GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Turn weekly ad-platform exports into an exact wasted-spend report
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.05
- Wall time
- 55s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Weekly ads review: wasted-spend report from Google-style and Meta-style exports The files are synthetic exports for one week: `google_ads.csv`, `meta_ads.csv` and `config.json`. Write `solve.py` (Python 3 standard library only, no network). `python solve.py input.json answer.json` must write `answer.json` (the first argument is unused; read the three files from the current directory). Fresh exports are generated when scoring. ## Parsing rules 1. `google_ads.csv`: skip lines before the header line that starts with `Day,`. Rows whose `Day` is not a date like `Oct 12, 2026` (e.g. `Total: ...` rows) are non-data rows. `Cost` and `Conv. value` may contain thousands commas or be `--` (= 0). Spend currency is the row's `Currency code` (USD or EUR). 2. `meta_ads.csv`: may start with a UTF-8 BOM. The spend column is named `Amount spent (USD)` or `Amount spent (EUR)`; that currency applies to every row. Use `Reporting starts` as the row date. Empty numeric cells = 0. 3. Meta `Results` count as conversions **only** when `Result indicator` is `actions:offsite_conversion.fb_pixel_purchase`; otherwise the row has 0 conversions (its results are clicks, not conversions). `Purchases conversion value` is the conversion value. 4. Convert EUR to USD by multiplying each row's amount by `eur_to_usd`. Keep full precision while summing; round only final reported money values to 2 decimals, half-up. 5. Drop rows dated outside `week_start`..`week_end` (inclusive). Drop exact duplicate lines within a file (keep the first). Count drops in `rows_dropped`: `{"duplicates", "out_of_range", "non_data"}` (a duplicate is counted as a duplicate even if also out of range; check duplicates first, then non-data, then range). 6. A unit is (platform `google`|`meta`, campaign, ad group/ad set). Names are matched after trimming, collapsing internal whitespace and ignoring case; report the first-seen form in file order (trimmed and whitespace-collapsed). ## Metrics and waste rules (per unit, over the week) 7. `spend` (USD), `clicks` (int), `conversions`, `conv_value` (USD; Google values follow the row currency, Meta values the file currency). 8. `cpa` = spend / conversions rounded to 2 dp, `null` when conversions = 0. `roas` = conv_value / spend rounded to 4 dp, `null` when spend = 0. 9. `reason` = `"zero_conversions"` when conversions = 0 and spend >= `zero_conv_min_spend_usd`, then `wasted` = spend. `reason` = `"high_cpa"` when conversions > 0 and cpa (unrounded) > `cpa_multiple` x `target_cpa_usd`, then `wasted` = spend - conversions x `target_cpa_usd`. Otherwise `reason` = null, `wasted` = 0. 10. `units` order: wasted descending, then spend descending, then platform, campaign, ad group ascending (case-insensitive). ## answer.json `{"units": [{"platform", "campaign", "ad_group", "spend", "clicks", "conversions", "conv_value", "cpa", "roas", "reason", "wasted"}], "totals": {"spend", "wasted", "wasted_pct", "by_platform": {"google": {"spend", "wasted"}, "meta": {"spend", "wasted"}}}, "top5": [[platform, campaign, ad_group] for the first 5 units with wasted > 0], "rows_dropped": {...}}`. `wasted_pct` = 100 x wasted / spend, 1 dp. Totals are sums of the rounded unit values. ## Optional visual: chart.svg If you write `chart.svg`: a static, self-contained SVG (root `<svg>`, no scripts, images, links, external URLs or `on*` handlers) with an element `id="wasted-bars"` and one element `id="bar-1"` .. `id="bar-N"` for the top-5 units in order. It is not required; if present it must follow these rules. ## Scoring Hidden checks (partial credit; all must pass; money within 0.005): `parse_dedupe_and_drops`, `unit_keys_and_spend_usd`, `conversions_value_and_result_indicator`, `waste_rules`, `totals_ranking_top5`, `chart_svg_if_present` on the given exports; `fresh_run_1`, `fresh_run_2` (whole answer on new exports). 30-second limit per run.
Every messy-export trap moves real dollars between units, so a quick pandas-style sum gets the shape right and most numbers wrong. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
Success criteria: 1. parse_dedupe_and_drops 2. unit_keys_and_spend_usd 3. conversions_value_and_result_indicator 4. waste_rules 5. totals_ranking_top5 6. chart_svg_if_present 7. fresh_run_1 8. fresh_run_2
Setup notes: Use Case Scout batch13 case n05-ads-wasted-spend (offline fixture). Prepared with batch13/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Visual output (chart.svg (optional)) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 8/8. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 3 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.05
- Wall time
- 55s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 73,073 | 2,120 | $0.05 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. parse_dedupe_and_drops 2. unit_keys_and_spend_usd 3. conversions_value_and_result_indicator 4. waste_rules 5. totals_ranking_top5 6. chart_svg_if_present 7. fresh_run_1 8. fresh_run_2
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"type": "thread.started", "thread_id": "01a16415-d16f-7dd3-8885-6a0e55d8805a"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the export columns and config, then write `solve.py` using decimal arithmetic and check the output.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; for f in config.json google_ads.csv meta_ads.csv; do if [ -f \"$f\" ]; then head -n 12 \"$f\"; fi; done'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; for f in config.json google_ads.csv meta_ads.csv; do if [ -f \"$f\" ]; then head -n 12 \"$f\"; fi; done'", "aggregated_output": "total 28\ndrwxr-xr-x 6 bob bob 4096 Oct 22 10:48 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 10:48 ..\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:48 .agents\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:48 .aws\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:48 .codex\ndr-xr-xr-x 2 bob bob 40 Oct 22 10:48 .git\n-rw-r--r-- 1 bob bob 3866 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 156 Oct 22 10:08 config.json\n-rw-r--r-- 1 bob bob 3869 Oct 22 10:08 google_ads.csv\n-rw-r--r-- 1 bob bob 6417 Oct 22 10:08 meta_ads.csv\n{\n \"week_start\": \"2026-10-12\",\n \"week_end\": \"2026-10-18\",\n \"eur_to_usd\": 1.1431,\n \"target_cpa_usd\": 25,\n \"zero_conv_min_spend_usd\": 75,\n \"cpa_multiple\": 2\n}Campaign performance report\n\"October 12, 2026 - October 18, 2026\"\nDay,Campaign,Ad group,Currency code,Cost,Impr.,Clicks,Conversions,Conv. value\n\"Oct 17, 2026\",\" Video\",\"DPA\",EUR,40.48,4243,9,0.00,0.00\n\"Oct 12, 2026\",\" Competitor\",\"Phrase\",EUR,23.55,2560,57,0.10,11.99\n\"Oct 18, 2026\",\"Competitor\",\"Remarketing 30d\",USD,24.19,1668,80,2.00,237.05\n\"Oct 15, 2026\",\"Shopping \",\"18-34\",USD,38.74,868,36,0.10,4.84\n\"Oct 13, 2026\",\" Shopping\",\"18-34\",USD,45.79,545,40,0.00,0.00\n\"Oct 15, 2026\",\"Video\",\"DPA\",EUR,37.67,243,38,1.00,55.95\n\"Oct 15, 2026\",\"Video\",\"Women 25+\",USD,54.72,4052,30,2.00,112.03\n\"Oct 12, 2026\",\"Shopping \",\"DPA\",USD,50.40,225,20,0.50,54.42\n\"Oct 17, 2026\",\"Competitor\",\"Remarketing 30d\",USD,18.72,3413,1,1.00,32.76\n\ufeffReporting starts,Reporting ends,Campaign name,Ad set name,Amount spent (USD),Impressions,Link clicks,Results,Result indicator,Purchases conversion value\n2026-10-17,2026-10-17,\"Competitor\",\"35-54\",76.63,2672,148,148,actions:link_click,\n2026-10-17,2026-10-17,\"Brand\",\"Phrase\",73.84,5917,106,,actions:offsite_conversion.fb_pixel_purchase,\n2026-10-18,2026-10-18,\"Video\",\"Top