There is no 'never comment' school — only 'comment by exception' (Uncle Bob) versus 'comments are part of the design' (Ousterhout, antirez). In their 2024–2025 public debate, both agree on why-comments and against noise comments; the real fights are over interface comments for internal methods, long names as a substitute, and whether comments can be trusted. Controlled studies say quality decides: the same comments moved performance anywhere from -30% to +34% depending on the snippet.
Factory breaks 'can an agent work in this repo' into eight pillars and five levels, and published real scores: CockroachDB L4 (74%), FastAPI L3 (53%), Express L2 (28%). The thesis is that agent readiness approximates the density of deterministic validation loops — linters, type checkers, tests are reward signals for agents.
Google deployed AutoCommenter to tens of thousands of engineers and published the whole tuning process: suppressing 17 'technically correct but low-value' rules raised the useful ratio from 54% to 66%, with 80% set as the bar for the next rollout stage. Final comment-resolution rate landed around 40%. The bottleneck in AI code review was never detection — it's volume.
Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.