Gemini 4 Argon (effort medium · thinking on) on Best fishing windows from tide, solunar and weather data
GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.17
- Wall time
- 3m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt (this run)
# Fishing trip planner from tide, solunar and weather data `input.json` (synthetic) covers 48 hours, hour index h = 0..47 meaning the hour [h, h+1). It has `tide_m` (49 hourly heights at h = 0..48), `wind_kt` and `pressure_hpa` (48 values), `solunar_major` and `solunar_minor` (lists of [start, end] in fractional hours), `daylight` ([sunrise_hour, sunset_hour] for day 0 and day 1, in hour-of-day), and `window_hours` L. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `windows.svg`. Fresh inputs are used too. ## Hourly score - If wind > 20 kt the hour is **unfishable**: score null, ignore the rest. - +3 if any major period overlaps the hour (start < h+1 and end > h); +1.5 if any minor period overlaps (both can apply). - Tide movement d = |tide[h+1] - tide[h]|, rounded to 6 decimals: +2 if d >= 0.3, else +1 if d >= 0.15. - Wind above 10 kt (and <= 20): -1. - Pressure trend dp = pressure[h] - pressure[h-3] (0 for h < 3), rounded to 6 decimals: +1 if dp <= -1.0, -1 if dp >= +1.0. - Outside daylight: -2, where daylight means sunrise <= (h mod 24) < sunset for day h // 24. Round each score to 2 decimals. ## Windows A window starting at s covers hours s..s+L-1, all fishable; its score is the sum, rounded to 2 decimals. Sort all windows by score descending then start ascending, and greedily keep up to 3 that do not overlap any already kept one. ## answer.json `{"hourly_scores": [48 values or null], "unfishable_hours": n, "windows": [{"start", "end" (= start + L), "score"}, ...]}` ## Visual output: windows.svg Static, self-contained SVG (root `<svg>`; no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, `href`/`src` except `#...` or `data:`, CSS `url(http...)`/`@import` or fetches; renders headless) with an element `id="hourly-bars"` (the hourly scores) and one `<rect id="window-N">` for each reported window N = 1, 2, 3 in answer order. The drawing itself is not graded. ## Scoring Hidden checks (partial credit; all must pass): `hourly_scores_day1` (hours 0-23), `hourly_scores_day2` (24-47 plus unfishable count), `top_windows`, `windows_svg_static` (windows.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Tolerance 0.001. 60-second limit per run.
Overlap tests on fractional periods, threshold boundaries placed exactly at 10 and 20 kt, a 3-hour pressure trend with float rounding, daylight by day index, and greedy non-overlapping window selection with ties. 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. hourly_scores_day1 2. hourly_scores_day2 3. top_windows 4. windows_svg_static 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch10 case k03-fishing-window-planner (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 (windows.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: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 9 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.17
- Wall time
- 3m
- Human step-ins
- 0
- Turns
- 9
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on | 150,358 | 9,280 | $0.17 |
Reproduce this run
- Use the exact prompt shown above for this run (the use case page has the shared prompt).
- 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. hourly_scores_day1 2. hourly_scores_day2 3. top_windows 4. windows_svg_static 5. fresh_run_1 6. 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": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/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"}}
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{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.806776972, "usage": {"input_tokens": 8538, "output_tokens": 71, "thinking_tokens": 17, "cache_read_tokens": 0, "total_tokens": 8609}}}
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{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.67197203, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/input.json"}, "output": "196 lines, 1582 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 33.875218437, "usage": {"input_tokens": 2157, "output_tokens": 3151, "thinking_tokens": 1120, "cache_read_tokens": 8539, "total_tokens": 5308}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 4, "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/k03/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.5937453339999998, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.486077219, "usage": {"input_tokens": 3232, "output_tokens": 93, "thinking_tokens": 22, "cache_read_tokens": 10697, "total_tokens": 3325}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 6, "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 windows.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.2599553, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json && head -n 30 windows.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/k03/work/solve.py\", line 183, in <module>\r\n main()\r\n ~~~~^^\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py\", line 168, in main\r\n with open(output_path, \"w\", encoding=\"utf-8\") as f:\r\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nOSError: [Errno 30] Read-only file system: 'answer.json'\r\n"}}}
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{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "edit_file", "duration_seconds": 1.397113956, "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py"}}}}
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{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 10, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json && cat /tmp/answer.json && cat /tmp/windows.svg"}}}}
