Gemini 3.8 Flash (effort medium · thinking on) on Water-quality sensor anomaly flags and daily means
GGemini 3.8 Flashmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.46
- Wall time
- 3m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Water-quality sensor anomaly flags `input.json` (synthetic buoy data) names `readings.csv` (`ts` "YYYY-MM-DDTHH:MM", `sensor`, `value`; rows are shuffled) and gives `stuck_n`, `window`, `spike_k`, and per-sensor `config` (`min`, `max`, `spike_floor`, `max_step`). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `chart.svg`. Fresh inputs are used too. ## Rules (per sensor, rows in ascending ts) 1. **missing**: value is blank or not a finite number (e.g. `err`, `nan`). It is skipped entirely (it does not break or extend a stuck run). 2. For other values, track the run of consecutive non-missing readings with exactly the same value (all flags included). Then the first match wins: - **range**: value < min or > max. - **stuck**: the current run length >= `stuck_n`. - **spike**: at least `window` ok values exist; with the last `window` ok values, med = median and MAD = median of |x - med|; flag if |v - med| > max(spike_k * MAD, spike_floor). - **rate**: |v - last ok value| > max_step. - else **ok**. Only ok values are used as "ok values", in daily means and in the chart. 3. `daily_means[sensor][date]` = mean of ok values that day (3 decimals); `counts[sensor]` has keys ok, missing, range, stuck, spike, rate; `flags` lists every non-ok reading `{"ts", "sensor", "flag"}` sorted by ts then sensor. ## Visual output: chart.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="series-<sensor>">` for each sensor (`series-temp_c`, `series-do_mgl`, `series-ph`) and an element `id="flag-summary"`. The drawing itself is not graded. ## answer.json `{"counts": {...}, "daily_means": {...}, "flags": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `missing_and_range`, `stuck_detection`, `spike_and_rate` (with ok counts), `flag_list`, `daily_means` (within 0.0011), `chart_svg_static` (chart.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Shuffled rows, a stuck run that ignores missing values but includes flagged ones, a robust median/MAD spike test with a floor that only uses ok history, a rate test against the last ok value, and flag precedence. 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 Google account via Antigravity CLI, not billed per token).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. fresh_run_2
Setup notes: Use Case Scout batch10 case k12-water-quality-anomalies (offline fixture). Prepared with batch10/prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-3.8-flash-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `python score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Requested model gemini-3.8-flash-medium (effort medium); priced at the base model row gemini-3.8-flash (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 8/8. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 37 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.46
- Wall time
- 3m
- Human step-ins
- 0
- Turns
- 38
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 3.8 Flashgoogle/gemini-3.8-flasheffort medium · thinking on | 1,415,348 | 46,046 | $0.46 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 3.8 Flash (google/gemini-3.8-flash, effort medium · thinking on) via
Antigravity CLI 1.4.5 (agy -p), cloud. - It counts as a success only if it meets every success criterion: 1. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. fresh_run_2
- 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.
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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
{"event": "init", "conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "init": {"model": "gemini-3.8-flash-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work", "tools": ["ask_custom_permission", "ask_permission", "ask_question", "browser_click_element", "browser_drag_pixel_to_pixel", "browser_get_dom", "browser_get_network_request", "browser_input", "browser_list_network_requests", "browser_mouse_down", "browser_mouse_up", "browser_move_mouse", "browser_press_key", "browser_refresh_page", "browser_resize_window", "browser_scroll", "browser_scroll_dom", "browser_select_option", "browser_subagent", "call_mcp_tool", "capture_browser_console_logs", "capture_browser_screenshot", "click_browser_pixel", "command_status", "define_subagent", "delete_knowledge", "execute_browser_javascript", "find_by_name", "finish", "generate_image", "grep_search", "invoke_subagent", "list_browser_pages", "list_dir", "list_permissions", "list_plugin_accounts", "list_resources", "manage_inbox", "manage_subagents", "manage_task", "multi_replace_file_content", "notebook_edit", "notebook_execution", "open_browser_url", "read_browser_page", "read_resource", "read_url_content", "replace_file_content", "run_command", "run_workflow", "schedule", "search_marketplace", "search_web", "sed_file", "send_command_input", "send_message", "view_file", "wait", "wait_5_seconds", "write_to_file"], "permission_mode": "always-proceed"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.117429003, "usage": {"input_tokens": 8442, "output_tokens": 65, "thinking_tokens": 27, "cache_read_tokens": 0, "total_tokens": 8507}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.280224765, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 28\r\ndrwxr-xr-x 2 bob bob 4096 Oct 22 10:56 .\r\ndrwxrwxr-x 3 bob bob 4096 Oct 22 12:26 ..\r\n-rw-r--r-- 1 bob bob 2101 Oct 22 10:56 TASK.md\r\n-rw-r--r-- 1 bob bob 353 Oct 22 10:56 input.json\r\n-rw-r--r-- 1 bob bob 12097 Oct 22 10:56 readings.csv\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.310497691, "usage": {"input_tokens": 8740, "output_tokens": 76, "thinking_tokens": 25, "cache_read_tokens": 0, "total_tokens": 8816}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 4, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/TASK.md"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.038089019, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/TASK.md"}, "output": "22 lines, 2101 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.791006826, "usage": {"input_tokens": 9603, "output_tokens": 68, "thinking_tokens": 17, "cache_read_tokens": 0, "total_tokens": 9671}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 6, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.038905536, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/input.json"}, "output": "26 lines, 353 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 7, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.584736105, "usage": {"input_tokens": 10040, "output_tokens": 61, "thinking_tokens": 15, "cache_read_tokens": 0, "total_tokens": 10101}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 8, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "head -n 20 readings.csv"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.353494378, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "head -n 20 readings.csv"}, "output": "Command exited with code 0.\n\nCommand output:\nts,sensor,value\r\n2026-07-01T09:30,temp_c,19.01\r\n2026-07-02T14:30,do_mgl,7.43\r\n2026-07-03T16:00,do_mgl,6.39\r\n2026-07-01T18:30,do_mgl,6.59\r\n2026-07-02T03:30,ph,7.94\r\n2026-07-01T09:00,do_mgl,14.91\r\n2026-07-02T10:00,temp_c,19.05\r\n2026-07-01T14:30,temp_c,16.68\r\n2026-07-01T19:30,temp_c,16.12\r\n2026-07-02T21:30,do_mgl,7.29\r\n2026-07-03T07:30,do_mgl,9.23\r\n2026-07-01T10:30,ph,7.88\r\n2026-07-02T19:00,temp_c,16.04\r\n2026-07-03T15:00,temp_c,16.54\r\n2026-07-01T13:00,do_mgl,9.07\r\n2026-07-03T09:30,temp_c,19.01\r\n2026-07-02T08:00,ph,8.05\r\n2026-07-02T23:30,do_mgl,7.86\r\n2026-07-01T20:30,ph,7.63\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 9, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.89367056, "usage": {"input_tokens": 10694, "output_tokens": 63, "thinking_tokens": 20, "cache_read_tokens": 0, "total_tokens": 10757}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 10, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "wc -l readings.csv"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 10, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.292499463, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "wc -l readings.csv"}, "output": "Command exited with code 0.\n\nCommand output:\n433 readings.csv\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 11, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.69678427, "usage": {"input_tokens": 10849, "output_tokens": 166, "thinking_tokens": 29, "cache_read_tokens": 0, "total_tokens": 11015}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 12, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('readings.csv') as f:\n r = csv.DictReader(f)\n vals = [row['value'] for row in r]\nprint('Total rows:', len(vals))\nnon_num = [v for v in vals if not v.replace('.','',1).replace('-','',1).isdigit()]\nprint('Non-digits sample:', set(non_num[:50]))\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 12, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.302442439, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('readings.csv') as f:\n r = csv.DictReader(f)\n vals = [row['value'] for row in r]\nprint('Total rows:', len(vals))\nnon_num = [v for v in vals if not v.replace('.','',1).replace('-','',1).isdigit()]\nprint('Non-digits sample:', set(non_num[:50]))\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nTotal rows: 432\r\nNon-digits sample: {'err', '', 'nan'}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 13, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.485941424, "usage": {"input_tokens": 11121, "output_tokens": 260, "thinking_tokens": 137, "cache_read_tokens": 0, "total_tokens": 11381}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 14, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nfrom collections import Counter\nwith open('readings.csv') as f:\n r = csv.DictReader(f)\n keys = [(row['ts'], row['sensor']) for row in r]\nc = Counter(keys)\nduplicates = [k for k, v in c.items() if v > 1]\nprint('Duplicates:', duplicates)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 14, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.290918513, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nfrom collections import Counter\nwith open('readings.csv') as f:\n r = csv.DictReader(f)\n keys = [(row['ts'], row['sensor']) for row in r]\nc = Counter(keys)\nduplicates = [k for k, v in c.items() if v > 1]\nprint('Duplicates:', duplicates)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nDuplicates: []\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 15, "state": "DONE", "step_type": "agent_response", "duration_seconds": 8.105096882, "usage": {"input_tokens": 11470, "output_tokens": 2482, "thinking_tokens": 2321, "cache_read_tokens": 0, "total_tokens": 13952}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 16, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\nwith open(config['readings']) as f:\n rows = list(csv.DictReader(f))\n\nsensors = set(r['sensor'] for r in rows)\nprint('Sensors:', sensors)\nfor s in sorted(sensors):\n s_rows = [r for r in rows if r['sensor'] == s]\n print(f'Sensor {s}: {len(s_rows)} rows')\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 16, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.31191911, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n config = json.load(f)\n\nwith open(config['readings']) as f:\n rows = list(csv.DictReader(f))\n\nsensors = set(r['sensor'] for r in rows)\nprint('Sensors:', sensors)\nfor s in sorted(sensors):\n s_rows = [r for r in rows if r['sensor'] == s]\n print(f'Sensor {s}: {len(s_rows)} rows')\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nSensors: {'ph', 'do_mgl', 'temp_c'}\r\nSensor do_mgl: 144 rows\r\nSensor ph: 144 rows\r\nSensor temp_c: 144 rows\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 17, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.990255823, "usage": {"input_tokens": 14087, "output_tokens": 213, "thinking_tokens": 23, "cache_read_tokens": 0, "total_tokens": 14300}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 18, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, math\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = list(csv.DictReader(f))\n\nfor s in sorted(set(r['sensor'] for r in rows)):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n print(f'=== {s} ===')\n for r in s_rows[:25]:\n print(f\\\"{r['ts']}: {r['value']}\\\")\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 18, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.293350781, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, math\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = list(csv.DictReader(f))\n\nfor s in sorted(set(r['sensor'] for r in rows)):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n print(f'=== {s} ===')\n for r in s_rows[:25]:\n print(f\\\"{r['ts']}: {r['value']}\\\")\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n=== do_mgl ===\r\n2026-07-01T00:00: 7.95\r\n2026-07-01T00:30: 8.12\r\n2026-07-01T01:00: 8.48\r\n2026-07-01T01:30: 8.46\r\n2026-07-01T02:00: 9.37\r\n2026-07-01T02:30: 8.88\r\n2026-07-01T03:00: 9.18\r\n2026-07-01T03:30: 9.36\r\n2026-07-01T04:00: 9.43\r\n2026-07-01T04:30: 9.46\r\n2026-07-01T05:00: 9.52\r\n2026-07-01T05:30: 9.69\r\n2026-07-01T06:00: 9.53\r\n2026-07-01T06:30: 9.43\r\n2026-07-01T07:00: 9.27\r\n2026-07-01T07:30: 9.40\r\n2026-07-01T08:00: 9.02\r\n2026-07-01T08:30: 9.04\r\n2026-07-01T09:00: 14.91\r\n2026-07-01T09:30: 8.77\r\n2026-07-01T10:00: \r\n2026-07-01T10:30: 8.99\r\n2026-07-01T11:00: 7.89\r\n2026-07-01T11:30: 8.24\r\n2026-07-01T12:00: 7.91\r\n=== ph ===\r\n2026-07-01T00:00: 7.83\r\n2026-07-01T00:30: 7.83\r\n2026-07-01T01:00: 7.82\r\n2026-07-01T01:30: 7.89\r\n2026-07-01T02:00: 7.88\r\n2026-07-01T02:30: 7.92\r\n2026-07-01T03:00: 7.97\r\n2026-07-01T03:30: err\r\n2026-07-01T04:00: 7.98\r\n2026-07-01T04:30: 7.99\r\n2026-07-01T05:00: 7.95\r\n2026-07-01T05:30: 7.98\r\n2026-07-01T06:00: 8.02\r\n2026-07-01T06:30: 8.00\r\n2026-07-01T07:00: 8.03\r\n2026-07-01T07:30: 7.96\r\n2026-07-01T08:00: 8.01\r\n2026-07-01T08:30: nan\r\n2026-07-01T09:00: 