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/ondata/ckan-mcp-server/data-explorationnpx skills add ondata/ckan-mcp-server --skill data-explorationgit clone --depth 1 https://github.com/ondata/ckan-mcp-serverWrote 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/ondata/ckan-mcp-server/data-exploration)<a href="https://agentmods.dev/skills/ondata/ckan-mcp-server/data-exploration"><img src="https://agentmods.dev/badge/skills/ondata/ckan-mcp-server/data-exploration.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 5.1 | $0.00050 | $0.01776 |
| Opus 5 | $0.00025 | $0.00888 |
| Sonnet 5 | $0.00010 | $0.00355 |
| Haiku 4.5 | $0.00005 | $0.00178 |
Grade A, and why
data-exploration 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 6d 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Exploration Skill
Overview
This skill provides comprehensive capabilities for Exploratory Data Analysis (EDA) on datasets from CKAN portals and direct CSV files. It focuses on understanding data structure, assessing quality, identifying patterns, and generating insights.
Capabilities
1. Dataset Discovery & Metadata Analysis
- CKAN Dataset Exploration: Search and retrieve datasets from CKAN portals
- Organization Analysis: Examine publishers and their datasets
- Metadata Validation: Assess completeness and quality of metadata
- Resource Evaluation: Analyze available data resources and formats
2. Structural Analysis
- Schema Discovery: Identify columns, data types, and relationships
- Data Profiling: Generate statistical summaries and distributions
- Format Assessment: Evaluate data formatting and standards compliance
- Field Analysis: Examine individual columns and their characteristics
3. Quality Assessment
- Completeness Checking: Identify missing values and null patterns
- Consistency Validation: Verify internal logical consistency
- Accuracy Evaluation: Cross-check totals and calculated fields
- Temporal Analysis: Assess time-series completeness and gaps
- Outlier Detection: Identify anomalous values and patterns
4. Statistical Analysis
- Descriptive Statistics: Calculate measures of central tendency and dispersion
- Distribution Analysis: Examine value distributions and patterns
- Correlation Analysis: Identify relationships between variables
- Trend Analysis: Detect temporal patterns and changes
- Segmentation: Group and compare data subsets
5. Insight Generation
- Pattern Identification: Discover meaningful patterns in data
- Anomaly Detection: Find unusual values or inconsistencies
- Quality Scoring: Assign quality metrics to datasets
- Recommendation Generation: Provide actionable improvement suggestions
Tools & Techniques
DuckDB Integration
For advanced CSV analysis using SQL:
# Schema analysis
duckdb -jsonlines -c "DESCRIBE SELECT * FROM read_csv('url')"
# Statistical summarization
duckdb -jsonlines -c "SUMMARIZE SELECT * FROM read_csv('url')"
# Data sampling
duckdb -jsonlines -c "SELECT * FROM read_csv('url') USING SAMPLE N"
# Custom queries
duckdb -jsonlines -c "SELECT column_name, COUNT(*), AVG(value) FROM read_csv('url') GROUP BY column_name"
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.
- 6d ago First seen · 253 lines · 50 tokens per session scan A 4af5ab1c0dbe
data-exploration is a skill published in the GitHub repository ondata/ckan-mcp-server (57 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 1,776 once invoked, about $0.0003 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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