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 axiomhq/cli --skill explore-datasetgit clone --depth 1 https://github.com/axiomhq/cliWrote 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/axiomhq/cli/explore-dataset)<a href="https://agentmods.dev/skills/axiomhq/cli/explore-dataset"><img src="https://agentmods.dev/badge/skills/axiomhq/cli/explore-dataset/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/axiomhq/cli/explore-dataset"><img src="https://agentmods.dev/badge/skills/axiomhq/cli/explore-dataset.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.00939 |
| Opus 5.5 | $0.00016 | $0.00376 |
| Sonnet 5.5 | $0.00008 | $0.00188 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
explore-dataset 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 1mo 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dataset Exploration
Systematically explore an Axiom dataset to understand its structure, content, and potential use cases.
Arguments
When invoked with a dataset name (e.g., /explore-dataset logs), the name is available as $ARGUMENTS.
Exploration Protocol
1. List Available Datasets
If no dataset specified, list what's available:
axiom dataset list -f json
2. Schema Discovery
Always start here. Discover actual field names and types:
axiom query "['<dataset>'] | getschema" --start-time -1h
Identify:
- Field names and types
- Dotted fields requiring bracket notation
- Timestamp fields
- Key dimensions (service, status, level)
OTel trace data: If schema contains trace_id, span_id, attributes.*, note that:
- Service fields are promoted: use
['service.name']not['resource.service.name'] - Custom attributes:
['attributes.custom']['field']withtostring()for aggregations - See
axiom-aplskill's OTel reference for field mappings
3. Sample Data
Examine actual values:
axiom query "['<dataset>'] | limit 10" --start-time -1h -f json
Look for:
- Data structure and relationships
- Field value formats
- Data quality issues
4. Volume Analysis
Understand data volume patterns:
axiom query "['<dataset>'] | summarize count() by bin(_time, 1h) | sort by _time asc" --start-time -24h
Analyze:
- Event volume over time
- Data freshness
- Collection gaps
5. Categorical Field Analysis
For each key categorical field (status, level, service):
axiom query "['<dataset>'] | summarize count() by <field> | top 20 by count_" --start-time -1h
Identify:
- Value distributions
- Cardinality
- Key dimensions for filtering
6. Numerical Field Statistics
For numeric fields (duration, bytes, count):
axiom query "['<dataset>'] | summarize count(), min(<field>), max(<field>), avg(<field>), percentiles(<field>, 50, 95, 99)" --start-time -1h
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.
- 1mo ago First seen · 142 lines · 40 tokens per session scan A 0d337d71e58e
explore-dataset is a skill published in the GitHub repository axiomhq/cli (60 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 939 once invoked, about $0.0002 per session on Opus 5.5. 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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