Funnel\",34.06,2081,51,2,actions:offsite_conversion.fb_pixel_purchase,99.15\n2026-10-15,2026-10-15,\"Competitor\",\"Broad\",69.64,6201,30,30,actions:link_click,\n2026-10-12,2026-10-12,\"Competitor\",\"Remarketing 30d\",16.58,604,81,1,actions:offsite_conversion.fb_pixel_purchase,64.11\n2026-10-15,2026-10-15,\"Video\",\"Top Funnel\",76.34,5260,42,1,actions:offsite_conversion.fb_pixel_purchase,91.23\n2026-10-19,2026-10-19,\"Competitor\",\"Remarketing 30d\",44.68,7999,98,1,actions:offsite_conversion.fb_pixel_purchase,65.93\n2026-10-11,2026-10-11,\"Video\",\"Top Funnel\",51.66,6557,60,,actions:offsite_conversion.fb_pixel_purchase,\n2026-10-12,2026-10-12,\"Competitor\",\"35-54\",57.40,5965,107,107,actions:link_click,\n2026-10-17,2026-10-17,\"Competitor\",\"Remarketing 30d\",46.22,1870,26,0,actions:offsite_conversion.fb_pixel_purchase,\n2026-10-14,2026-10-14,\"Video\",\"Top Funnel\",55.78,7909,108,1,actions:offsite_conversion.fb_pixel_purchase,85.90\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\n#\"'!/usr/bin/env python3\n\"\"\"Aggregate weekly ad exports using exact decimal money arithmetic.\"\"\"\nimport csv\nimport io\nimport json\nimport re\nimport sys\nfrom datetime import date, datetime\nfrom decimal import Decimal, ROUND_HALF_UP, getcontext\n\ngetcontext().prec = 50\nZERO = Decimal(0)\n\n\ndef number(value):\n value = (value or '\"'').strip().replace(',', '')\n return ZERO if value in ('', '--') else Decimal(value)\n\n\ndef rounded(value, places=2):\n return value.quantize(Decimal(1).scaleb(-places), rounding=ROUND_HALF_UP)\n\n\ndef name(value):\n return ' '.join(value.split())\n\n\ndef records(path, google=False):\n with open(path, encoding='utf-8-sig', newline='') as stream:\n lines = stream.readlines()\n if google:\n start = next(i for i, line in enumerate(lines) if line.startswith('Day,'))\n lines = lines[start:]\n reader = csv.reader(io.StringIO(''.join(lines), newline=''))\n header = next(reader)\n previous = reader.line_num\n for cells in reader:\n # Retain the original record text: equivalent parsed cells need not be\n # exact duplicate export lines (quoting and whitespace may differ).\n raw = ''.join(lines[previous:reader.line_num]).rstrip('\\\\r\\\\n')\n previous = reader.line_num\n yield dict(zip(header, cells)), raw, header\n\n\ndef solve():\n with open('config.json', encoding='utf-8') as stream:\n config = json.load(stream, parse_float=Decimal, parse_int=Decimal)\n first = date.fromisoformat(config['week_start'])\n last = date.fromisoformat(config['week_end'])\n drops = dict(duplicates=0, out_of_range=0, non_data=0)\n units = {}\n for platform in ('google', 'meta'):\n seen = set()\n for row, raw, header in records(platform + '_ads.csv', platform == 'google'):\n if raw in seen:\n drops['duplicates'] += 1\n continue\n seen.add(raw)\n try:\n if platform == 'google':\n text = row.get('Day', '').strip()\n if not re.fullmatch(r'[A-Z][a-z]{2} \\\\d{1,2}, \\\\d{4}', text):\n raise ValueError('non-date')\n day = datetime.strptime(text, '%b %d, %Y').date()\n else:\n day = date.fromisoformat(row.get('Reporting starts', '').strip())\n except ValueError:\n drops['non_data'] += 1\n continue\n if not first <= day <= last:\n drops['out_of_range'] += 1\n continue\n if platform == 'google':\n campaign, group = name(row['Campaign']), name(row['Ad group'])\n currency = row['Currency code'].strip()\n spend = number(row['Cost'])\n clicks = number(row['Clicks'])\n conversions = number(row['Conversions'])\n value = number(row['Conv. value'])\n else:\n campaign, group = name(row['Campaign name']), name(row['Ad set name'])\n spend_column = next(h for h in header if re.fullmatch(r'Amount spent \\\\((USD|EUR)\\\\)', h))\n currency = spend_column[-4:-1]\n spend = number(row[spend_column])\n clicks = number(row['Link clicks'])\n conversions = (number(row['Results']) if row['Result indicator'].strip() ==\n 'actions:offsite_conversion.fb_pixel_purchase' else ZERO)\n value = number(row['Purchases conversion value'])\n rate = config['eur_to_usd'] if currency == 'EUR' else Decimal(1)\n key = (platform, campaign.casefold(), group.casefold())\n if key not in units:\n units[key] = dict(platform=platform, campaign=campaign, ad_group=group,\n spend=ZERO, clicks=0, conversions=ZERO, conv_value=ZERO)\n unit = units[key]\n unit['spend'] += spend * rate\n unit['clicks'] += int(clicks)\n unit['conversions'] += conversions\n unit['conv_value'] += value * rate\n\n for unit in units.values():\n spend, conversions = unit['spend'], unit['conversions']\n cpa = spend / conversions if conversions else None\n unit['cpa'] = rounded(cpa) if cpa is not None else None\n unit['roas'] = rounded(unit['conv_value'] / spend, 4) if spend else None\n unit['reason'], unit['wasted'] = None, ZERO\n if not conversions and spend >= config['zero_conv_min_spend_usd']:\n unit['reason'], unit['wasted'] = 'zero_conversions', spend\n elif conversions > 0 and spend > conversions * config['cpa_multiple'] * config['target_cpa_usd']:\n unit['reason'] = 'high_cpa'\n unit['wasted'] = spend - conversions * config['target_cpa_usd']\n ordered = sorted(units.values(), key=lambda u: (-u['wasted'], -u['spend'], u['platform'],\n u['campaign'].casefold(), u['ad_group'].casefold()))\n for unit in ordered:\n for key in ('spend', 'conv_value', 'wasted'):\n unit[key] = rounded(unit[key])\n by_platform = {p: {k: sum((u[k] for u in ordered if u['platform'] == p), ZERO)\n for k in ('spend', 'wasted')} for p in ('google', 'meta')}\n totals = {k: sum((u[k] for u in ordered), ZERO) for k in ('spend', 'wasted')}\n totals['wasted_pct'] = rounded(100 * totals['wasted'] / totals['spend'], 1) if totals['spend'] else ZERO\n totals['by_platform'] = by_platform\n result = dict(units=ordered, totals=totals,\n top5=[[u['platform'], u['campaign'], u['ad_group']]\n for u in ordered if u['wasted'] > 0][:5], rows_dropped=drops)\n with open(sys.argv[2], 'w', encoding='utf-8') as stream:\n json.dump(result, stream, ensure_ascii=False, indent=2,\n default=lambda d: int(d) if d == d.to_integral_value() else float(d))\n stream.write('\\\\n')\n\n\nif __name__ == '__main__':\n solve()\nPY\npython solve.py input.json answer.json\npython - <<'PY'\nimport json\nx=json.load(open('answer.json'))\nprint(json.dumps({k:v for k,v in x.items() if k\"'!='\"'units'}, indent=2))\nassert all(x['units'][i]['wasted'] >= x['units'][i+1]['wasted'] for i in range(len(x['units'])-1))\nfor k in ('spend','wasted'):\n assert abs(sum(u[k] for u in x['units'])-x['totals'][k]) < 1e-8\nprint('Validated', len(x['units']), 'units and rounded totals.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\n#\"'!/usr/bin/env python3\n\"\"\"Aggregate weekly ad exports using exact decimal money arithmetic.