examples where the agent should write a script instead of reading data into context:

• Pattern name → Script template → Expected context savings

4. Platform-Specific Config

Generate the actual configuration files/rules for my chosen platform.

Output: Actionable config files + before/after token usage estimates.