Awesome Reviewers

When code builds prompts, traverses trees, calls external APIs, or processes many items, make performance predictable by enforcing budgets, preventing pathological inputs, and removing redundant work.

Apply these rules: 1) Short-circuit expensive work at global caps

• If there’s a per-request/per-PR budget (tokens, items, characters), stop as soon as the combined total hits the cap.
• Also avoid constructing API clients/workflows when you already know the cap is reached.

Example pattern:

# Assume MAX_ITEMS and items already collected
if len(items) >= MAX_ITEMS:
    return items  # do not build clients or call more APIs

for candidate in candidates:
    if len(items) >= MAX_ITEMS:
        break
    items.append(candidate)

2) Guard resource usage with hard limits

• Before reading/parsing/adding content, estimate size and skip inputs that are too large (with a warning log).

3) Reuse fetched data; avoid redundant API calls

• If you already call get_comments() once, don’t call get_comment(comment_id) again—filter from the in-memory list.

4) Avoid repeated expensive lookups and unnecessary allocations

• Cache settings = get_settings() in a local variable when used multiple times.
• Reuse existing provider instances rather than creating new ones inside loops.

5) Prefer O(1) lookups with the right data structures

• Replace repeated list.index() inside loops with a dict map.

Net effect: fewer external calls, less parsing/IO, bounded memory, and deterministic latency under worst-case inputs.

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