GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Schedule a 40-task build DAG on 3 workers with exclusive resources
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.06
- Wall time
- 1m
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Task: schedule a build `tasks.json` describes 40 build tasks: duration (minutes), dependencies, and resources each task holds while running. There are `workers` identical workers (each runs one task at a time) and resource limits (at most `db: 1` and `net: 2` tasks holding that resource concurrently). Tasks can't be preempted; a task may start only when all its deps have finished (start ≥ max dep end time). Produce `schedule.json`: {"t00": {"start": <int minute>, "worker": <0..workers-1>}, ...} for ALL tasks, minimising the makespan (time the last task finishes). Also write `plan_notes.md` with your makespan and the lower bound you computed (max of critical path and total work / workers). You may write code. No network.
Validator checks deps, worker overlap, resource limits; must match or beat critical-path list scheduling (makespan 98; lower bound 91). Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
Success criteria: 1. schedule.json must contain exactly the 40 tasks, each with a non-negative integer start and a valid worker index. 2. No task starts before all its dependencies finish. 3. No two tasks overlap on the same worker. 4. At no start time do concurrent tasks exceed the db (1) or net (2) resource limits. 5. Makespan <= the reference critical-path list-scheduling makespan.
Setup notes: Use Case Scout batch4 case d12-build-scheduler (offline fixture). Prepared with batch4/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR/key.json`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 1/1 checks. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 6 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.06
- Wall time
- 1m
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 145,894 | 1,910 | $0.06 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. schedule.json must contain exactly the 40 tasks, each with a non-negative integer start and a valid worker index. 2. No task starts before all its dependencies finish. 3. No two tasks overlap on the same worker. 4. At no start time do concurrent tasks exceed the db (1) or net (2) resource limits. 5. Makespan <= the reference critical-path list-scheduling makespan.
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"type": "thread.started", "thread_id": "01a1642b-2783-7240-9020-e17ea00ecffd"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the tasks, compute the lower bound, and search for a schedule, then validate its dependencies and resource usage."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la && cat tasks.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la && cat tasks.json'", "aggregated_output": "total 16\ndrwxr-xr-x 6 bob bob 4096 Oct 22 11:11 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 11:11 ..\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:11 .agents\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:11 .aws\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:11 .codex\ndr-xr-xr-x 2 bob bob 40 Oct 22 11:11 .git\n-rw-r--r-- 1 bob bob 734 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 3927 Oct 22 10:08 tasks.json\n{\n \"workers\": 3,\n \"resource_limits\": {\n \"db\": 1,\n \"net\": 2\n },\n \"tasks\": {\n \"t00\": {\n \"duration\": 5,\n \"deps\": [],\n \"resources\": [\n \"net\"\n ]\n },\n \"t01\": {\n \"duration\": 13,\n \"deps\": [\n \"t00\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t02\": {\n \"duration\": 8,\n \"deps\": [\n \"t01\"\n ],\n \"resources\": []\n },\n \"t03\": {\n \"duration\": 2,\n \"deps\": [],\n \"resources\": [\n \"db\"\n ]\n },\n \"t04\": {\n \"duration\": 8,\n \"deps\": [\n \"t00\",\n \"t01\",\n \"t02\"\n ],\n \"resources\": []\n },\n \"t05\": {\n \"duration\": 2,\n \"deps\": [\n \"t02\"\n ],\n \"resources\": [\n \"db\"\n ]\n },\n \"t06\": {\n \"duration\": 2,\n \"deps\": [\n \"t04\"\n ],\n \"resources\": []\n },\n \"t07\": {\n \"duration\": 8,\n \"deps\": [\n \"t00\",\n \"t03\",\n \"t04\"\n ],\n \"resources\": []\n },\n \"t08\": {\n \"duration\": 13,\n \"deps\": [\n \"t00\",\n \"t07\"\n ],\n \"resources\": [\n \"db\"\n ]\n },\n \"t09\": {\n \"duration\": 3,\n \"deps\": [\n \"t01\",\n \"t08\"\n ],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t10\": {\n \"duration\": 8,\n \"deps\": [\n \"t00\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t11\": {\n \"duration\": 5,\n \"deps\": [\n \"t02\"\n ],\n \"resources\": []\n },\n \"t12\": {\n \"duration\": 2,\n \"deps\": [\n \"t06\"\n ],\n \"resources\": []\n },\n \"t13\": {\n \"duration\": 5,\n \"deps\": [\n \"t06\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t14\": {\n \"duration\": 2,\n \"deps\": [\n \"t03\",\n \"t05\",\n \"t08\"\n ],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t15\": {\n \"duration\": 2,\n \"deps\": [\n \"t11\"\n ],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t16\": {\n \"duration\": 13,\n \"deps\": [\n \"t11\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t17\": {\n \"duration\": 13,\n \"deps\": [\n \"t00\",\n \"t14\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t18\": {\n \"duration\": 3,\n \"deps\": [],\n \"resources\": [\n \"net\"\n ]\n },\n \"t19\": {\n \"duration\": 2,\n \"deps\": [],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t20\": {\n \"duration\": 13,\n \"deps\": [\n \"t08\"\n ],\n \"resources\": []\n },\n \"t21\": {\n \"duration\": 13,\n \"deps\": [\n \"t13\"\n ],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t22\": {\n \"duration\": 8,\n \"deps\": [\n \"t11\"\n ],\n \"resources\": []\n },\n \"t23\": {\n \"duration\": 2,\n \"deps\": [\n \"t02\",\n \"t12\",\n \"t15\"\n ],\n \"resources\": []\n },\n \"t24\": {\n \"duration\": 2,\n \"deps\": [\n \"t02\",\n \"t10\",\n \"t13\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t25\": {\n \"duration\": 3,\n \"deps\": [\n \"t04\",\n \"t06\"\n ],\n \"resources\": [\n \"db\"\n ]\n },\n \"t26\": {\n \"duration\": 2,\n \"deps\": [\n \"t25\"\n ],\n \"resources\": []\n },\n \"t27\": {\n \"duration\": 13,\n \"deps\": [\n \"t16\",\n \"t18\",\n \"t20\"\n ],\n \"resources\": []\n },\n \"t28\": {\n \"duration\": 2,\n \"deps\": [],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t29\": {\n \"duration\": 13,\n \"deps\": [\n \"t10\"\n ],\n \"resources\": [\n \"db\"\n ]\n },\n \"t30\": {\n \"duration\": 5,\n \"deps\": [\n \"t00\"\n ],\n \"resources\": []\n },\n \"t31\": {\n \"duration\": 5,\n \"deps\": [],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t32\": {\n \"duration\": 13,\n \"deps\": [],\n \"resources\": [\n \"db\",\n \"net\"\n ]\n },\n \"t33\": {\n \"duration\": 5,\n \"deps\": [\n \"t11\",\n \"t22\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t34\": {\n \"duration\": 13,\n \"deps\": [\n \"t16\"\n ],\n \"resources\": []\n },\n \"t35\": {\n \"duration\": 8,\n \"deps\": [],\n \"resources\": [\n \"db\"\n ]\n },\n \"t36\": {\n \"duration\": 5,\n \"deps\": [\n \"t09\",\n \"t11\",\n \"t34\"\n ],\n \"resources\": [\n \"db\"\n ]\n },\n \"t37\": {\n \"duration\": 8,\n \"deps\": [\n \"t10\",\n \"t11\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t38\": {\n \"duration\": 8,\n \"deps\": [\n \"t09\",\n \"t29\"\n ],\n \"resources\": [\n \"net\"\n ]\n },\n \"t39\": {\n \"duration\": 13,\n \"deps\": [\n \"t15\"\n ],\n \"resources\": []\n }\n }\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport