Token Pilot

Token Pilot

Token-efficient AI coding, enforced. Cuts context consumption in AI coding assistants by up to 90% without changing the way you work.

Why it matters more now: as frontier models move up in price, the tokens you don't spend reading code are worth more, not less. The savings are in tokens; the value is in tokens × price. Token Pilot keeps the expensive main thread lean so the premium model spends its budget on reasoning, not on re-reading files.

Two layers, each useful on its own, stronger together:

1. MCP tools — structural reads (smart_read, read_symbol, read_for_edit, …). Ask for an outline or load one function by name instead of the whole file.
2. PreToolUse hooks — intercept heavy native tool calls (Read on large files, recursive Grep, unbounded git diff) and redirect to token-efficient alternatives.

How It Works

Traditional:  Read("user-service.ts")  →  500 lines  →  ~3000 tokens
Token Pilot:  smart_read("user-service.ts")  →  15-line outline  →  ~200 tokens
              read_symbol("UserService.updateUser")  →  45 lines  →  ~350 tokens
              After edit: read_diff("user-service.ts")  →  ~20 tokens

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View the full README on GitHub