Digital-Threads/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 — Claude's Fable 5 is the most capable (and most expensive-per-token) tier yet — 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.

Three 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.
3. tp-* subagents — Claude Code delegates with MCP-first behaviour and tight response budgets.

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

Files under 200 lines are returned in full — zero overhead for small files.

Benchmarks

Measured on public open-source repos. Files ≥50 lines only:

| Repo | Files | Raw Tokens | Outline Tokens | Savings | |------|------:|----------:|--------------:|--------:| | token-pilot (TS) | 55 | 102,086 | 8,992 | 91% | | express (JS) | 6 | 14,421 | 193 | 99% | | fastify (JS) | 23 | 50,000 | 3,161 | 94% | | flask (Python) | 20 | 78,236 | 7,418 | *