OpenLoomi is an open-source desktop AI coworker that connects work tools, gathers context, and highlights decisions or actions needing attention. It is for people managing work across multiple apps, and its catalogue add-ons extend the resident desktop for agent frameworks such as Claude Code, Codex, OpenCode, Hermes, and OpenClaw.
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/melandlabs/openloomi/data_analysisnpx skills add melandlabs/openloomi --skill data_analysisgit clone --depth 1 https://github.com/melandlabs/openloomiWrote 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/melandlabs/openloomi/data_analysis)<a href="https://agentmods.dev/skills/melandlabs/openloomi/data_analysis"><img src="https://agentmods.dev/badge/skills/melandlabs/openloomi/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 | $0.00044 | $0.01076 |
| Opus 5 | $0.00022 | $0.00538 |
| Sonnet 5 | $0.00009 | $0.00215 |
| Haiku 4.5 | $0.00004 | $0.00108 |
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 5d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- data_analysis — 100% identical, 0 lines differ
- data_analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis Skill
Comprehensive data analysis toolkit using Polars - a blazingly fast DataFrame library. This skill provides instructions, reference documentation, and ready-to-use scripts for common data analysis tasks.
Iteration Checkpoints
| Step | What to Present | User Input Type |
|---|---|---|
| Data Loading | Shape, columns, sample rows | "Is this the right data?" |
| Data Exploration | Summary stats, data quality issues | "Any columns to focus on?" |
| Transformation | Before/after comparison | "Does this transformation look correct?" |
| Analysis | Key findings, charts | "Should I dig deeper into anything?" |
| Export | Output preview | "Ready to save, or any changes?" |
Quick Start
import polars as pl
from polars import col
# Load data
df = pl.read_csv("data.csv")
# Explore
print(df.shape, df.schema)
df.describe()
# Transform and analyze
result = (
df.filter(col("value") > 0)
.group_by("category")
.agg(col("value").sum().alias("total"))
.sort("total", descending=True)
)
# Export
result.write_csv("output.csv")
When to Use This Skill
- Loading datasets (CSV, JSON, Parquet, Excel, databases)
- Data cleaning, filtering, and transformation
- Aggregations, grouping, and pivot tables
- Statistical analysis and summary statistics
- Time series analysis and resampling
- Joining and merging multiple datasets
- Creating visualizations and charts
- Exporting results to various formats
Skill Contents
Reference Documentation
Detailed API reference and patterns for specific operations:
reference/loading.md- Loading data from all supported formatsreference/transformations.md- Column operations, filtering, sorting, type castingreference/aggregations.md- Group by, window functions, running totalsreference/time_series.md- Date parsing, resampling, lag featuresreference/statistics.md- Correlations, distributions, hypothesis testing setupreference/visualization.md- Creating charts with matplotlib/plotly
What ships with it
8 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.
- 5d ago First seen · 158 lines · 44 tokens per session scan A 365334b22d4d
data_analysis is a skill published in the GitHub repository melandlabs/openloomi (1,020 stars, last pushed 4d ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,076 once invoked, about $0.0002 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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