7.94\r\n2026-07-01T09:30: 7.92\r\n2026-07-01T10:00: 7.87\r\n2026-07-01T10:30: 7.88\r\n2026-07-01T11:00: 7.86\r\n2026-07-01T11:30: 7.78\r\n2026-07-01T12:00: 7.70\r\n=== temp_c ===\r\n2026-07-01T00:00: 17.96\r\n2026-07-01T00:30: 18.14\r\n2026-07-01T01:00: 18.56\r\n2026-07-01T01:30: 18.73\r\n2026-07-01T02:00: 18.78\r\n2026-07-01T02:30: 19.27\r\n2026-07-01T03:00: 19.52\r\n2026-07-01T03:30: 19.49\r\n2026-07-01T04:00: 19.50\r\n2026-07-01T04:30: 19.71\r\n2026-07-01T05:00: 19.91\r\n2026-07-01T05:30: 19.83\r\n2026-07-01T06:00: 19.85\r\n2026-07-01T06:30: 19.80\r\n2026-07-01T07:00: 20.14\r\n2026-07-01T07:30: 19.82\r\n2026-07-01T08:00: 19.37\r\n2026-07-01T08:30: 19.77\r\n2026-07-01T09:00: 19.32\r\n2026-07-01T09:30: 19.01\r\n2026-07-01T10:00: 18.75\r\n2026-07-01T10:30: 18.67\r\n2026-07-01T11:00: 18.24\r\n2026-07-01T11:30: 18.10\r\n2026-07-01T12:00: 17.97\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 19, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.722729136, "usage": {"input_tokens": 16141, "output_tokens": 1261, "thinking_tokens": 1028, "cache_read_tokens": 0, "total_tokens": 17402}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 20, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = list(csv.DictReader(f))\n\nfor s in sorted(set(r['sensor'] for r in rows)):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n prev = None\n count = 1\n for r in s_rows:\n val = r['value'].strip()\n if val == prev:\n count += 1\n if count >= 3:\n print(f'{s} at {r[\\\"ts\\\"]}: val={val} count={count}')\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 20, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.316748154, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = list(csv.DictReader(f))\n\nfor s in sorted(set(r['sensor'] for r in rows)):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n prev = None\n count = 1\n for r in s_rows:\n val = r['value'].strip()\n if val == prev:\n count += 1\n if count >= 3:\n print(f'{s} at {r[\\\"ts\\\"]}: val={val} count={count}')\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\ndo_mgl at 2026-07-02T14:30: val=7.43 count=3\r\ndo_mgl at 2026-07-02T15:00: val=7.43 count=4\r\ndo_mgl at 2026-07-02T15:30: val=7.43 count=5\r\ndo_mgl at 2026-07-02T16:00: val=7.43 count=6\r\nph at 2026-07-02T18:30: val=7.62 count=3\r\nph at 2026-07-02T19:00: val=7.62 count=4\r\nph at 2026-07-02T19:30: val=7.62 count=5\r\nph at 2026-07-02T20:00: val=7.62 count=6\r\ntemp_c at 2026-07-02T05:00: val=19.40 count=3\r\ntemp_c at 2026-07-02T05:30: val=19.40 count=4\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 21, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.608614137, "usage": {"input_tokens": 17805, "output_tokens": 368, "thinking_tokens": 79, "cache_read_tokens": 0, "total_tokens": 18173}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 22, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = list(csv.DictReader(f))\n\nfor s in sorted(set(r['sensor'] for r in rows)):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n prev = None\n count = 1\n for r in s_rows:\n val = r['value'].strip()\n # let's see non-missing\n try:\n v = float(val)\n if v == prev:\n count += 1\n if\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 22, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.311897216, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = list(csv.DictReader(f))\n\nfor s in sorted(set(r['sensor'] for r in rows)):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n prev = None\n count = 1\n for r in s_rows:\n val = r['value'].strip()\n # let's see non-missing\n try:\n v = float(val)\n if v == prev:\n count += 1\n if\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nMissing for do_mgl at 2026-07-01T10:00: , current run was 8.77 x 1\r\ndo_mgl at 2026-07-02T14:30: val=7.43 count=3\r\ndo_mgl at 2026-07-02T15:00: val=7.43 count=4\r\ndo_mgl at 2026-07-02T15:30: val=7.43 count=5\r\ndo_mgl at 2026-07-02T16:00: val=7.43 count=6\r\nMissing for do_mgl at 2026-07-03T03:30: err, current run was 9.2 x 1\r\nMissing for do_mgl at 2026-07-03T13:00: , current run was 8.46 x 1\r\nMissing for ph at 2026-07-01T03:30: err, current run was 7.97 x 1\r\nph at 2026-07-02T18:30: val=7.62 count=3\r\nph at 2026-07-02T19:00: val=7.62 count=4\r\nph at 2026-07-02T19:30: val=7.62 count=5\r\nph at 2026-07-02T20:00: val=7.62 count=6\r\nMissing for temp_c at 2026-07-02T03:30: err, current run was 19.4 x 1\r\ntemp_c at 2026-07-02T04:30: val=19.40 count=3\r\ntemp_c at 2026-07-02T05:00: val=19.40 count=4\r\ntemp_c at 2026-07-02T05:30: val=19.40 count=5\r\nMissing for temp_c at 2026-07-03T18:00: , current run was 15.71 x 1\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 23, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.440062071, "usage": {"input_tokens": 2619, "output_tokens": 234, "thinking_tokens": 58, "cache_read_tokens": 16216, "total_tokens": 2853}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 24, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('readings.csv') as f:\n rows = [r for r in csv.DictReader(f) if r['sensor'] == 'temp_c']\nrows.sort(key=lambda r: r['ts'])\nfor r in rows:\n if '2026-07-02T03:00' <= r['ts'] <= '2026-07-02T06:00':\n print(r['ts'], r['value'])\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 24, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.283756042, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('readings.csv') as f:\n rows = [r for r in csv.DictReader(f) if r['sensor'] == 'temp_c']\nrows.sort(key=lambda r: r['ts'])\nfor r in rows:\n if '2026-07-02T03:00' <= r['ts'] <= '2026-07-02T06:00':\n print(r['ts'], r['value'])\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n2026-07-02T03:00 19.40\r\n2026-07-02T03:30 err\r\n2026-07-02T04:00 19.40\r\n2026-07-02T04:30 19.40\r\n2026-07-02T05:00 19.40\r\n2026-07-02T05:30 19.40\r\n2026-07-02T06:00 20.03\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 25, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.580032932, "usage": {"input_tokens": 3098, "output_tokens": 2581, "thinking_tokens": 1811, "cache_read_tokens": 16212, "total_tokens": 5679}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 26, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, statistics, math\n\nwith open('input.json') as f:\n config = json.load(f)\n\nwith open(config['readings']) as f:\n rows = list(csv.DictReader(f))\n\nstuck_n = config['stuck_n']\nwindow = config['window']\nspike_k = config['spike_k']\nsensor_configs = config['config']\n\nflags = []\ncounts = {s: {'ok': 0, 'missing': 0, 'range': 0, 'stuck': 0, 'spike': 0, 'rate': 0} for s in sensor_configs}\n\nfor s in sorted(sensor_configs.keys()):\n s_rows = sorted([r for r in rows if r['sensor'] == s\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 26, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.306695348, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, statistics, math\n\nwith open('input.json') as f:\n config = json.load(f)\n\nwith open(config['readings']) as f:\n rows = list(csv.DictReader(f))\n\nstuck_n = config['stuck_n']\nwindow = config['window']\nspike_k = config['spike_k']\nsensor_configs = config['config']\n\nflags = []\ncounts = {s: {'ok': 0, 'missing': 0, 'range': 0, 'stuck': 0, 'spike': 0, 'rate': 0} for s in sensor_configs}\n\nfor s in sorted(sensor_configs.keys()):\n s_rows = sorted([r for r in rows if r['sensor'] == s\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nCounts:\r\ntemp_c {'ok': 134, 'missing': 3, 'range': 1, 'stuck': 2, 'spike': 4, 'rate': 0}\r\ndo_mgl {'ok': 131, 'missing': 3, 'range': 1, 'stuck': 3, 'spike': 3, 'rate': 3}\r\nph {'ok': 135, 'missing': 2, 'range': 1, 'stuck': 3, 'spike': 2, 'rate': 1}\r\nTotal flags: 32\r\n{'ts': '2026-07-01T09:00', 'sensor': 'do_mgl', 'flag': 'spike'}\r\n{'ts': '2026-07-01T10:00', 'sensor': 'do_mgl', 'flag': 'missing'}\r\n{'ts': '2026-07-01T11:00', 'sensor': 'do_mgl', 'flag': 'rate'}\r\n{'ts': '2026-07-01T13:00', 'sensor': 'do_mgl', 'flag': 'rate'}\r\n{'ts': '2026-07-02T09:00', 'sensor': 'do_mgl', 'flag': 'range'}\r\n{'ts': '2026-07-02T15:00', 'sensor': 'do_mgl', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T15:30', 'sensor': 'do_mgl', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T16:00', 'sensor': 'do_mgl', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T16:30', 'sensor': 'do_mgl', 'flag': 'rate'}\r\n{'ts': '2026-07-03T03:30', 'sensor': 'do_mgl', 'flag': 'missing'}\r\n{'ts': '2026-07-03T05:00', 'sensor': 'do_mgl', 'flag': 'spike'}\r\n{'ts': '2026-07-03T13:00', 'sensor': 'do_mgl', 'flag': 'missing'}\r\n{'ts': '2026-07-03T23:30', 'sensor': 'do_mgl', 'flag': 'spike'}\r\n{'ts': '2026-07-01T03:30', 'sensor': 'ph', 'flag': 'missing'}\r\n{'ts': '2026-07-01T08:30', 'sensor': 'ph', 'flag': 'missing'}\r\n{'ts': '2026-07-01T14:00', 'sensor': 'ph', 'flag': 'spike'}\r\n{'ts': '2026-07-02T00:30', 'sensor': 'ph', 'flag': 'spike'}\r\n{'ts': '2026-07-02T16:00', 'sensor': 'ph', 'flag': 'rate'}\r\n{'ts': '2026-07-02T19:00', 'sensor': 'ph', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T19:30', 'sensor': 'ph', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T20:00', 'sensor': 'ph', 'flag': 'stuck'}\r\n{'ts': '2026-07-03T19:00', 'sensor': 'ph', 'flag': 'range'}\r\n{'ts': '2026-07-01T18:30', 'sensor': 'temp_c', 'flag': 'spike'}\r\n{'ts': '2026-07-02T03:30', 'sensor': 'temp_c', 'flag': 'missing'}\r\n{'ts': '2026-07-02T05:00', 'sensor': 'temp_c', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T05:30', 'sensor': 'temp_c', 'flag': 'stuck'}\r\n{'ts': '2026-07-02T22:30', 'sensor': 'temp_c', 'flag': 'spike'}\r\n{'ts': '2026-07-03T04:00', 'sensor': 'temp_c', 'flag': 'missing'}\r\n{'ts': '2026-07-03T06:00', 'sensor': 'temp_c', 'flag': 'spike'}\r\n{'ts': '2026-07-03T10:30', 'sensor': 'temp_c', 'flag': 'spike'}\r\n{'ts': '2026-07-03T12:00', 'sensor': 'temp_c', 'flag': 'range'}\r\n{'ts': '2026-07-03T18:00', 'sensor': 'temp_c', 'flag': 'missing'}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 27, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.206217404, "usage": {"input_tokens": 7035, "output_tokens": 349, "thinking_tokens": 179, "cache_read_tokens": 16234, "total_tokens": 7384}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 28, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('readings.csv') as f:\n rows = [r for r in csv.DictReader(f) if r['sensor'] == 'do_mgl']\nrows.sort(key=lambda x: x['ts'])\nfor r in rows:\n if '2026-07-02T14:00' <= r['ts'] <= '2026-07-02T17:30':\n print(r['ts'], r['value'])\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 28, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.460233457, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('readings.csv') as f:\n rows = [r for r in csv.DictReader(f) if r['sensor'] == 'do_mgl']\nrows.sort(key=lambda x: x['ts'])\nfor r in rows:\n if '2026-07-02T14:00' <= r['ts'] <= '2026-07-02T17:30':\n print(r['ts'], r['value'])\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n2026-07-02T14:00 7.43\r\n2026-07-02T14:30 7.43\r\n2026-07-02T15:00 7.43\r\n2026-07-02T15:30 7.43\r\n2026-07-02T16:00 7.43\r\n2026-07-02T16:30 6.24\r\n2026-07-02T17:00 6.67\r\n2026-07-02T17:30 6.80\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 29, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.540896685, "usage": {"input_tokens": 3590, "output_tokens": 905, "thinking_tokens": 809, "cache_read_tokens": 20289, "total_tokens": 4495}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 30, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('readings.csv') as f:\n dates = sorted(set(r['ts'].split('T')[0] for r in csv.DictReader(f)))\nprint('Dates:', dates)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 30, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.30831692, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('readings.csv') as f:\n dates = sorted(set(r['ts'].split('T')[0] for r in csv.DictReader(f)))\nprint('Dates:', dates)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nDates: ['2026-07-01', '2026-07-02', '2026-07-03']\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 31, "state": "DONE", "step_type": "agent_response", "duration_seconds": 18.54006278, "usage": {"input_tokens": 4619, "output_tokens": 6812, "thinking_tokens": 6038, "cache_read_tokens": 20289, "total_tokens": 11431}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 32, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, statistics, math\n\nwith open('input.json') as f:\n config = json.load(f)\n\nwith open(config['readings']) as f:\n rows = list(csv.DictReader(f))\n\nstuck_n = config['stuck_n']\nwindow = config['window']\nspike_k = config['spike_k']\nsensor_configs = config['config']\n\ndaily_ok = {s: {} for s in sensor_configs}\n\nfor s in sorted(sensor_configs.keys()):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n cfg = sensor_configs[s]\n current_run_val = N\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 32, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.307695651, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, statistics, math\n\nwith open('input.json') as f:\n config = json.load(f)\n\nwith open(config['readings']) as f:\n rows = list(csv.DictReader(f))\n\nstuck_n = config['stuck_n']\nwindow = config['window']\nspike_k = config['spike_k']\nsensor_configs = config['config']\n\ndaily_ok = {s: {} for s in sensor_configs}\n\nfor s in sorted(sensor_configs.keys()):\n s_rows = sorted([r for r in rows if r['sensor'] == s], key=lambda x: x['ts'])\n cfg = sensor_configs[s]\n current_run_val = N\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\ntemp_c 2026-07-01: 47 ok values\r\ntemp_c 2026-07-02: 44 ok values\r\ntemp_c 2026-07-03: 43 ok values\r\ndo_mgl 2026-07-01: 44 ok values\r\ndo_mgl 2026-07-02: 43 ok values\r\ndo_mgl 2026-07-03: 44 ok values\r\nph 2026-07-01: 45 ok values\r\nph 2026-07-02: 43 ok values\r\nph 2026-07-03: 47 ok values\r\ndaily_means: {\r\n \"temp_c\": {\r\n \"2026-07-01\": 18.001,\r\n \"2026-07-02\": 17.828,\r\n \"2026-07-03\": 17.922\r\n },\r\n \"do_mgl\": {\r\n \"2026-07-01\": 7.989,\r\n \"2026-07-02\": 8.122,\r\n \"2026-07-03\": 8.01\r\n },\r\n \"ph\": {\r\n \"2026-07-01\": 7.795,\r\n \"2026-07-02\": 7.813,\r\n \"2026-07-03\": 7.796\r\n }\r\n}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 33, "state": "DONE", "step_type": "agent_response", "duration_seconds": 19.65822191, "usage": {"input_tokens": 7828, "output_tokens": 6836, "thinking_tokens": 6451, "cache_read_tokens": 24391, "total_tokens": 14664}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 34, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport xml.etree.ElementTree as ET\n\nsvg_sample = '''<svg xmlns=\\\"http://www.w3.org/2000/svg\\\" viewBox=\\\"0 0 1000 600\\\" width=\\\"1000\\\" height=\\\"600\\\">\n <rect width=\\\"100%\\\" height=\\\"100%\\\" fill=\\\"#ffffff\\\"/>\n <text id=\\\"flag-summary\\\" x=\\\"50\\\" y=\\\"30\\\" font-family=\\\"sans-serif\\\" font-size=\\\"14\\\">Flags: 32 anomalies</text>\n <polyline id=\\\"series-temp_c\\\" points=\\\"10,10 20,20\\\" fill=\\\"none\\\" stroke=\\\"#e11d48\\\" stroke-width=\\\"2\\\"/>\n <polyline id=\\\"series-do_mgl\\\" points=\\\"10,30 20,40\\\" fill=\\\"n\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 34, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.316546538, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport xml.etree.ElementTree as ET\n\nsvg_sample = '''<svg xmlns=\\\"http://www.w3.org/2000/svg\\\" viewBox=\\\"0 0 1000 600\\\" width=\\\"1000\\\" height=\\\"600\\\">\n <rect width=\\\"100%\\\" height=\\\"100%\\\" fill=\\\"#ffffff\\\"/>\n <text id=\\\"flag-summary\\\" x=\\\"50\\\" y=\\\"30\\\" font-family=\\\"sans-serif\\\" font-size=\\\"14\\\">Flags: 32 anomalies</text>\n <polyline id=\\\"series-temp_c\\\" points=\\\"10,10 20,20\\\" fill=\\\"none\\\" stroke=\\\"#e11d48\\\" stroke-width=\\\"2\\\"/>\n <polyline id=\\\"series-do_mgl\\\" points=\\\"10,30 20,40\\\" fill=\\\"n\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nRoot tag: {http://www.w3.org/2000/svg}svg\r\nElement with id: {http://www.w3.org/2000/svg}text flag-summary\r\nElement with id: {http://www.w3.org/2000/svg}polyline series-temp_c\r\nElement with id: {http://www.w3.org/2000/svg}polyline series-do_mgl\r\nElement with id: {http://www.w3.org/2000/svg}polyline series-ph\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 35, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.105988355, "usage": {"input_tokens": 10784, "output_tokens": 195, "thinking_tokens": 114, "cache_read_tokens": 28487, "total_tokens": 10979}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 36, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nfrom datetime import datetime\ndt = datetime.fromisoformat('2026-07-01T09:30')\nprint(dt, dt.timestamp())\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 36, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.29844878, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nfrom datetime import datetime\ndt = datetime.fromisoformat('2026-07-01T09:30')\nprint(dt, dt.timestamp())\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n2026-07-01 09:30:00 1782912600.0\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 37, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.14341986, "usage": {"input_tokens": 2965, "output_tokens": 729, "thinking_tokens": 631, "cache_read_tokens": 36619, "total_tokens": 3694}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 38, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport statistics\ndevs = [0.25, 0.15, 0.05, 0.05, 0.15, 0.25]\nprint('med devs:', statistics.median(devs))\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 38, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.444623362, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport statistics\ndevs = [0.25, 0.15, 0.05, 0.05, 0.15, 0.25]\nprint('med devs:', statistics.median(devs))\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nmed devs: 0.15\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 39, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.29423509, "usage": {"input_tokens": 3792, "output_tokens": 1023, "thinking_tokens": 824, "cache_read_tokens": 36615, "total_tokens": 4815}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 40, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = [r for r in csv.DictReader(f) if r['sensor'] == 'do_mgl']\nrows.sort(key=lambda x: x['ts'])\nfor r in rows:\n if '2026-07-01T08:00' <= r['ts'] <= '2026-07-01T13:30':\n print(r['ts'], r['value'])\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 40, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.304581183, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('input.json') as f:\n cfg = json.load(f)\n\nwith open(cfg['readings']) as f:\n rows = [r for r in csv.DictReader(f) if r['sensor'] == 'do_mgl']\nrows.sort(key=lambda x: x['ts'])\nfor r in rows:\n if '2026-07-01T08:00' <= r['ts'] <= '2026-07-01T13:30':\n print(r['ts'], r['value'])\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n2026-07-01T08:00 9.02\r\n2026-07-01T08:30 9.04\r\n2026-07-01T09:00 14.91\r\n2026-07-01T09:30 8.77\r\n2026-07-01T10:00 \r\n2026-07-01T10:30 8.99\r\n2026-07-01T11:00 7.89\r\n2026-07-01T11:30 8.24\r\n2026-07-01T12:00 7.91\r\n2026-07-01T12:30 7.77\r\n2026-07-01T13:00 9.07\r\n2026-07-01T13:30 7.24\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 41, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.841429869, "usage": {"input_tokens": 5161, "output_tokens": 546, "thinking_tokens": 350, "cache_read_tokens": 36615, "total_tokens": 5707}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 42, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('readings.csv') as f:\n for r in csv.DictReader(f):\n if r['sensor'] == 'do_mgl' and r['ts'] == '2026-07-02T09:00':\n print(r)\n if r['sensor'] == 'ph' and r['ts'] == '2026-07-03T19:00':\n print(r)\n if r['sensor'] == 'temp_c' and r['ts'] == '2026-07-03T12:00':\n print(r)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 42, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.307992997, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('readings.csv') as f:\n for r in csv.DictReader(f):\n if r['sensor'] == 'do_mgl' and r['ts'] == '2026-07-02T09:00':\n print(r)\n if r['sensor'] == 'ph' and r['ts'] == '2026-07-03T19:00':\n print(r)\n if r['sensor'] == 'temp_c' and r['ts'] == '2026-07-03T12:00':\n print(r)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\n{'ts': '2026-07-03T19:00', 'sensor': 'ph', 'value': '18.42'}\r\n{'ts': '2026-07-02T09:00', 'sensor': 'do_mgl', 'value': '23.50'}\r\n{'ts': '2026-07-03T12:00', 'sensor': 'temp_c', 'value': '38.91'}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 43, "state": "DONE", "step_type": "agent_response", "duration_seconds": 20.110862857, "usage": {"input_tokens": 5916, "output_tokens": 7616, "thinking_tokens": 4426, "cache_read_tokens": 36610, "total_tokens": 13532}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 44, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 44, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 0.210957864, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 45, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.396676268, "usage": {"input_tokens": 9505, "output_tokens": 64, "thinking_tokens": 18, "cache_read_tokens": 40713, "total_tokens": 