importlib.util\nfor m in ['ortools','scipy','z3']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport importlib.util\nfor m in ['ortools','scipy','z3']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport importlib.util\nfor m in ['ortools','scipy','z3']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport importlib.util\nfor m in ['ortools','scipy','z3']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"", "aggregated_output": "ortools False\nscipy True\nz3 False\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json\nx=json.load(open('tasks.json'));e={}\nfor k,t in x['tasks'].items(): e[k]=t['duration']+max([e[d] for d in t['deps']]+[0])\nprint('CP',max(e.values()),'work',sum(t['duration'] for t in x['tasks'].values()))\nprint({r:sum(t['duration'] for t in x['tasks'].values() if r in t['resources']) for r in x['resource_limits']})\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json\nx=json.load(open('tasks.json'));e={}\nfor k,t in x['tasks'].items(): e[k]=t['duration']+max([e[d] for d in t['deps']]+[0])\nprint('CP',max(e.values()),'work',sum(t['duration'] for t in x['tasks'].values()))\nprint({r:sum(t['duration'] for t in x['tasks'].values() if r in t['resources']) for r in x['resource_limits']})\nPY\"", "aggregated_output": "CP 81 work 273\n{'db': 88, 'net': 125}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\nimport json, math, time\nimport numpy as np\nfrom scipy.optimize import milp, Bounds, LinearConstraint\nfrom scipy.sparse import coo_matrix\nx=json.load(open('tasks.json')); tasks=x['tasks']; names=list(tasks); W=x['workers']\nearliest={}\nfor k,t in tasks.items(): earliest[k]=max([earliest[d]+tasks[d]['duration'] for d in t['deps']]+[0])\ntail={}\nfor k in reversed(names): tail[k]=tasks[k]['duration']+max([tail[j] for j in names if k in tasks[j]['deps']]+[0])\nH=115\nvs=[(k,s) for k in names for s in range(earliest[k],H-tail[k]+1)]\nidx={k:[] for k in names}\nfor i,(k,s) in enumerate(vs):idx[k].append((i,s))\nn=len(vs)+1; C=n-1; rr=[];cc=[];vv=[];lb=[];ub=[]\ndef row(items,l,u):\n r=len(lb)\n for c,v in items:rr.append(r);cc.append(c);vv.append(v)\n lb.append(l);ub.append(u)\nfor k in names:\n row([(i,1) for i,s in idx[k]],1,1)\n for d in tasks[k]['deps']:\n row([(i,s) for i,s in idx[k]]+[(i,-s) for i,s in idx[d]],tasks[d]['duration'],np.inf)\n row([(i,s+tasks[k]['duration']) for i,s in idx[k]]+[(C,-1)],-np.inf,0)\nfor tm in range(H):\n active=[(i,1) for i,(k,s) in enumerate(vs) if s<=tm<s+tasks[k]['duration']]\n row(active,-np.inf,W)\n for r,cap in x['resource_limits'].items():row([(i,v) for i,v in active if r in tasks[vs[i][0]]['resources']],-np.inf,cap)\na=coo_matrix((vv,(rr,cc)),shape=(len(lb),n)).tocsc();cost=np.zeros(n);cost[C]=1\nprint('Variables',n,'constraints',len(lb),flush=True)\nres=milp(cost,integrality=np.ones(n),bounds=Bounds(np.zeros(n),np.array([1]*(n-1)+[H])),constraints=LinearConstraint(a,lb,ub),options={'time_limit':180,'mip_rel_gap':0})\nprint(res.message, 'objective',res.fun,'dual',getattr(res,'mip_dual_bound',None),flush=True)\nif res.x is not None:\n starts={k:s for i,(k,s) in enumerate(vs) if res.x[i]>.5};ends=[0]*W;schedule={}\n for k in sorted(names,key=lambda k:(starts[k],k)):\n w=next(w for w in range(W) if ends[w]<=starts[k]);schedule[k]={'start':starts[k],'worker':w};ends[w]=starts[k]+tasks[k]['duration']\n json.dump(dict(sorted(schedule.items())),open('schedule.json','w'),indent=2);open('solver_result.json','w').write(json.dumps({'makespan':res.fun,'dual_bound':res.mip_dual_bound,'message':res.message}))\nPY\nOPENBLAS_NUM_THREADS=1 python3 solve.py\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\nimport json, math, time\nimport numpy as np\nfrom scipy.optimize import