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When implementing non-trivial logic (token estimation, parsing, normalization, cap/policy decisions, diff/tree composition), first write down the exact semantics/invariants the algorithm must satisfy, then implement and test against “tricky shapes” that commonly break those semantics.

Practical checklist:

  1. Specify the invariant in code comments near the logic (e.g., “image token cost must be per-image flat and independent of base64 length”; “disabled takes precedence over enabled:false”; “reasoning-only stop is not a completion”).
  2. Align transformation outputs with the external system’s semantics (e.g., gitlink/submodule handling should mirror how git previews it).
  3. Apply normalization recursively and at every relevant branch (objects, array items, combinators like anyOf/oneOf/allOf), not just the top level.
  4. Encode policy with clear cap/floor intent (e.g., a default fraction should only raise a floor, never accidentally reduce allowances), and then rely on existing clamps.
  5. Add regression tests that cover at least:
    • one edge-case shape (nested fields / inline scalars / nested schema branches), and
    • one adversarial example that breaks naive implementations (payload-length-based estimates, double-counting reasoning tokens, degenerate repetition loops).

Example (token estimation invariant):

// Invariant: per-image cost is flat (independent of encoded base64 payload length)
const estimate = (data: string) => estimateRequestInputTokens({
  messages: [{ role: "user", content: [{ type: "image", data, mediaType: "image/png" }] }],
});
const withoutImage = estimateRequestInputTokens({ messages: [{ role: "user", content: [] }] });
const perImage = estimate("A");
const perImageOnly = perImage - withoutImage;
expect(estimate("A".repeat(500_000)) - withoutImage).toBeCloseTo(perImageOnly, 50);

This standard reduces subtle correctness bugs by making the intended meaning explicit, implementing precisely to that meaning, and preventing regressions with targeted “shape/semantics” tests.

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