Gemini 4 Argon (effort medium · thinking on) on Water-quality sensor anomaly flags and daily means
GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.32
- Wall time
- 4m
- 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-4-argon-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-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (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 13 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.32
- Wall time
- 4m
- Human step-ins
- 0
- Turns
- 11
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on | 327,866 | 16,784 | $0.32 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 4 Argon (google/gemini-4-argon, 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": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-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": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.838803622, "usage": {"input_tokens": 8407, "output_tokens": 124, "thinking_tokens": 9, "cache_read_tokens": 0, "total_tokens": 8531}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.070432466, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/input.json"}, "output": "26 lines, 353 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 3, "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-4-argon-medium/k12/work/readings.csv", "StartLine": 1}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 3, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.045991612, "tool_info": {"name": "read_file", "parameters": {"EndLine": 50, "Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/readings.csv", "StartLine": 1}, "output": "433 lines, 12097 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 4, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.69318273, "usage": {"input_tokens": 2058, "output_tokens": 114, "thinking_tokens": 47, "cache_read_tokens": 8408, "total_tokens": 2172}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 5, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"EndLine": 450, "Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/readings.csv", "StartLine": 51}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 5, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.049722884, "tool_info": {"name": "read_file", "parameters": {"EndLine": 450, "Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/readings.csv", "StartLine": 51}, "output": "433 lines, 12097 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 6, "state": "DONE", "step_type": "agent_response", "duration_seconds": 19.868925656000002, "usage": {"input_tokens": 11997, "output_tokens": 1525, "thinking_tokens": 874, "cache_read_tokens": 10467, "total_tokens": 13522}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 7, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport csv, json, math, statistics\n\nwith open(\"input.json\") as f:\n inp = json.load(f)\n\nrows = []\nwith open(inp[\"readings\"]) as f:\n for r in csv.DictReader(f):\n rows.append(r)\n\nrows.sort(key=lambda r: (r[\"ts\"], r[\"sensor\"]))\nby_sensor = {s: [] for s in inp[\"config\"]}\nfor r in rows:\n by_sensor.setdefault(r[\"sensor\"], []).append(r)\n\nfor s, srows in by_sensor.items():\n cfg = inp[\"config\"][s]\n ok_vals = []\n last_val = None\n run_len = 0\n for r in srows:\n ts = r[\"\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 7, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.469668249, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport csv, json, math, statistics\n\nwith open(\"input.json\") as f:\n inp = json.load(f)\n\nrows = []\nwith open(inp[\"readings\"]) as f:\n for r in csv.DictReader(f):\n rows.append(r)\n\nrows.sort(key=lambda r: (r[\"ts\"], r[\"sensor\"]))\nby_sensor = {s: [] for s in inp[\"config\"]}\nfor r in rows:\n by_sensor.setdefault(r[\"sensor\"], []).append(r)\n\nfor s, srows in by_sensor.items():\n cfg = inp[\"config\"][s]\n ok_vals = []\n last_val = None\n run_len = 0\n for r in srows:\n ts = r[\"\u2026"}, "output": "Command exited with code 1.\n\nCommand output:\n File \"<string>\", line 52\r\n print(f\"{s} {ts}: rate v={v} last_ok={ok_vals[-1]} diff={abs(v - ok_vals[-1]):.4f} max_step={cfg[\\\"max_step\\\"]}\")\r\n ^\r\nSyntaxError: unexpected character after line continuation character\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 8, "state": "DONE", "step_type": "agent_response", "duration_seconds": 11.123945064, "usage": {"input_tokens": 3743, "output_tokens": 672, "thinking_tokens": 16, "cache_read_tokens": 20415, "total_tokens": 4415}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 9, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport csv, json, math, statistics\n\nwith open(\"input.json\") as f:\n inp = json.load(f)\n\nrows = []\nwith open(inp[\"readings\"]) as f:\n for r in csv.DictReader(f):\n rows.append(r)\n\nrows.sort(key=lambda r: (r[\"ts\"], r[\"sensor\"]))\nby_sensor = {s: [] for s in inp[\"config\"]}\nfor r in rows:\n by_sensor.setdefault(r[\"sensor\"], []).append(r)\n\nfor s, srows in by_sensor.items():\n cfg = inp[\"config\"][s]\n ok_vals = []\n last_val = None\n run_len = 0\n for r in srows:\n ts = r[\"\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 9, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.504995373, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport csv, json, math, statistics\n\nwith open(\"input.json\") as f:\n inp = json.load(f)\n\nrows = []\nwith open(inp[\"readings\"]) as f:\n for r in csv.DictReader(f):\n rows.append(r)\n\nrows.sort(key=lambda r: (r[\"ts\"], r[\"sensor\"]))\nby_sensor = {s: [] for s in inp[\"config\"]}\nfor r in rows:\n by_sensor.setdefault(r[\"sensor\"], []).append(r)\n\nfor s, srows in by_sensor.items():\n cfg = inp[\"config\"][s]\n ok_vals = []\n last_val = None\n run_len = 0\n for r in srows:\n ts = r[\"\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\ntemp_c 2026-07-01T18:30: spike v=22.17 med=16.1850 mad=0.0800 diff=5.9850 thresh=2.0000\r\ntemp_c 2026-07-02T03:30: missing ('err')\r\ntemp_c 2026-07-02T05:00: stuck v=19.4 run=4\r\ntemp_c 2026-07-02T05:30: stuck v=19.4 run=5\r\ntemp_c 2026-07-02T22:30: spike v=18.74 med=16.5750 mad=0.1900 diff=2.1650 thresh=2.0000\r\ntemp_c 2026-07-03T04:00: missing ('nan')\r\ntemp_c 2026-07-03T06:00: spike v=25.57 med=19.6900 mad=0.2050 diff=5.8800 thresh=2.0000\r\ntemp_c 2026-07-03T10:30: spike v=24.7 med=19.5700 mad=0.0900 diff=5.1300 thresh=2.0000\r\ntemp_c 2026-07-03T12:00: range v=38.91\r\ntemp_c 2026-07-03T18:00: missing ('')\r\ndo_mgl 2026-07-01T09:00: spike v=14.91 med=9.3350 mad=0.1450 diff=5.5750 thresh=2.0000\r\ndo_mgl 2026-07-01T10:00: missing ('')\r\ndo_mgl 2026-07-01T11:00: rate v=7.89 last_ok=8.99 diff=1.1000 max_step=1.0\r\ndo_mgl 2026-07-01T13:00: rate v=9.07 last_ok=7.77 diff=1.3000 max_step=1.0\r\ndo_mgl 2026-07-02T09:00: range v=23.5\r\ndo_mgl 2026-07-02T15:00: stuck v=7.43 run=4\r\ndo_mgl 2026-07-02T15:30: stuck v=7.43 run=5\r\ndo_mgl 2026-07-02T16:00: stuck v=7.43 run=6\r\ndo_mgl 2026-07-02T16:30: rate v=6.24 last_ok=7.43 diff=1.1900 max_step=1.0\r\ndo_mgl 2026-07-03T03:30: missing ('err')\r\ndo_mgl 2026-07-03T05:00: spike v=16.18 med=9.2450 mad=0.2700 diff=6.9350 thresh=2.0000\r\ndo_mgl 2026-07-03T13:00: missing ('')\r\ndo_mgl 2026-07-03T23:30: spike v=13.0 med=7.2150 mad=0.1550 diff=5.7850 thresh=2.0000\r\nph 2026-07-01T03:30: missing ('err')\r\nph 2026-07-01T08:30: missing ('nan')\r\nph 2026-07-01T14:00: spike v=9.2 med=7.7750 mad=0.0450 diff=1.4250 thresh=0.4000\r\nph 2026-07-02T00:30: spike v=9.34 med=7.7450 mad=0.0500 diff=1.5950 thresh=0.4000\r\nph 2026-07-02T16:00: rate v=8.01 last_ok=7.6 diff=0.4100 max_step=0.25\r\nph 2026-07-02T19:00: stuck v=7.62 run=4\r\nph 2026-07-02T19:30: stuck v=7.62 run=5\r\nph 2026-07-02T20:00: stuck v=7.62 run=6\r\nph 2026-07-03T19:00: range v=18.42\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 10, "state": "DONE", "step_type": "agent_response", "duration_seconds": 12.560428049, "usage": {"input_tokens": 2070, "output_tokens": 1027, "thinking_tokens": 519, "cache_read_tokens": 24159, "total_tokens": 3097}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 11, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport csv, json, math, statistics\n\nwith open(\"input.json\") as f:\n inp = json.load(f)\n\nrows = list(csv.DictReader(open(inp[\"readings\"])))\nrows.sort(key=lambda r: (r[\"ts\"], r[\"sensor\"]))\nby_sensor = {s: [] for s in inp[\"config\"]}\nfor r in rows:\n by_sensor.setdefault(r[\"sensor\"], []).append(r)\n\nfor s, srows in by_sensor.items():\n cfg = inp[\"config\"][s]\n ok_vals = []\n last_val = None\n run_len = 0\n for r in srows:\n ts = r[\"ts\"]\n vstr = r[\"value\"].strip()\n t\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 11, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.05967984, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport csv, json, math, statistics\n\nwith open(\"input.json\") as f:\n inp = json.load(f)\n\nrows = list(csv.DictReader(open(inp[\"readings\"])))\nrows.sort(key=lambda r: (r[\"ts\"], r[\"sensor\"]))\nby_sensor = {s: [] for s in inp[\"config\"]}\nfor r in rows:\n by_sensor.setdefault(r[\"sensor\"], []).append(r)\n\nfor s, srows in by_sensor.items():\n cfg = inp[\"config\"][s]\n ok_vals = []\n last_val = None\n run_len = 0\n for r in srows:\n ts = r[\"ts\"]\n vstr = r[\"value\"].strip()\n t\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nNEAR RATE: do_mgl 2026-07-03T13:30 7.47 8.46 0.9900000000000011 1.0\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 12, "state": "DONE", "step_type": "agent_response", "duration_seconds": 44.661261722, "usage": {"input_tokens": 2573, "output_tokens": 4124, "thinking_tokens": 1525, "cache_read_tokens": 24831, "total_tokens": 6697}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 13, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 13, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.4555995689999999, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 14, "state": "DONE", "step_type": "agent_response", "duration_seconds": 14.855016946, "usage": {"input_tokens": 4210, "output_tokens": 101, "thinking_tokens": 30, "cache_read_tokens": 27405, "total_tokens": 4311}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 15, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json && head -n 30 chart.