Csv report writer

Skill baronguyen001/ai-automation-skills/skills/csv-report-writer

8 production-tested Claude skills: automation, Gemini cost/structured output, OSS bounty scouting, ML validation.

Install

npx -y skills add baronguyen001/ai-automation-skills --skill csv-report-writer

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Turn a run's list of result dicts into a schema'd CSV and a Markdown table from one column spec - declare columns once, emit both, with stable ordering and safe escaping, stdlib only, no pandas. Use when the user asks to write results to CSV, export a report, make a markdown summary table, or save a run's output as a spreadsheet.

SKILL.md

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CSV Report Writer

Use this skill when a job produces a list of result rows and you want a tidy CSV for spreadsheets plus a Markdown table for a PR comment or Telegram digest - from a single column definition, so the two outputs never drift. You declare the columns (key, header, optional formatter) once; the helper emits both, in a stable column order, with proper CSV quoting and pipe-escaping for Markdown. No pandas.

When to invoke

User says: "write the results to a CSV", "export a report", "make a markdown table of this", "save the run output as a spreadsheet".
Code in the conversation has a list[dict] (or list of objects) it currently prints ad hoc.

When NOT to invoke

The data needs real dataframe work - joins, group-bys, pivots; reach for pandas/polars instead.
A single scalar result, where a CSV/table is overkill.

Concrete example

User input:

I have a list of scanned tokens with a score and price. Save a CSV and also give me a markdown table for the Telegram digest.

Output:

# Copy assets/report.py into your project, then:
from report import Column, write_csv, to_markdown

columns = [
    Column("symbol", "Symbol"),
    Column("score", "Score", fmt=lambda v: f"{v:.1f}"),
    Column("price_usd", "Price", fmt=lambda v: f"${v:,.4f}"),
]
rows = [
    {"symbol": "ABC", "score": 8.4, "price_usd": 0.0123},
    {"symbol": "XYZ", "score": 6.1, "price_usd": 1.5},
]

write_csv("scan.csv", columns, rows)        # schema'd CSV, stable column order
print(to_markdown(columns, rows))           # same columns, Markdown table

Both outputs use the exact same columns in the exact same order, so the CSV and the digest table always agree.

Pattern to apply

Declare each Column(key, header, fmt=...) once; both writers consume the same list, so headers and order stay in sync.
Keep the column order stable and explicit - do not rely on dict insertion order of arbitrary rows.
Let the fmt callable own presentation (currency, rounding); store raw values in the row dicts.
Use csv.writer for correct quoting; escape | and newlines for the Markdown table so it never breaks layout.
Treat a missing key as an empty cell rather than crashing, so partial rows still report.

Reference: assets/report.py.

Source

Distilled from production use across the author's automation projects. v1.0.0. See also: [[pr-body-formatter]], [[sqlite-state]], [[telegram-alerter]].

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