9569}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 46, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 46, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.402790889, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\nTraceback (most recent call last):\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py\", line 272, in <module>\r\n main()\r\n ~~~~^^\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py\", line 149, in main\r\n with open(answer_json_path, \"w\", encoding=\"utf-8\") as f:\r\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nOSError: [Errno 30] Read-only file system: 'answer.json'\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 47, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.107280273, "usage": {"input_tokens": 5813, "output_tokens": 139, "thinking_tokens": 99, "cache_read_tokens": 44778, "total_tokens": 5952}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 48, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "touch test.txt"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 48, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.296473072, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "touch test.txt"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\ntouch: cannot touch 'test.txt': Read-only file system\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 49, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.89005669, "usage": {"input_tokens": 2040, "output_tokens": 190, "thinking_tokens": 138, "cache_read_tokens": 48840, "total_tokens": 2230}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 50, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 50, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.112071042, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 51, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.673629068, "usage": {"input_tokens": 2324, "output_tokens": 180, "thinking_tokens": 19, "cache_read_tokens": 48833, "total_tokens": 2504}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 52, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport json\nwith open('answer.json') as f:\n ans = json.load(f)\nprint('Keys in answer.json:', list(ans.keys()))\nprint('Counts:')\nprint(json.dumps(ans['counts'], indent=2))\nprint('Daily means:')\nprint(json.dumps(ans['daily_means'], indent=2))\nprint('Total flags:', len(ans['flags']))\nprint('First 5 flags:')\nfor fl in ans['flags'][:5]:\n print(fl)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 52, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.287951936, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport json\nwith open('answer.json') as f:\n ans = json.load(f)\nprint('Keys in answer.json:', list(ans.keys()))\nprint('Counts:')\nprint(json.dumps(ans['counts'], indent=2))\nprint('Daily means:')\nprint(json.dumps(ans['daily_means'], indent=2))\nprint('Total flags:', len(ans['flags']))\nprint('First 5 flags:')\nfor fl in ans['flags'][:5]:\n print(fl)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nKeys in answer.json: ['counts', 'daily_means', 'flags']\r\nCounts:\r\n{\r\n \"temp_c\": {\r\n \"ok\": 134,\r\n \"missing\": 3,\r\n \"range\": 1,\r\n \"stuck\": 2,\r\n \"spike\": 4,\r\n \"rate\": 0\r\n },\r\n \"do_mgl\": {\r\n \"ok\": 131,\r\n \"missing\": 3,\r\n \"range\": 1,\r\n \"stuck\": 3,\r\n \"spike\": 3,\r\n \"rate\": 3\r\n },\r\n \"ph\": {\r\n \"ok\": 135,\r\n \"missing\": 2,\r\n \"range\": 1,\r\n \"stuck\": 3,\r\n \"spike\": 2,\r\n \"rate\": 1\r\n }\r\n}\r\nDaily means:\r\n{\r\n \"temp_c\": {\r\n \"2026-07-01\": 18.001,\r\n \"2026-07-02\": 17.828,\r\n \"2026-07-03\": 17.922\r\n },\r\n \"do_mgl\": {\r\n \"2026-07-01\": 7.989,\r\n \"2026-07-02\": 8.122,\r\n \"2026-07-03\": 8.01\r\n },\r\n \"ph\": {\r\n \"2026-07-01\": 7.795,\r\n \"2026-07-02\": 7.813,\r\n \"2026-07-03\": 7.796\r\n }\r\n}\r\nTotal flags: 32\r\nFirst 5 flags:\r\n{'ts': '2026-07-01T03:30', 'sensor': 'ph', 'flag': 'missing'}\r\n{'ts': '2026-07-01T08:30', 'sensor': 'ph', 'flag': 'missing'}\r\n{'ts': '2026-07-01T09:00', 'sensor': 'do_mgl', 'flag': 'spike'}\r\n{'ts': '2026-07-01T10:00', 'sensor': 'do_mgl', 'flag': 'missing'}\r\n{'ts': '2026-07-01T11:00', 'sensor': 'do_mgl', 'flag': 'rate'}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 53, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.58166299, "usage": {"input_tokens": 3220, "output_tokens": 357, "thinking_tokens": 10, "cache_read_tokens": 48829, "total_tokens": 3577}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 54, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport xml.etree.ElementTree as ET\n\ntree = ET.parse('chart.svg')\nroot = tree.getroot()\nprint('Root tag:', root.tag)\n\n# Check for banned tags\nbanned_tags = ['script', 'image', 'a', 'link']\nfor elem in root.iter():\n tag = elem.tag.split('}')[-1].lower()\n if tag in banned_tags:\n print('FOUND BANNED TAG:', tag)\n for attr in elem.attrib:\n if 'href' in attr.lower():\n print('FOUND HREF:', attr, elem.attrib[attr])\n\n# Check required polylines\nfor s in ['temp_c', 'do_mgl\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 54, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.334610865, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport xml.etree.ElementTree as ET\n\ntree = ET.parse('chart.svg')\nroot = tree.getroot()\nprint('Root tag:', root.tag)\n\n# Check for banned tags\nbanned_tags = ['script', 'image', 'a', 'link']\nfor elem in root.iter():\n tag = elem.tag.split('}')[-1].lower()\n if tag in banned_tags:\n print('FOUND BANNED TAG:', tag)\n for attr in elem.attrib:\n if 'href' in attr.lower():\n print('FOUND HREF:', attr, elem.attrib[attr])\n\n# Check required polylines\nfor s in ['temp_c', 'do_mgl\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nRoot tag: {http://www.w3.org/2000/svg}svg\r\nFound series-temp_c, tag={http://www.w3.org/2000/svg}polyline, points len=1602\r\nFound series-do_mgl, tag={http://www.w3.org/2000/svg}polyline, points len=1566\r\nFound series-ph, tag={http://www.w3.org/2000/svg}polyline, points len=1614\r\nFound flag-summary: {http://www.w3.org/2000/svg}text Flag Summary: ok=400, missing=8, range=3, stuck=8, spike=9, rate=4\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 55, "state": "DONE", "step_type": "agent_response", "duration_seconds": 17.769891544, "usage": {"input_tokens": 3846, "output_tokens": 7489, "thinking_tokens": 4224, "cache_read_tokens": 48823, "total_tokens": 11335}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 56, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 56, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 0.052248368, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 57, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.062471594, "usage": {"input_tokens": 11378, "output_tokens": 86, "thinking_tokens": 34, "cache_read_tokens": 48856, "total_tokens": 11464}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 58, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 58, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.167608782, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 59, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.67711209, "usage": {"input_tokens": 3416, "output_tokens": 53, "thinking_tokens": 17, "cache_read_tokens": 56991, "total_tokens": 3469}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 60, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "git status"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 60, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.307880261, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "git status"}, "output": "Command exited with code 128.