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 skills add Cyb3rWard0g/agent-jupyter-toolkit --skill data-exploration-analysisgit clone --depth 1 https://github.com/Cyb3rWard0g/agent-jupyter-toolkitWrote 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/cyb3rward0g/agent-jupyter-toolkit/data-exploration-analysis)<a href="https://agentmods.dev/skills/cyb3rward0g/agent-jupyter-toolkit/data-exploration-analysis"><img src="https://agentmods.dev/badge/skills/cyb3rward0g/agent-jupyter-toolkit/data-exploration-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cyb3rward0g/agent-jupyter-toolkit/data-exploration-analysis"><img src="https://agentmods.dev/badge/skills/cyb3rward0g/agent-jupyter-toolkit/data-exploration-analysis.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00079 | $0.00722 |
| Opus 5 | $0.00039 | $0.00361 |
| Sonnet 5 | $0.00016 | $0.00144 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
data-exploration-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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Exploration and Analysis
Use this skill to guide systematic analysis of structured datasets using notebook execution and DataFrame-based workflows. The goal is to move from raw data retrieval to validated insights while maintaining transparency, reproducibility, and analytical rigor.
Workflow
- You MUST complete each step in order.
- You MUST NOT skip directly to conclusions or visualizations before understanding the data.
- Always prefer incremental exploration over overly complex queries.
- All reasoning and conclusions MUST be documented in markdown cells.
- Reference documents (under
references/) MUST be read progressively — only when the current step calls for them. Do NOT read all reference documents upfront. Each step specifies which reference to consult; read it at that point and not before.
Step 1: Understand the dataset structure
Before performing analysis, establish a basic understanding of the dataset.
- Identify available tables or data sources.
- Inspect schema and column definitions.
- Determine key attributes such as timestamps, identifiers, and categorical fields.
- Identify potential join keys or relationships if multiple tables exist.
Use guidance from references/data-query-guide.md.
This step is complete only when the structure and basic semantics of the data are understood.
Step 2: Retrieve an exploratory dataset
Retrieve an initial dataset that allows you to observe the structure and distribution of the data.
- Start with broad queries rather than narrow filters.
- Avoid arbitrarily small limits that obscure patterns.
- Prefer retrieving data into a DataFrame for exploration.
- Ensure the dataset includes sufficient rows to capture variability.
Use guidance from references/data-query-guide.md.
Do NOT perform complex filtering or aggregation during this step.
Step 3: Perform exploratory analysis
Explore the dataset to understand distributions, anomalies, and relationships.
- Inspect row counts and column distributions.
- Identify categorical values and frequency patterns.
- Examine time ranges and event densities.
- Identify missing or unexpected values.
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.
- 10d ago First seen · 103 lines · 79 tokens per session scan A 0b6e2d8c2c72
data-exploration-analysis is a skill published in the GitHub repository Cyb3rWard0g/agent-jupyter-toolkit (24 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 722 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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