\"\"\"\nimport csv\nimport io\nimport json\nimport re\nimport sys\nfrom datetime import date, datetime\nfrom decimal import Decimal, ROUND_HALF_UP, getcontext\n\ngetcontext().prec = 50\nZERO = Decimal(0)\n\n\ndef number(value):\n value = (value or '\"'').strip().replace(',', '')\n return ZERO if value in ('', '--') else Decimal(value)\n\n\ndef rounded(value, places=2):\n return value.quantize(Decimal(1).scaleb(-places), rounding=ROUND_HALF_UP)\n\n\ndef name(value):\n return ' '.join(value.split())\n\n\ndef records(path, google=False):\n with open(path, encoding='utf-8-sig', newline='') as stream:\n lines = stream.readlines()\n if google:\n start = next(i for i, line in enumerate(lines) if line.startswith('Day,'))\n lines = lines[start:]\n reader = csv.reader(io.StringIO(''.join(lines), newline=''))\n header = next(reader)\n previous = reader.line_num\n for cells in reader:\n # Retain the original record text: equivalent parsed cells need not be\n # exact duplicate export lines (quoting and whitespace may differ).\n raw = ''.join(lines[previous:reader.line_num]).rstrip('\\\\r\\\\n')\n previous = reader.line_num\n yield dict(zip(header, cells)), raw, header\n\n\ndef solve():\n with open('config.json', encoding='utf-8') as stream:\n config = json.load(stream, parse_float=Decimal, parse_int=Decimal)\n first = date.fromisoformat(config['week_start'])\n last = date.fromisoformat(config['week_end'])\n drops = dict(duplicates=0, out_of_range=0, non_data=0)\n units = {}\n for platform in ('google', 'meta'):\n seen = set()\n for row, raw, header in records(platform + '_ads.csv', platform == 'google'):\n if raw in seen:\n drops['duplicates'] += 1\n continue\n seen.add(raw)\n try:\n if platform == 'google':\n text = row.get('Day', '').strip()\n if not re.fullmatch(r'[A-Z][a-z]{2} \\\\d{1,2}, \\\\d{4}', text):\n raise ValueError('non-date')\n day = datetime.strptime(text, '%b %d, %Y').date()\n else:\n day = date.fromisoformat(row.get('Reporting starts', '').strip())\n except ValueError:\n drops['non_data'] += 1\n continue\n if not first <= day <= last:\n drops['out_of_range'] += 1\n continue\n if platform == 'google':\n campaign, group = name(row['Campaign']), name(row['Ad group'])\n currency = row['Currency code'].strip()\n spend = number(row['Cost'])\n clicks = number(row['Clicks'])\n conversions = number(row['Conversions'])\n value = number(row['Conv. value'])\n else:\n campaign, group = name(row['Campaign name']), name(row['Ad set name'])\n spend_column = next(h for h in header if re.fullmatch(r'Amount spent \\\\((USD|EUR)\\\\)', h))\n currency = spend_column[-4:-1]\n spend = number(row[spend_column])\n clicks = number(row['Link clicks'])\n conversions = (number(row['Results']) if row['Result indicator'].strip() ==\n 'actions:offsite_conversion.fb_pixel_purchase' else ZERO)\n value = number(row['Purchases conversion value'])\n rate = config['eur_to_usd'] if currency == 'EUR' else Decimal(1)\n key = (platform, campaign.casefold(), group.casefold())\n if key not in units:\n units[key] = dict(platform=platform, campaign=campaign, ad_group=group,\n spend=ZERO, clicks=0, conversions=ZERO, conv_value=ZERO)\n unit = units[key]\n unit['spend'] += spend * rate\n unit['clicks'] += int(clicks)\n unit['conversions'] += conversions\n unit['conv_value'] += value * rate\n\n for unit in units.values():\n spend, conversions = unit['spend'], unit['conversions']\n cpa = spend / conversions if conversions else None\n unit['cpa'] = rounded(cpa) if cpa is not None else None\n unit['roas'] = rounded(unit['conv_value'] / spend, 4) if spend else None\n unit['reason'], unit['wasted'] = None, ZERO\n if not conversions and spend >= config['zero_conv_min_spend_usd']:\n unit['reason'], unit['wasted'] = 'zero_conversions', spend\n