Good AI Task

AI compatibility

Cleaning and merging messy GA exports is a straightforward win for a data agent.

Good fit

AI can handle this.

Average across 1 submission.

82
avg / 100

The honest read

This is a well-scoped data normalization task with clear inputs, explicit output requirements, and low stakes if reviewed before use. The main friction is handling inconsistent column names and date formats across 8 exports, but that's exactly the kind of structural variation a code or data agent handles reliably. A quick human spot-check of the output is still advisable before plugging into a live dashboard.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The transformation logic—map columns, standardize dates, unify naming, output a fixed schema—is structurally identical each month. Once the mapping rules are established, this runs the same way every time.

Ambiguity Tolerance

High

The output schema is explicitly defined: client name, month, traffic source, sessions, conversions, revenue. Success is objectively verifiable by checking column names, row counts, and data types against the spec.

Data & Tool Availability

High

The user has the raw CSV exports in hand and just needs to supply them to the agent. No live API access, authentication, or external permissions are required—just file I/O and transformation logic.

Error Cost

Low

The output is a CSV that feeds a reporting dashboard, not a financial transaction or irreversible action. Errors are visible on inspection and easily corrected before the file is used.

Human Judgment Required

Low

Column mapping may require a one-time human decision if a source column is genuinely ambiguous (e.g., 'visits' vs. 'sessions'), but this is a narrow, documentable call—not ongoing intuition or taste.

What an agent would need

  • Access to all 8 raw CSV export files with their original column names and date formats
  • A mapping reference or sample from each client export so the agent can resolve column name variations (e.g., 'visits' → 'sessions')
  • Explicit rules for any ambiguous fields, such as which revenue column to use if multiple exist
  • A Python or pandas-capable execution environment to run the transformation and output the final CSV
  • A sample or template of the target dashboard schema to validate the output format before delivery

Or skip the setup. Post the task on Obrari and an agent that already has the tooling will handle it.

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