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 15, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.714074404, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json && head -n 30 chart.svg"}, "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-4-argon-medium/k12/work/solve.py\", line 223, in <module>\r\n solve(in_path, out_path, svg_path)\r\n ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/solve.py\", line 201, in solve\r\n with open(answer_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": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 16, "state": "DONE", "step_type": "agent_response", "duration_seconds": 29.581913781, "usage": {"input_tokens": 2458, "output_tokens": 186, "thinking_tokens": 104, "cache_read_tokens": 29566, "total_tokens": 2644}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 17, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json /tmp/chart.svg && cat /tmp/answer.json && cat /tmp/chart.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 17, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.988060471, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json /tmp/chart.svg && cat /tmp/answer.json && cat /tmp/chart.svg"}, "output": "The tool's output was truncated due to being too long. You can find the full output in a file at file:///home/bob/.gemini/antigravity-cli/brain/97b90e81-e528-4d5f-9bb4-f8354808dc7f/.system_generated/steps/17/output.txt.\n\nHere is a preview with the middle of the output truncated:\nCommand exited with code 0.\n\nCommand output:\n{\r\n \"counts\": {\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\n \"daily_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 \"flags\": [\r\n {\r\n \"ts\": \"2026-07-01T03:30\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T08:30\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T09:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T10:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T11:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"rate\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T13:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"rate\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T14:00\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-01T18:30\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T00:30\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T03:30\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T05:00\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T05:30\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T09:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"range\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T15:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T15:30\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T16:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T16:00\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"rate\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T16:30\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"rate\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T19:00\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T19:30\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T20:00\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"stuck\"\r\n },\r\n {\r\n \"ts\": \"2026-07-02T22:30\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T03:30\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T04:00\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T05:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T06:00\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T10:30\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"spike\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T12:00\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"range\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T13:00\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T18:00\",\r\n \"sensor\": \"temp_c\",\r\n \"flag\": \"missing\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T19:00\",\r\n \"sensor\": \"ph\",\r\n \"flag\": \"range\"\r\n },\r\n {\r\n \"ts\": \"2026-07-03T23:30\",\r\n \"sensor\": \"do_mgl\",\r\n \"flag\": \"spike\"\r\n }\r\n ]\r\n}<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 800 500\" width=\"800\" height=\"500\">\r\n <rect