# Correctness and Regression Audit

You are being dropped into a real maintenance audit for a Python text-formatting utility.

Repository path:
/Users/kle1nz/m1nd/.m1nd-field-workspaces/bughunt-humanize-20260514T021500Z/audit-06/humanize

Task:
Find concrete correctness bugs, regression risks, boundary mistakes, data-preservation issues, and missing tests in this codebase. Work as if this report will be used by maintainers to decide what to fix before the next release. Do not assume a fixed number of issues. Do not make broad rewrites. If you produce patches, keep them local and minimal, but a high-quality audit report is the main deliverable.

Focus on evidence. A good finding includes file, function/class, cause, impact, reproduction idea or focused test, and why it matters. Avoid style-only comments. Avoid vague risk claims without a concrete code path.

Write your result JSON to:
/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-humanize-20260514T021500Z/lane-results/audit-06.json

Append raw investigation events as JSONL to:
/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-humanize-20260514T021500Z/event-streams/audit-06.jsonl

Use this result shape:
{
  "schema": "m1nd-bug-hunt-audit-result-v0",
  "round_id": "bughunt-humanize-20260514T021500Z",
  "lane_id": "audit-06",
  "repo_path": "/Users/kle1nz/m1nd/.m1nd-field-workspaces/bughunt-humanize-20260514T021500Z/audit-06/humanize",
  "findings": [
    {
      "title": "short specific defect title",
      "severity": "critical|high|medium|low",
      "file": "relative/path.py",
      "symbol": "function or class",
      "cause": "what is wrong",
      "impact": "what breaks or weakens",
      "evidence": ["file/function/test/proof references"],
      "reproduction_or_test": "focused test or command",
      "confidence": "high|medium|low"
    }
  ],
  "commands_run": [],
  "files_inspected": [],
  "tests_run": [],
  "m1nd_usage": [],
  "final_notes": "limits and non-claims",
  "agent_testimony": "how you worked and what helped or hurt",
  "confidence": "high|medium|low"
}

Event JSONL examples:
{"event_type":"file_read","event_source":"agent","detail":"read src/humanize/number.py"}
{"event_type":"finding","event_source":"agent","detail":"identified exact boundary regression"}
{"event_type":"test_run","event_source":"agent","detail":"uv run --extra tests pytest -q returned ..."}

Operational note:
m1nd is available. Use it first where it helps with orientation, localization, connected context, impact, or recovery. If it is stale, blocked, or unavailable, record what happened and verify final truth with repo files and tests.
