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Text World Cup final scores to a Signal group

Exact prompt

Sandboxed mock (no real messaging). feed/poll_00.json .. poll_05.json are successive snapshots of a mock ESPN scoreboard, one per 30-minute poll. Write notifier.py that processes the polls in order (simulating each poll) and sends every match's final result exactly once to the Signal group "wc-friends" using: python3 signal_send.py wc-friends "<text>" (text format: "FINAL: <match> <score>"). A match counts as final when status starts with "final". Keep state so repeated polls never resend. Run it once over all polls, then run it again to prove no duplicates are sent.

Task prompt given to the agent (headless, offline, synthetic fixture inputs): Sandboxed mock (no real messaging). feed/poll_00.json .. poll_05.json are successive snapshots of a mock ESPN scoreboard, one per 30-minute poll. Write notifier.py that processes the polls in order (simulating each poll) and sends every match's final result exactly once to the Signal group "wc-friends" using: python3 signal_send.py wc-friends "<text>" (text format: "FINAL: <match> <score>"). A match counts as final when status starts with "final". Keep state so repeated polls never resend. Run it once over all polls, then run it again to prove no duplicates are sent. Inspired by https://agentusecases.ai (listing: 'Text every World Cup final score to a friends' Signal group', github.com source) (unverified; not checked with the author). This is an independent re-run on a reproducible synthetic task; it does not report or reproduce the original post's numbers. Cost shown is a list-price API-equivalent estimate (run on a Claude Pro subscription, not billed).

Success criteria: All automated rubric checks pass: exactly 3 messages (no duplicates after rerun); messages match expected text and order; all to wc-friends; notifier.py exists.

Best model for this task so far: Gemma 4 31B Instruct (Venice) (success, $0.0021, 57s).

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All runs (7)

100% pass rate across 7 eligible runs: 7 pass, 0 partial, 0 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
AClaude Haiku 5.5default · thinking on$0.004215s0Fully autonomouspassTrust 80%2026-10-16
XGrok 4.3 (Venice)$0.008612s0Fully autonomouspassTrust 80%2026-10-15
QQwen3 Coder 480B Turbo (Venice)$0.003216s0Fully autonomouspassTrust 80%2026-10-15
DDeepSeek V4.1 Flash (Venice)$0.002610s0Fully autonomouspassTrust 80%2026-10-14
GGemma 4 31B Instruct (Venice)$0.002157s0Fully autonomouspassTrust 80%2026-10-14
AClaude Opus 5.5$0.1322s0Fully autonomouspassTrust 80%2026-10-14
AClaude Sonnet 5.5$0.0514s0Fully autonomouspassTrust 80%2026-10-14

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