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 ai-analyst-lab/ai-analyst --skill datasetsgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/datasets)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/datasets"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/datasets/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/ai-analyst-lab/ai-analyst/datasets"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/datasets.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 | $0.00168 | $0.00880 |
| Opus 5 | $0.00084 | $0.00440 |
| Sonnet 5 | $0.00034 | $0.00176 |
| Haiku 4.5 | $0.00017 | $0.00088 |
Grade A, and why
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 2d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Datasets
Purpose
List all connected datasets with their status, table counts, and last analysis date.
When to Use
Invoke as /datasets when the user wants to see what datasets are available.
Instructions
Step 1: Discover available datasets
The system supports two discovery paths:
Path A: Registry-first (preferred)
- Read
data_sources.yamlto get the official list of registered sources - If the file exists and has entries, use this as your source of truth
Path B: Brain-first (fallback when registry is empty)
- If
data_sources.yamlis empty or missing, scan.knowledge/datasets/directory - Each subdirectory represents a dataset (directory name = dataset ID)
- Read each dataset's
manifest.yamlto get connection details and metadata
Use whichever path yields results. Many installations have datasets in .knowledge/datasets/ but an empty data_sources.yaml registry — this is normal during initial setup or when datasets are added manually.
Step 2: Read the active pointer
Read .knowledge/active.yaml to determine which dataset is currently active.
Step 3: Enrich with manifest data
For each discovered dataset (whether from registry or directory scan), read .knowledge/datasets/{name}/manifest.yaml to get:
display_name— human-readable nameconnection.type— connection type (csv, duckdb, postgres, snowflake, bigquery, databricks, redshift, mssql, mysql)connection.databaseor other connection-specific fieldssummary.table_count— number of tablessummary.date_range— temporal coverage (if available)summary.row_counts— per-table row counts (if profiled)summary.last_updated— when manifest was last written
If a manifest is missing or incomplete, show what you can determine from the directory structure and note that the dataset needs profiling.
Step 4: Display the list
Connected Datasets:
* your_dataset (active)
Your Dataset Name — {table_count} tables, {date_range}
Connection: {type} ({database})
Analyses: 0
- {other_dataset}
{display_name} — {table_count} tables, {date_range}
Connection: {type} ({details})
Analyses: {count}
Commands:
/switch-dataset {name} — switch active dataset
/connect-data — connect a new dataset
/data — inspect active dataset schema
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.
- 2d ago First seen · 77 lines · 168 tokens per session scan A 2c69f80c223b
datasets is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 168 tokens to every session and 880 once invoked, about $0.0008 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 2026-09-12.
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