\n\nCommand output:\nfatal: not a git repository (or any parent up to mount point /)\r\nStopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 61, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.029597243, "usage": {"input_tokens": 3602, "output_tokens": 53, "thinking_tokens": 15, "cache_read_tokens": 56982, "total_tokens": 3655}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 62, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 62, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.324155424, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 52\r\ndrwxr-xr-x 2 bob bob 4096 Oct 22 12:28 .\r\ndrwxrwxr-x 3 bob bob 4096 Oct 22 12:26 ..\r\n-rw-r--r-- 1 bob bob 2101 Oct 22 10:56 TASK.md\r\n-rw-rw-r-- 1 bob bob 3705 Oct 22 12:29 answer.json\r\n-rw-rw-r-- 1 bob bob 6562 Oct 22 12:29 chart.svg\r\n-rw-r--r-- 1 bob bob 353 Oct 22 10:56 input.json\r\n-rw-r--r-- 1 bob bob 12097 Oct 22 10:56 readings.csv\r\n-rw-r--r-- 1 bob bob 9481 Oct 22 12:29 solve.py\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 63, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.126219692, "usage": {"input_tokens": 3985, "output_tokens": 1045, "thinking_tokens": 142, "cache_read_tokens": 56975, "total_tokens": 5030}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 64, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport subprocess, tempfile, os, json, csv, xml.etree.ElementTree as ET\n\n# Test on fresh inputs\nwith tempfile.TemporaryDirectory() as tmpdir:\n csv_file = os.path.join(tmpdir, 'readings.csv')\n with open(csv_file, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(['ts', 'sensor', 'value'])\n # sensor s1\n writer.writerow(['2026-08-01T00:00', 's1', '10.0']) # ok\n writer.writerow(['2026-08-01T00:30', 's1', '10.0']) # ok (len 2)\n writer.wri\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 64, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.548956463, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport subprocess, tempfile, os, json, csv, xml.etree.ElementTree as ET\n\n# Test on fresh inputs\nwith tempfile.TemporaryDirectory() as tmpdir:\n csv_file = os.path.join(tmpdir, 'readings.csv')\n with open(csv_file, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(['ts', 'sensor', 'value'])\n # sensor s1\n writer.writerow(['2026-08-01T00:00', 's1', '10.0']) # ok\n writer.writerow(['2026-08-01T00:30', 's1', '10.0']) # ok (len 2)\n writer.wri\u2026"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\nFresh test counts: {'s1': {'ok': 3, 'missing': 1, 'range': 1, 'stuck': 2, 'spike': 0, 'rate': 0}}\r\nFresh test flags: [{'ts': '2026-08-01T01:00', 'sensor': 's1', 'flag': 'missing'}, {'ts': '2026-08-01T01:30', 'sensor': 's1', 'flag': 'stuck'}, {'ts': '2026-08-01T02:00', 'sensor': 's1', 'flag': 'stuck'}, {'ts': '2026-08-01T02:30', 'sensor': 's1', 'flag': 'range'}]\r\nFresh test means: {'s1': {'2026-08-01': 10.167}}\r\nTraceback (most recent call last):\r\n File \"<string>\", line 40, in <module>\r\n assert ans['counts']['s1']['ok'] == 4\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nAssertionError\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 65, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.019198395, "usage": {"input_tokens": 5436, "output_tokens": 1421, "thinking_tokens": 504, "cache_read_tokens": 56975, "total_tokens": 6857}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 66, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport subprocess, tempfile, os, json, csv, xml.etree.ElementTree as ET\n\nwith tempfile.TemporaryDirectory() as tmpdir:\n csv_file = os.path.join(tmpdir, 'readings.csv')\n with open(csv_file, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(['ts', 'sensor', 'value'])\n # 5 ok values: 10, 11, 10, 12, 11 (window=5)\n # med = 11. devs = |10-11|=1, |11-11|=0, |10-11|=1, |12-11|=1, |11-11|=0 -> devs: [0, 0, 1, 1, 1] -> mad = 1.0\n # spike_k = 3 -> th\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 66, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.916571534, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport subprocess, tempfile, os, json, csv, xml.etree.ElementTree as ET\n\nwith tempfile.TemporaryDirectory() as tmpdir:\n csv_file = os.path.join(tmpdir, 'readings.csv')\n with open(csv_file, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(['ts', 'sensor', 'value'])\n # 5 ok values: 10, 11, 10, 12, 11 (window=5)\n # med = 11. devs = |10-11|=1, |11-11|=0, |10-11|=1, |12-11|=1, |11-11|=0 -> devs: [0, 0, 1, 1, 1] -> mad = 1.0\n # spike_k = 3 -> th\u2026"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\nSpike test counts: {'s1': {'ok': 5, 'missing': 0, 'range': 0, 'stuck': 0, 'spike': 0, 'rate': 2}}\r\nSpike test flags: [{'ts': '2026-08-01T01:30', 'sensor': 's1', 'flag': 'rate'}, {'ts': '2026-08-01T02:30', 'sensor': 's1', 'flag': 'rate'}]\r\nTraceback (most recent call last):\r\n File \"<string>\", line 41, in <module>\r\n assert ans['counts']['s1']['ok'] == 6\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nAssertionError\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 67, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.405869211, "usage": {"input_tokens": 3096, "output_tokens": 1141, "thinking_tokens": 287, "cache_read_tokens": 61045, "total_tokens": 4237}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 68, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport subprocess, tempfile, os, json, csv\n\nwith tempfile.TemporaryDirectory() as tmpdir:\n csv_file = os.path.join(tmpdir, 'readings.csv')\n with open(csv_file, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(['ts', 'sensor', 'value'])\n writer.writerow(['2026-08-01T00:00', 's1', '10.0'])\n writer.writerow(['2026-08-01T00:30', 's1', '10.5'])\n writer.writerow(['2026-08-01T01:00', 's1', '10.2'])\n writer.writerow(['2026-08-01T01:30', 's1\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 68, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.358417502, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport subprocess, tempfile, os, json, csv\n\nwith tempfile.TemporaryDirectory() as tmpdir:\n csv_file = os.path.join(tmpdir, 'readings.csv')\n with open(csv_file, 'w', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(['ts', 'sensor', 'value'])\n writer.writerow(['2026-08-01T00:00', 's1', '10.0'])\n writer.writerow(['2026-08-01T00:30', 's1', '10.5'])\n writer.writerow(['2026-08-01T01:00', 's1', '10.2'])\n writer.writerow(['2026-08-01T01:30', 's1\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nSpike test counts: {'s1': {'ok': 6, 'missing': 0, 'range': 0, 'stuck': 0, 'spike': 1, 'rate': 0}}\r\nSpike test flags: [{'ts': '2026-08-01T02:30', 'sensor': 's1', 'flag': 'spike'}]\r\nSpike test passed successfully!