| name | analyze-csv-sample |
| description | Guides fast exploration of tabular CSV data for lead-style or operational lists. Use when the user works with sample-data/leads-sample.csv, mentions CSV analysis, lead lists, filtering, or summarizing spreadsheet exports. |
Analyze the sample CSV (sample-data/leads-sample.csv)
File purpose
Example B2B-style leads: company, contact, industry, geography, size, pipeline stage, and dates. Safe fictional data (example.com emails).
Quick approaches
A — Agent-led (no install)
Ask for:
- Row count; column list and types (text vs numeric).
- Counts by
industry or region.
- Filter: e.g. “Qualified” only, or companies with
employees ≥ 50.
- Simple chart description or markdown table of top segments.
B — One-shot script (Python stdlib)
From repo root:
python3 scripts/quick_csv_summary.py sample-data/leads-sample.csv
Requires scripts/quick_csv_summary.py (included in this demo). Uses only the standard library.
Suggested prompts for demos
- “Summarize this CSV: how many rows per industry and region?”
- “List contacts in Healthcare with stage Negotiating.”
- “Which company has the largest employee count?”
Teaching angle
Show that large tables become manageable when the agent writes or runs small scripts, filters in conversation, or produces summaries—typical for lead gen and ops lists.