elif conversions > 0 and spend > conversions * config['cpa_multiple'] * config['target_cpa_usd']:\n unit['reason'] = 'high_cpa'\n unit['wasted'] = spend - conversions * config['target_cpa_usd']\n ordered = sorted(units.values(), key=lambda u: (-u['wasted'], -u['spend'], u['platform'],\n u['campaign'].casefold(), u['ad_group'].casefold()))\n for unit in ordered:\n for key in ('spend', 'conv_value', 'wasted'):\n unit[key] = rounded(unit[key])\n by_platform = {p: {k: sum((u[k] for u in ordered if u['platform'] == p), ZERO)\n for k in ('spend', 'wasted')} for p in ('google', 'meta')}\n totals = {k: sum((u[k] for u in ordered), ZERO) for k in ('spend', 'wasted')}\n totals['wasted_pct'] = rounded(100 * totals['wasted'] / totals['spend'], 1) if totals['spend'] else ZERO\n totals['by_platform'] = by_platform\n result = dict(units=ordered, totals=totals,\n top5=[[u['platform'], u['campaign'], u['ad_group']]\n for u in ordered if u['wasted'] > 0][:5], rows_dropped=drops)\n with open(sys.argv[2], 'w', encoding='utf-8') as stream:\n json.dump(result, stream, ensure_ascii=False, indent=2,\n default=lambda d: int(d) if d == d.to_integral_value() else float(d))\n stream.write('\\\\n')\n\n\nif __name__ == '__main__':\n solve()\nPY\npython solve.py input.json answer.json\npython - <<'PY'\nimport json\nx=json.load(open('answer.json'))\nprint(json.dumps({k:v for k,v in x.items() if k\"'!='\"'units'}, indent=2))\nassert all(x['units'][i]['wasted'] >= x['units'][i+1]['wasted'] for i in range(len(x['units'])-1))\nfor k in ('spend','wasted'):\n assert abs(sum(u[k] for u in x['units'])-x['totals'][k]) < 1e-8\nprint('Validated', len(x['units']), 'units and rounded totals.')\nPY\"", "aggregated_output": "/bin/bash: line 134: python: command not found\n/bin/bash: line 135: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json\npython3 - <<'PY'\nimport json\nfrom decimal import Decimal\nx=json.load(open('answer.json'),parse_float=Decimal)\nfor key in ('spend','wasted'):\n assert sum(u[key] for u in x['units']) == x['totals'][key]\nassert all(x['units'][i]['wasted'] >= x['units'][i+1]['wasted'] for i in range(len(x['units'])-1))\nprint('Validated',len(x['units']),'units; drops:',x['rows_dropped'])\nprint('Totals:',x['totals'])\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json\npython3 - <<'PY'\nimport json\nfrom decimal import Decimal\nx=json.load(open('answer.json'),parse_float=Decimal)\nfor key in ('spend','wasted'):\n assert sum(u[key] for u in x['units']) == x['totals'][key]\nassert all(x['units'][i]['wasted'] >= x['units'][i+1]['wasted'] for i in range(len(x['units'])-1))\nprint('Validated',len(x['units']),'units; drops:',x['rows_dropped'])\nprint('Totals:',x['totals'])\nPY\"", "aggregated_output": "Validated 15 units; drops: {'duplicates': 5, 'out_of_range': 25, 'non_data': 2}\nTotals: {'spend': Decimal('3636.06'), 'wasted': Decimal('2178.61'), 'wasted_pct': Decimal('59.9'), 'by_platform': {'google': {'spend': Decimal('1301.91'), 'wasted': Decimal('603.93')}, 'meta': {'spend': Decimal('2334.15'), 'wasted': Decimal('1574.68')}}}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "agent_message", "text": "Created [solve.py](solve.py) and generated [answer.json](answer.json).\n\nVerified 15 units, ranking, and rounded totals using Python 3. This environment provides `python3`, but no `python` alias."}}
{"type": "turn.completed", "usage": {"input_tokens": 73073, "cached_input_tokens": 60032, "cache_write_input_tokens": 0, "output_tokens": 2120, "reasoning_output_tokens": 60}}