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{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "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/k03/work/windows.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 13, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 3.651779986, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/windows.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 14, "state": "DONE", "step_type": "agent_response", "duration_seconds": 17.320796229, "usage": {"input_tokens": 3567, "output_tokens": 1208, "thinking_tokens": 727, "cache_read_tokens": 18491, "total_tokens": 4775}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 15, "state": "ACTIVE", "step_type": "tool", "tool_name": "edit_file", "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 15, "state": "DONE", "step_type": "tool", "tool_name": "edit_file", "duration_seconds": 2.085718347, "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 16, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.819653422, "usage": {"input_tokens": 3388, "output_tokens": 198, "thinking_tokens": 33, "cache_read_tokens": 20009, "total_tokens": 3586}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 17, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c 'import xml.etree.ElementTree as ET, json; tree = ET.parse(\"windows.svg\"); root = tree.getroot(); print(\"root:\", root.tag, \"ids:\", [el.attrib.get(\"id\") for el in root.iter() if \"id\" in el.attrib]); ans = json.load(open(\"answer.json\")); print(\"len(hourly_scores):\", len(ans[\"hourly_scores\"]), \"unfishable:\", ans[\"unfishable_hours\"], \"windows:\", ans[\"windows\"])'"}}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 17, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.055909136, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c 'import xml.etree.ElementTree as ET, json; tree = ET.parse(\"windows.svg\"); root = tree.getroot(); print(\"root:\", root.tag, \"ids:\", [el.attrib.get(\"id\") for el in root.iter() if \"id\" in el.attrib]); ans = json.load(open(\"answer.json\")); print(\"len(hourly_scores):\", len(ans[\"hourly_scores\"]), \"unfishable:\", ans[\"unfishable_hours\"], \"windows:\", ans[\"windows\"])'"}, "output": "Command exited with code 0.\n\nCommand output:\nroot: {http://www.w3.org/2000/svg}svg ids: ['windows-highlight', 'window-1', 'window-2', 'window-3', 'hourly-bars']\r\nlen(hourly_scores): 48 unfishable: 2 windows: [{'start': 6, 'end': 9, 'score': 12.0}, {'start': 29, 'end': 32, 'score': 10.0}, {'start': 11, 'end': 14, 'score': 8.0}]\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " the fishing trip planne"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "r in [solve.py](file:///"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "home/bob/usecase-runs/ag"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "y-batch/out/"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "gemini-"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "4-argon-medium/k03/work/solve.py) an"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "d generated [answer.json"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ":///home/bob/usecase-run"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s/agy-batch/out/gemini-4"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-argon-mediu"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "m/"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "k03/work/answer.json) and [windows.s"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "vg](file:///home/bob/use"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "case"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/agy-batch/out/gemini-4-argon-m"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "edium/k03/wo"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rk/windows"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ".svg).\n\n### Summary\n- [`"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "compute_solution`](file:"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "///home/bob/"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "usecase-runs/agy-batch/o"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ut/gemini"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-4-argon-medium/k03/work/solve.py#L7-L78): Compu"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tes the 48 h"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ourly"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " scores (marking hours with `wind_kt > 20` as `n"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ull` and cou"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nting `unfishable_hours`"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "), evaluates"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " solunar major/minor overlaps, 6-dec"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "imal rounded tide moveme"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nt and 3-hou"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "r pressure trends, wind "}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "penalties, and"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " daylight ranges, and greedily selec"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ts up to 3 non-overlappi"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ng `L`-hour fishable win"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "dows sorted "}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "by "}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`(-score, start)`.\n- [`generate_svg`"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file:///home/bob/useca"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "se-runs/agy"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-batch/out/gemini-4-argo"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "n-medium/k03/work/solve."}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "py#L"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "81-L157): Outputs a self-contained s"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tatic SVG containing `<g id=\"hourly-bars\">` and `<rect id=\"w"}}
{"event": "step_update", "step_update": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "step_index": 18, "state": "DONE", "step_type": "agent_response", "text_delta": "indow-N\">` elements for each selected window.\n", "duration_seconds": 7.526941009, "usage": {"input_tokens": 2468, "output_tokens": 370, "thinking_tokens": 14, "cache_read_tokens": 21348, "total_tokens": 2838}}}
{"event": "result", "result": {"conversation_id": "47d5760b-2f38-454d-bd5b-6b326ff57d05", "status": "SUCCESS", "response": "Implemented the fishing trip planner in [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/answer.json) and [windows.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/windows.svg).\n\n### Summary\n- [`compute_solution`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py#L7-L78): Computes the 48 hourly scores (marking hours with `wind_kt > 20` as `null` and counting `unfishable_hours`), evaluates solunar major/minor overlaps, 6-decimal rounded tide movement and 3-hour pressure trends, wind penalties, and daylight ranges, and greedily selects up to 3 non-overlapping `L`-hour fishable windows sorted by `(-score, start)`.\n- [`generate_svg`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k03/work/solve.py#L81-L157): Outputs a self-contained static SVG containing `<g id=\"hourly-bars\">` and `<rect id=\"window-N\">` elements for each selected window.\n", "duration_seconds": 156.580697161, "num_turns": 1, "usage": {"input_tokens": 32008, "output_tokens": 9280, "thinking_tokens": 2399, "cache_read_tokens": 118350, "total_tokens": 41288}}}