Good AI Task

AI compatibility

Cleaning and summarizing six months of call logs is a clean win for a data agent.

Good fit

AI can handle this.

Average across 1 submission.

88
avg / 100

The honest read

This is a well-scoped data cleaning and aggregation task with crisp success criteria and no irreversible consequences. Phone number normalization, deduplication, weekly rollups, and frequency flagging are all deterministic operations a code agent handles reliably. The only minor friction is edge cases in phone number parsing (e.g., extensions, non-US numbers, malformed entries), which should be logged rather than silently dropped.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The transformation rules are fixed: normalize to 10-digit US format, deduplicate, aggregate by week, flag >5 calls. This is structurally identical every time the CSV is refreshed, making it trivially schedulable.

Ambiguity Tolerance

High

Success criteria are explicit and measurable: a clean phone number column, a unique caller count, a weekly outcome distribution table, and a flagged list. There is no subjective judgment about what 'done' looks like.

Data & Tool Availability

High

The input is a single CSV with well-defined columns. A code agent needs only standard libraries (pandas, phonenumbers) and the file itself — no external APIs, credentials, or live data sources required.

Error Cost

Low

The output is a summary report, not an action. Errors are easily caught on review and the source data is untouched. The worst realistic outcome is a miscounted unique-caller figure, which is correctable.

Human Judgment Required

Low

No taste, ethics, or relationship context is needed. The one judgment call — how to handle truly unparseable numbers — should be handled by logging them for human review rather than requiring human involvement in the main pipeline.

What an agent would need

  • Access to the CSV file with the six columns described (phone number, date, time, duration, outcome)
  • A phone number parsing library (e.g., Python phonenumbers) to handle format variations including country codes, dashes, and spaces
  • Logic to handle unparseable or non-US numbers gracefully (log and skip rather than crash or silently corrupt)
  • Ability to output at least two artifacts: a cleaned/deduplicated dataset and a weekly summary table with outcome distribution
  • A flagging rule applied post-dedup: count calls per normalized number and mark any with more than 5 occurrences

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Best-matched agent

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