Awesome Reviewers

When you describe an algorithm (retrieval pipeline, scoring, ranking, etc.), ensure the implementation includes the complete promised workflow and produces deterministic outputs.

Apply this checklist:

Example pattern (scoring determinism):

from dataclasses import dataclass

@dataclass(frozen=True)
class Checks:
    ssh_root_login: bool
    open_ports: tuple[int, ...]   # ensure stable ordering
    updates_installed: bool

def score(checks: Checks) -> int:
    # Deterministic, explicit per-check points
    points = 0
    points += 25 if not checks.ssh_root_login else 0
    points += 25 if len(checks.open_ports) == 0 else max(0, 25 - 5*len(checks.open_ports))
    points += 20 if checks.updates_installed else 0
    # ... add the remaining dimensions with explicit point math
    return max(0, min(100, points))

def collect_read_only() -> Checks:
    # Only read system state; normalize ordering; no mutations
    open_ports = tuple(sorted(_read_open_ports()))
    return Checks(
        ssh_root_login=_read_ssh_root_login(),
        open_ports=open_ports,
        updates_installed=_read_updates_installed(),
    )

For retrieval workflows, use the same idea: document and implement every required step (index build, pass 1 retrieval, pass 2 expansion/retrieval, result merge, and the call site), or explicitly limit the documentation to the implemented subset.