Good AI Task

AI compatibility

Cleaning 8,000 survey responses is exactly the kind of mechanical data work AI handles well.

Good fit

AI can handle this.

Average across 1 submission.

91
avg / 100

The honest read

This task is almost entirely mechanical: deterministic text transformations with explicit rules, a clear success state, and low error cost given the original data is preserved. An agent with file I/O and a standard text-processing library can execute every step reliably at scale. The only minor uncertainty is edge cases in accent normalization, which are well-handled by existing Unicode libraries.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

Every transformation rule is explicitly stated and structurally identical across all 8,000 records. There is no per-record judgment required — the same logic applies uniformly, making this highly automatable.

Ambiguity Tolerance

High

Success criteria are precisely defined: lowercase, trimmed whitespace, ASCII-normalized accents, duplicate respondent IDs flagged. A non-human can verify each rule mechanically with no interpretation needed.

Data & Tool Availability

High

The agent needs only the input JSON file and standard libraries (e.g., Python's unicodedata, json, csv modules). No external APIs, credentials, or live context are required.

Error Cost

Low

The original file is preserved, so any mistake is fully reversible by re-running the process. The transformation log also makes errors auditable and correctable after the fact.

Human Judgment Required

Low

All rules are deterministic and leave no room for taste or interpretation. The only marginal edge case — unusual Unicode characters not covered by standard normalization — is rare and easily caught in a spot-check.

What an agent would need

  • Access to the input JSON file containing the 8,000 survey responses
  • A Python or Node.js runtime with Unicode normalization and CSV-writing libraries available
  • Clear definition of which JSON fields contain free-text answers to be cleaned
  • A defined output format for the transformation log CSV (e.g., respondent ID, field name, original value, cleaned value, transformation type)
  • Specification of what 'flagging duplicates' means in the output — whether duplicates are removed, marked, or listed separately in the log

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Best-matched agent

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