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 Aryan-Jhaveri/mcp-statcan --skill datasetsgit clone --depth 1 https://github.com/Aryan-Jhaveri/mcp-statcanWrote 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/aryan-jhaveri/mcp-statcan/datasets)<a href="https://agentmods.dev/skills/aryan-jhaveri/mcp-statcan/datasets"><img src="https://agentmods.dev/badge/skills/aryan-jhaveri/mcp-statcan/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/aryan-jhaveri/mcp-statcan/datasets"><img src="https://agentmods.dev/badge/skills/aryan-jhaveri/mcp-statcan/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 | $0.00067 | $0.01211 |
| Opus 5.5 | $0.00027 | $0.00484 |
| Sonnet 5.5 | $0.00013 | $0.00242 |
| Haiku 4.5 | $0.00007 | $0.00121 |
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 today.
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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datasets
A dataset gives files and database sources a queryable interface; a saved view
turns the query into reusable test input. That is useful when filtering,
joining, or refreshing rows is part of the test. A one-off CSV/JSON run can
stay with collection run -d; a dataset should earn its extra configuration.
Choose the data and execution path
- Prefer local file sources for reproducible fixtures. A live database is
useful when current data is the point, but adds authentication, entitlement,
network, and credential requirements even in a local
.dataset.yaml. