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/ai-analyst-lab/ai-analyst-plugin/compare-datasetsnpx skills add ai-analyst-lab/ai-analyst-plugin --skill compare-datasetsgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/compare-datasets)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/compare-datasets"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/compare-datasets.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 | $0.00080 | $0.02763 |
| Opus 5 | $0.00040 | $0.01381 |
| Sonnet 5 | $0.00016 | $0.00553 |
| Haiku 4.5 | $0.00008 | $0.00276 |
Grade A, and why
compare-datasets 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Compare Datasets
Purpose
Compare metrics, findings, and patterns across two or more connected datasets. Helps identify cross-dataset patterns (e.g., "conversion funnel behavior is similar across both product lines") and dataset-specific anomalies.
When to Use
- User says
/compare-datasetsor "compare across datasets" - After analyzing multiple datasets, to find commonalities
- When the user asks "is this pattern unique to this dataset?"
Invocation
/compare-datasets — compare active dataset with all others
/compare-datasets {id1} {id2} — compare two specific datasets
/compare-datasets metric={name} — compare a specific metric across datasets
Instructions
Step 0: Feasibility Check (NEW)
Before running the full 6-step workflow, verify the comparison is possible:
-
Check minimum requirements:
- At least 2 datasets must be connected (check
.knowledge/datasets/) - If only 1 dataset: "Only one dataset connected. Use
/connect-datato add another."
- At least 2 datasets must be connected (check
-
For data-intensive comparisons (e.g., retention, conversion funnels):
- Verify temporal span: Each dataset needs sufficient history (e.g., 3-6 months for retention)
- Verify sample size: Small datasets (<30 rows) may not produce meaningful comparisons
- Verify schema compatibility: Key fields must exist in both datasets
-
If comparison is BLOCKED:
- State why upfront: "Cannot compare retention — Dataset B has only 27 days of data vs. Dataset A's 12 months. Retention requires 3-6 months minimum."
- Provide decision criteria: "We can compare when Dataset B has [specific requirement]."
- Skip to Step 6 (Present Results) with a diagnostic report instead of attempting analysis
Rationale: This saves time and avoids misleading partial comparisons. Better to diagnose "why we can't compare" upfront than to discover data gaps in Step 4.
Step 1: Identify Datasets to Compare
- Read
.knowledge/datasets/to enumerate all connected datasets. - If specific datasets are named, validate they exist.
- If no datasets specified, use active + all others.
- List datasets with their connection status (connected vs. not connected).
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 · 244 lines · 80 tokens per session scan A 641642c85139
compare-datasets is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 10d ago), licensed MIT. It adds 80 tokens to every session and 2,763 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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