# Bug-Hunt Audit Lane: audit-05

Round: `bughunt-p-limit-fullspec-20260514T170336Z`
Repo: `p-limit`
Instruction mode: `m1nd-trained`
Workspace: `/Users/kle1nz/m1nd/.m1nd-field-workspaces/bughunt-p-limit-fullspec-20260514T170336Z/audit-05/p-limit`

Work as if this is a real production-minded dependency audit.
Do not guess the benchmark hypothesis, bug count, or comparison arm.
Find real behavioral defects, edge-case regressions, missing tests, or contract mismatches.
Do not patch files. Do not read `operator-only/` artifacts.

## m1nd-Trained Operating Loop

Use the trained-agent m1nd loop:

1. Establish trust with `trust_selftest`, or `session_handshake` scoped to this repo.
2. If trust is not full, follow `recovery_playbook` before interpreting empty retrieval.
3. Treat `wrong_workspace_binding` as binding/scope state, not stale graph truth.
4. Orient with `audit`, then use `search`, `seek`, or `activate` for focused discovery.
5. Read runtime envelopes before trusting empty results.
6. Verify final truth with source reads, focused probes, tests, or compiler/runtime output.
7. Use `impact`, `validate_plan`, or `surgical_context_v2` when a finding needs connected proof.
8. If using local `probe_m1nd.py` in this benchmark workspace, pass `--no-worktree-artifacts` unless intentionally debugging runtime sidecar state.
9. Record m1nd calls, recovery path, files inspected, commands run, and fallback reasons.

## Required Output

Write your final JSON result to `/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-p-limit-fullspec-20260514T170336Z/lane-results/audit-05.json`.
Append investigation events to `/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-p-limit-fullspec-20260514T170336Z/event-streams/audit-05.jsonl` using `event_source="agent"`.
Use the schema in `lane-result-template.json`.

Findings should include title, severity, file, symbol, cause, impact, evidence, reproduction_or_test, and confidence.
Extra findings are welcome, but they must be concrete and source-backed.
