Good AI Task

AI compatibility

Cleaning a messy recruitment spreadsheet is a solid 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 cleaning and summarization task with explicit, verifiable success criteria — consistent formats, deduplication, and a defined output table. The messiness (inconsistent dates, status aliases, blank cells) is exactly the kind of structured variation that code agents handle reliably. The main risk is edge cases in status normalization that a human should spot-check before the output is used in decisions.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The transformation rules are consistent: parse two known date formats, map a finite set of status aliases to canonical values, remove blanks and duplicates, then aggregate. This structure is the same every run, making it highly automatable.

Ambiguity Tolerance

High

The user has specified the target date format (YYYY-MM-DD), the output schema (requisition_id, total_candidates, candidates_by_stage, avg_days_in_stage), and the cleaning goals. Success is objectively verifiable by inspecting the output.

Data & Tool Availability

High

The source is a Google Sheet, which is accessible via the Sheets API or a direct export. A code agent with Python (pandas) or a spreadsheet tool can read, transform, and write the data without any missing context.

Error Cost

Low

The output is a cleaned copy of existing data — the original sheet is not destroyed. Errors in normalization (e.g., a status alias mapped incorrectly) are visible in the output and easily corrected before downstream use.

Human Judgment Required

Low

The only judgment call is resolving ambiguous status aliases not in the known list, but with only 9 requisitions and a finite status vocabulary, a human can define the full mapping upfront or review a short list of unknowns flagged by the agent.

What an agent would need

  • Read access to the Google Sheet (OAuth credentials or a CSV export)
  • A complete or agent-inferred mapping of all status alias variants to canonical stage names
  • A code execution environment with pandas or equivalent for date parsing, deduplication, and aggregation
  • A definition of how 'avg_days_in_stage' is calculated (e.g., date entered stage vs. date exited, or current date for active candidates)
  • A write target for the cleaned data and summary table (new sheet tab, CSV, or dashboard)

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