milp, Bounds, LinearConstraint\nfrom scipy.sparse import coo_matrix\nx=json.load(open('tasks.json')); tasks=x['tasks']; names=list(tasks); W=x['workers']\nearliest={}\nfor k,t in tasks.items(): earliest[k]=max([earliest[d]+tasks[d]['duration'] for d in t['deps']]+[0])\ntail={}\nfor k in reversed(names): tail[k]=tasks[k]['duration']+max([tail[j] for j in names if k in tasks[j]['deps']]+[0])\nH=115\nvs=[(k,s) for k in names for s in range(earliest[k],H-tail[k]+1)]\nidx={k:[] for k in names}\nfor i,(k,s) in enumerate(vs):idx[k].append((i,s))\nn=len(vs)+1; C=n-1; rr=[];cc=[];vv=[];lb=[];ub=[]\ndef row(items,l,u):\n r=len(lb)\n for c,v in items:rr.append(r);cc.append(c);vv.append(v)\n lb.append(l);ub.append(u)\nfor k in names:\n row([(i,1) for i,s in idx[k]],1,1)\n for d in tasks[k]['deps']:\n row([(i,s) for i,s in idx[k]]+[(i,-s) for i,s in idx[d]],tasks[d]['duration'],np.inf)\n row([(i,s+tasks[k]['duration']) for i,s in idx[k]]+[(C,-1)],-np.inf,0)\nfor tm in range(H):\n active=[(i,1) for i,(k,s) in enumerate(vs) if s<=tm<s+tasks[k]['duration']]\n row(active,-np.inf,W)\n for r,cap in x['resource_limits'].items():row([(i,v) for i,v in active if r in tasks[vs[i][0]]['resources']],-np.inf,cap)\na=coo_matrix((vv,(rr,cc)),shape=(len(lb),n)).tocsc();cost=np.zeros(n);cost[C]=1\nprint('Variables',n,'constraints',len(lb),flush=True)\nres=milp(cost,integrality=np.ones(n),bounds=Bounds(np.zeros(n),np.array([1]*(n-1)+[H])),constraints=LinearConstraint(a,lb,ub),options={'time_limit':180,'mip_rel_gap':0})\nprint(res.message, 'objective',res.fun,'dual',getattr(res,'mip_dual_bound',None),flush=True)\nif res.x is not None:\n starts={k:s for i,(k,s) in enumerate(vs) if res.x[i]>.5};ends=[0]*W;schedule={}\n for k in sorted(names,key=lambda k:(starts[k],k)):\n w=next(w for w in range(W) if ends[w]<=starts[k]);schedule[k]={'start':starts[k],'worker':w};ends[w]=starts[k]+tasks[k]['duration']\n json.dump(dict(sorted(schedule.items())),open('schedule.json','w'),indent=2);open('solver_result.json','w').write(json.dumps({'makespan':res.fun,'dual_bound':res.mip_dual_bound,'message':res.message}))\nPY\nOPENBLAS_NUM_THREADS=1 python3 solve.py\"", "aggregated_output": "Variables 2670 constraints 478\nOptimization terminated successfully. (HiGHS Status 7: Optimal) objective 95.0 dual 95.0\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "The solver found a 95-minute schedule and proved it optimal. The requested lower bound is 91 minutes: the critical path is 81 minutes, and 273 worker-minutes divided by 3 workers is 91. I\u2019ll check the saved schedule for dependency, worker, and resource conflicts."}}
{"type": "item.started", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc \"cat > validate.py <<'PY'\nimport json\nx=json.load(open('tasks.json'));s=json.load(open('schedule.json'));tasks=x['tasks'];W=x['workers']\nassert set(s)==set(tasks)\nfor k,v in s.items():\n assert type(v['start']) is int and v['start']>=0\n assert type(v['worker']) is int and 0<=v['worker']<W\n for d in tasks[k]['deps']:assert v['start']>=s[d]['start']+tasks[d]['duration'],(k,d)\nM=max(s[k]['start']+tasks[k]['duration'] for k in tasks)\nfor tm in range(M):\n active=[k for k in tasks if s[k]['start']<=tm<s[k]['start']+tasks[k]['duration']]\n assert len({s[k]['worker'] for k in active})==len(active),(tm,active)\n for r,cap in x['resource_limits'].items():assert sum(r in tasks[k]['resources'] for k in active)<=cap,(tm,r)\nprint(f'Validated all {len(tasks)} tasks: makespan {M}; all dependencies, workers, and resource limits satisfied.')\nPY\npython3 validate.py\ncat > plan_notes.md <<'EOF'\nMakespan: **95 minutes**, proven optimal by integer optimization (HiGHS); the solver's final lower bound was also 95 minutes.