# Bug-Hunt Audit Lane: audit-08

Round: `bughunt-p-limit-tempo-20260514T145029Z`
Repo: `p-limit`
Instruction mode: `m1nd-temponizer-full`
Workspace: `/Users/kle1nz/m1nd/.m1nd-field-workspaces/bughunt-p-limit-tempo-20260514T145029Z/audit-08/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 + Temponizer Full-Spec Mode

Use the trained-agent m1nd loop and the full Temponizer recalibration model.

m1nd trained 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.

Temponizer full spec:

Before every major investigation move, classify phase `phi`: `GEN`, `IO`, `DBG`, or `PAR`.
For any action that feels long, name the inherited human-duration estimate `Tp`, then compute corrected agent time: `Tc = alpha(phi) * Tp`.
Initial alpha priors: `GEN=0.03`, `IO=0.10`, `DBG=0.08`, `PAR=0.05`.
Act on `Tc`, not `Tp`.
Your real constraints are tokens, context window, tool latency, compute, permissions, and I/O.
Your real constraints are not fatigue, boredom, attention decay, human schedule, or manual typing cost.
After each phase, record measured `Te`. If `Te` diverges from `Tc`, update the local alpha used for the next similar phase.
Use this loop to decide whether to keep searching, run a focused probe, run broad tests, iterate, abandon a line, parallelize independent reads/probes, or stop when proof is enough.

Record `temponizer_usage` with at least: `phase`, `tp_estimate`, `alpha`, `tc_estimate`, `te_measured`, `decision`, and `recalibration_note` where measurable.


## Runtime Probe Note

The lane workspace already contains the runtime dependency needed for focused `node --input-type=module` probes. Full `npm install` or full `npm test` may be network-dependent and is not required for this audit. Prefer small behavior probes when they prove a finding.

## Required Output

Write your final JSON result to `/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-p-limit-tempo-20260514T145029Z/lane-results/audit-08.json`.
Append investigation events to `/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-p-limit-tempo-20260514T145029Z/event-streams/audit-08.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.