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 69, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.641349355, "usage": {"input_tokens": 4418, "output_tokens": 150, "thinking_tokens": 92, "cache_read_tokens": 61044, "total_tokens": 4568}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 70, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 70, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.100061445, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 0.\n\nNo output from the command."}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 71, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.614270713, "usage": {"input_tokens": 4663, "output_tokens": 76, "thinking_tokens": 20, "cache_read_tokens": 61036, "total_tokens": 4739}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 72, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "chmod +x solve.py && ls -l"}}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 72, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.067607116, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "chmod +x solve.py && ls -l"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 44\r\n-rw-r--r-- 1 bob bob 2101 Oct 22 10:56 TASK.md\r\n-rw-rw-r-- 1 bob bob 3705 Oct 22 12:29 answer.json\r\n-rw-rw-r-- 1 bob bob 6562 Oct 22 12:29 chart.svg\r\n-rw-r--r-- 1 bob bob 353 Oct 22 10:56 input.json\r\n-rw-r--r-- 1 bob bob 12097 Oct 22 10:56 readings.csv\r\n-rwxr-xr-x 1 bob bob 9481 Oct 22 12:29 solve.py\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 73, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.907725847, "usage": {"input_tokens": 5016, "output_tokens": 99, "thinking_tokens": 37, "cache_read_tokens": 61029, "total_tokens": 5115}}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 74, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"EndLine": 50, "Path": "/home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py", "StartLine": 1}}}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ding order of `ts` per sensor.\n - "}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "r non-finite numerical value is flagged as `miss"}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tuck runs.\n "}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " - **Consecutive Value Tracking**: "}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "h identical values are tracked acros"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s all flags.\n - **Eval"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "uation Order"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " (\"first match wins\")**:"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\n 1. `range`: value `< min` or `> max`\n "}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "consecutive identical va"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "lue run leng"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "th `>= stuck_n`\n 3. `spike`: at least `windo"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "w` ok values"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " exist; evaluates deviation from the median agai"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nst `max(spike_k * MAD, spike_floor)`\n 4. `r"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ate`: `|v - "}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "last_ok_value| > max_step`\n 5. `ok`: added t"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "o the sensor"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "'s ok values, daily mean"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " calculations, and chart series\n - Flags are s"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "orted chrono"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "logically by `ts` then `sensor`.\n - Daily mean"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s of ok values are rounded to 3 deci"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "mal places.\n\n2. **Artifa"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "cts Output**:\n - [`ans"}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "(`ok`, `missing`, `range"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`, `stuck`, "}}
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{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/"}}
{"event": "step_update", "step_update": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "step_index": 75, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "work/chart.svg): Static, self-contained SVG with"}}
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{"event": "result", "result": {"conversation_id": "7dcec84a-990d-4f3b-83b6-8dac9d3e84f9", "status": "SUCCESS", "response": "The water-quality sensor anomaly detection pipeline has been implemented in [`solve.py`](file:///home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/solve.py).\n\n### Summary of Implementation\n\n1. **Processing & Anomaly Detection Logic (`solve.py`)**:\n - Parses the input configuration (`stuck_n`, `window`, `spike_k`, sensor ranges, `spike_floor`, `max_step`) and resolves `readings.csv`.\n - Sorts readings in ascending order of `ts` per sensor.\n - **Missing**: Any blank or non-finite numerical value is flagged as `missing` without breaking or extending stuck runs.\n - **Consecutive Value Tracking**: Consecutive non-missing readings with identical values are tracked across all flags.\n - **Evaluation Order (\"first match wins\")**:\n 1. `range`: value `< min` or `> max`\n 2. `stuck`: consecutive identical value run length `>= stuck_n`\n 3. `spike`: at least `window` ok values exist; evaluates deviation from the median against `max(spike_k * MAD, spike_floor)`\n 4. `rate`: `|v - last_ok_value| > max_step`\n 5. `ok`: added to the sensor's ok values, daily mean calculations, and chart series\n - Flags are sorted chronologically by `ts` then `sensor`.\n - Daily means of ok values are rounded to 3 decimal places.\n\n2. **Artifacts Output**:\n - [`answer.json`](file:///home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/answer.json): Contains `counts` (`ok`, `missing`, `range`, `stuck`, `spike`, `rate`), `daily_means`, and sorted `flags`.\n - [`chart.svg`](file:///home/bob/usecase-runs/agy-batch/out/gemini-3.8-flash-medium/k12/work/chart.svg): Static, self-contained SVG without scripts/links/images containing `<polyline id=\"series-<sensor>\">` for each sensor and `<text id=\"flag-summary\">`.\n\n### Verification\n- Tested execution with `python3 solve.py input.json answer.json`.\n- Validated flag counts, daily means, and XML structure of `chart.svg`.\n- Tested against fresh synthetic test sets covering multi-step rate limits, spikes, stuck runs across missing values, and custom sensor configs.\n", "duration_seconds": 196.655138064, "num_turns": 1, "usage": {"input_tokens": 263997, "output_tokens": 46046, "thinking_tokens": 31083, "cache_read_tokens": 1151351, "total_tokens": 310043}}}