width=\"800\" height=\"500\" fill=\"#ffffff\" />\r\n <text x=\"400\" y=\"24\" text-anchor=\"middle\" font-family=\"sans-serif\" font-size=\"16\" font-weight=\"bold\" fill=\"#222222\">Water-Quality Sensor Readings (OK Values)</text>\r\n <rect x=\"60\" y=\"40.0\" width=\"700\" height=\"106.7\" fill=\"#f9fbfd\" stroke=\"#cccccc\" stroke-width=\"1\" />\r\n <text x=\"52\" y=\"93.3\" text-anchor=\"end\" dominant-baseline=\"middle\" font-family=\"sans-serif\" font-size=\"12\" fill=\"#333333\">temp_c</text>\r\n <polyline id=\"series-temp_c\" fill=\"none\" stroke=\"#1f77b4\" stroke-width=\"2\" points=\"60.00,95.49 65.26,91.79 70.53,83.17 75.79,79.69 81.05,78.66 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font-family=\"sans-serif\" font-size=\"12\" fill=\"#333333\">ph</text>\r\n <polyline id=\"series-ph\" fill=\"none\" stroke=\"#d62728\" stroke-width=\"2\" points=\"60.00,340.03 65.22,340.03 70.45,341.93 75.67,328.66 80.90,330.56 86.12,322.97 91.34,313.50 96.57,311.60 101.79,309.71 107.01,317.29 112.24,311.60 117.46,304.02 122.69,307.81 127.91,302.12 133.13,315.39 138.36,305.92 143.58,319.18 148.81,322.97 154.03,332.45 159.25,330.56 164.48,334.35 169.70,349.51 174.93,364.67 180.15,340.03 185.37,357.09 190.60,351.41 195.82,370.36 201.04,374.15 206.27,374.15 211.49,377.94 216.72,387.42 221.94,376.05 227.16,385.52 232.39,385.52 237.61,383.63 242.84,385.52 248.06,383.63 253.28,374.15 258.51,377.94 263.73,368.46 268.96,368.46 274.18,362.78 279.40,364.67 284.63,345.72 289.85,349.51 295.07,341.93 300.30,336.24 305.52,336.24 310.75,330.56 315.97,321.08 321.19,315.39 326.42,319.18 331.64,315.39 336.87,317.29 342.09,309.71 347.31,305.92 352.54,307.81 357.76,307.81 362.99,317.29 368.21,309.71 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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 18, "state": "DONE", "step_type": "agent_response", "duration_seconds": 8.072517195, "usage": {"input_tokens": 7318, "output_tokens": 170, "thinking_tokens": 120, "cache_read_tokens": 32025, "total_tokens": 7488}}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 19, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.062506689, "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/answer.json"}, "output": "207 lines, 3705 bytes"}}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 22, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 22, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.914667157, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/answer.json"}}}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-4-argon-medium/k12/work"}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ") and [chart.svg](file"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ":///home/bob/usecase-runs/agy-batch/"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "out/gemini-4-argon-mediu"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "m/"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "k12/work/chart.svg).\n\n### Overview o"}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ts each sensor's reading"}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " order (`missing` $\\rightarrow$ `ran"}}
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{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rrow$ `stuck` $\\rightarr"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ow$ `spike` $\\rightarrow"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "$ `rate"}}
{"event": "step_update", "step_update": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "` $\\rightarrow$ `ok`), preserves con"}}
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{"event": "result", "result": {"conversation_id": "97b90e81-e528-4d5f-9bb4-f8354808dc7f", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/solve.py) using only the Python 3 standard library and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/answer.json) and [chart.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/chart.svg).\n\n### Overview of Implementation\n- [`solve`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/solve.py#L76-L216): Sorts each sensor's readings by ascending `ts`, evaluates anomalies in precedence order (`missing` $\\rightarrow$ `range` $\\rightarrow$ `stuck` $\\rightarrow$ `spike` $\\rightarrow$ `rate` $\\rightarrow$ `ok`), preserves consecutive non-missing run lengths across `missing` entries, and computes 3-decimal daily means from `ok` readings.\n- [`generate_svg`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k12/work/solve.py#L18-L73): Outputs a static, self-contained SVG containing `<polyline id=\"series-<sensor>\">` elements for `temp_c`, `do_mgl`, and `ph`, along with `<g id=\"flag-summary\">`.\n", "duration_seconds": 246.299304244, "num_turns": 1, "usage": {"input_tokens": 62627, "output_tokens": 16784, "thinking_tokens": 3341, "cache_read_tokens": 265239, "total_tokens": 79411}}}