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/archimedes-run/omniharness/data-analysisnpx skills add archimedes-run/omniHarness --skill data-analysisgit clone --depth 1 https://github.com/archimedes-run/omniHarnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/archimedes-run/omniharness/data-analysis)<a href="https://agentmods.dev/skills/archimedes-run/omniharness/data-analysis"><img src="https://agentmods.dev/badge/skills/archimedes-run/omniharness/data-analysis.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00069 | $0.02094 |
| Opus 5 | $0.00034 | $0.01047 |
| Sonnet 5 | $0.00014 | $0.00419 |
| Haiku 4.5 | $0.00007 | $0.00209 |
Grade A, and why
data-analysis 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 4d 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.
This is a copy
100% identical to data-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis Skill
Overview
This skill analyzes user-uploaded Excel/CSV files using DuckDB — an in-process analytical SQL engine. It supports schema inspection, SQL-based querying, statistical summaries, and result export, all through a single Python script.
Core Capabilities
- Inspect Excel/CSV file structure (sheets, columns, types, row counts)
- Execute arbitrary SQL queries against uploaded data
- Generate statistical summaries (mean, median, stddev, percentiles, nulls)
- Support multi-sheet Excel workbooks (each sheet becomes a table)
- Export query results to CSV, JSON, or Markdown
- Handle large files efficiently with DuckDB's columnar engine
Workflow
Step 1: Understand Requirements
When a user uploads data files and requests analysis, identify:
- File location: Path(s) to uploaded Excel/CSV files under
/mnt/user-data/uploads/ - Analysis goal: What insights the user wants (summary, filtering, aggregation, comparison, etc.)
- Output format: How results should be presented (table, CSV export, JSON, etc.)
- You don't need to check the folder under
/mnt/user-data
Step 2: Inspect File Structure
First, inspect the uploaded file to understand its schema:
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action inspect
This returns:
- Sheet names (for Excel) or filename (for CSV)
- Column names, data types, and non-null counts
- Row count per sheet/file
- Sample data (first 5 rows)
Step 3: Perform Analysis
Based on the schema, construct SQL queries to answer the user's questions.
Run SQL Query
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action query \
--sql "SELECT category, COUNT(*) as count, AVG(amount) as avg_amount FROM Sheet1 GROUP BY category ORDER BY count DESC"
Generate Statistical Summary
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action summary \
--table Sheet1
What ships with it
1 file 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.
- 4d ago First seen · 249 lines · 69 tokens per session scan A d9383f31df40
data-analysis is a skill published in the GitHub repository archimedes-run/omniHarness (6 stars, last pushed 9d ago), licensed MIT. It adds 69 tokens to every session and 2,094 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to data-analysis, differing in 0 lines, and is treated as a copy.
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