\n\nRequested lower bound: **91 minutes** = max(critical path 81 minutes, total work 273 minutes / 3 workers). This simple bound does not include all delays imposed by dependencies and resource contention.\n\nThe optimization uses binary variables for each feasible integer task start, with constraints for dependencies, three concurrent workers, db capacity 1, and net capacity 2. A 115-minute horizon contains the resulting schedule and every potentially better schedule. Task start windows also incorporate earliest dependency starts and remaining critical-path lengths. Minimizing the final completion time gives 95 minutes.\n\nWorkers were assigned to the resulting intervals, and \"'`validate.py` independently checked all 40 tasks, integer nonnegative starts, dependency completion times, worker exclusivity, and resource limits. Running intervals are half-open: [start, start + duration).\n\nReproduce with `OPENBLAS_NUM_THREADS=1 python3 solve.py` (requires NumPy and SciPy), then `python3 validate.py`.\nEOF'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc \"cat > validate.py <<'PY'\nimport json\nx=json.load(open('tasks.json'));s=json.load(open('schedule.json'));tasks=x['tasks'];W=x['workers']\nassert set(s)==set(tasks)\nfor k,v in s.items():\n assert type(v['start']) is int and v['start']>=0\n assert type(v['worker']) is int and 0<=v['worker']<W\n for d in tasks[k]['deps']:assert v['start']>=s[d]['start']+tasks[d]['duration'],(k,d)\nM=max(s[k]['start']+tasks[k]['duration'] for k in tasks)\nfor tm in range(M):\n active=[k for k in tasks if s[k]['start']<=tm<s[k]['start']+tasks[k]['duration']]\n assert len({s[k]['worker'] for k in active})==len(active),(tm,active)\n for r,cap in x['resource_limits'].items():assert sum(r in tasks[k]['resources'] for k in active)<=cap,(tm,r)\nprint(f'Validated all {len(tasks)} tasks: makespan {M}; all dependencies, workers, and resource limits satisfied.')\nPY\npython3 validate.py\ncat > plan_notes.md <<'EOF'\nMakespan: **95 minutes**, proven optimal by integer optimization (HiGHS); the solver's final lower bound was also 95 minutes.\n\nRequested lower bound: **91 minutes** = max(critical path 81 minutes, total work 273 minutes / 3 workers). This simple bound does not include all delays imposed by dependencies and resource contention.\n\nThe optimization uses binary variables for each feasible integer task start, with constraints for dependencies, three concurrent workers, db capacity 1, and net capacity 2. A 115-minute horizon contains the resulting schedule and every potentially better schedule. Task start windows also incorporate earliest dependency starts and remaining critical-path lengths. Minimizing the final completion time gives 95 minutes.\n\nWorkers were assigned to the resulting intervals, and \"'`validate.py` independently checked all 40 tasks, integer nonnegative starts, dependency completion times, worker exclusivity, and resource limits. Running intervals are half-open: [start, start + duration).\n\nReproduce with `OPENBLAS_NUM_THREADS=1 python3 solve.py` (requires NumPy and SciPy), then `python3 validate.py`.\nEOF'", "aggregated_output": "Validated all 40 tasks: makespan 95; all dependencies, workers, and resource limits satisfied.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_8", "type": "agent_message", "text": "Created [schedule.json](schedule.json) and [plan_notes.md](plan_notes.md).\n\n- **Optimal makespan: 95 minutes**\n- Requested lower bound: **91 minutes**\n- All 40 tasks validated for dependencies, worker exclusivity, and resource limits."}}
{"type": "turn.completed", "usage": {"input_tokens": 145894, "cached_input_tokens": 131328, "cache_write_input_tokens": 0, "output_tokens": 1910, "reasoning_output_tokens": 87}}