Shared memory and context tools for agentic work.
Code Rooms
# Bug-Hunt Audit Lane: audit-02
Round: `bughunt-p-limit-confirm-20260514T204651Z`
Repo: `p-limit`
Instruction mode: `m1nd-temponizer-compact`
Workspace: `/Users/kle1nz/m1nd/.m1nd-field-workspaces/bughunt-p-limit-confirm-20260514T204651Z/audit-02/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 Compact Mode
Use the trained-agent m1nd loop and the compact Temponizer 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.
8. If using local `probe_m1nd.py` in this benchmark workspace, pass `--no-worktree-artifacts` unless intentionally debugging runtime sidecar state.
Temponizer compact model:
- For major decisions only, classify phase `phi`: `GEN`, `IO`, `DBG`, or `PAR`.
- When an action feels long, name inherited human-time `Tp` and 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` and real agent constraints: tokens, context, tool latency, compute, permissions, and I/O.
- Do not optimize for human fatigue, boredom, attention decay, typing cost, or calendar intuition.
- Record `Te` only for meaningful branch decisions, broad probes, focused probes, and stopping decisions.
- Keep the audit moving; temporal calibration should reduce hesitation, not become paperwork.
Record compact `temponizer_usage` entries with: `phase`, `tc_estimate`, `te_measured` if known, `decision`, and `recalibration_note`.
## Required Output
Write your final JSON result to `/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-p-limit-confirm-20260514T204651Z/lane-results/audit-02.json`.
Append investigation events to `/Users/kle1nz/m1nd/docs/benchmarks/bug-hunt-rounds/bughunt-p-limit-confirm-20260514T204651Z/event-streams/audit-02.jsonl` using `event_source="agent"`.
Every event must include `schema`, `round_id`, `lane_id`, `event_source`, `event_type`, and `created_at`.
Record at least `audit_started`, one first-discovery event such as `findings_identified`, `focused_probes`, or `runtime_probe`, and `result_written`.
Use ISO timestamps; do not use `ts` or `event` as substitutes in new rounds.
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.