Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/chronoaiproject/ornn/csv-processornpx skills add ChronoAIProject/Ornn --skill csv-processorgit clone --depth 1 https://github.com/ChronoAIProject/OrnnWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00079 | $0.00488 |
| Opus 5 | $0.00039 | $0.00244 |
| Sonnet 5 | $0.00016 | $0.00098 |
| Haiku 4.5 | $0.00008 | $0.00049 |
Grade A, and why
csv-processor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 59 lines · 79 tokens per session scan A 9549d64cb09d
csv-processor is a skill published in the GitHub repository ChronoAIProject/Ornn (20 stars, last pushed 9d ago), licensed Apache-2.0. It adds 79 tokens to every session and 488 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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