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SKILL verified Apache-2.0 Self-run

Csv Processor

skill-chronoaiproject-ornn-csv-processor · by ChronoAIProject

Read a CSV file from disk, compute per-column min/mean/max for every numeric column, emit the result as JSON. Stdlib-only Python; no pandas, no numpy. Demonstrates the simplest possible "give me a file path, get back structured analysis" skill — a deliberate baseline for any skill that processes tabular data locally without an LLM in the loop.

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Install

$ agentstack add skill-chronoaiproject-ornn-csv-processor

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
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2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

csv-processor

A deterministic, network-free skill — the easiest case. Useful as a control when debugging the agent ↔ skill plumbing: if this fails, the failure is in the runner, not the skill.

Contract

Input (single CLI argument):

python src/main.py /path/to/data.csv

The script reads argv[1] as a filesystem path. CSV must have a header row.

Output (stdout, JSON):

{
  "rowCount": 1234,
  "columns": {
    "price": { "min": 1.23, "mean": 42.0, "max": 999.99, "count": 1234 },
    "quantity": { "min": 0, "mean": 7.5, "max": 100, "count": 1230 }
  }
}

Only numeric columns appear under columns. count is the number of cells that parsed successfully (numeric); non-numeric / blank cells are skipped.

Errors — written to stderr as {"error": "..."} and exit code 1.

Run locally

cd examples/csv-processor
python src/main.py sample.csv

A sample.csv is bundled so the example runs out of the box.

Adapt this

  • Different aggregations — add median, p95, stddev; same shape, more keys per column.
  • Streaming — for huge files, replace the in-memory accumulation with a running-mean update; one extra variable per column, same output shape.
  • Source other than disk — accept a URL or stdin instead of argv[1]. The aggregation core doesn't care.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.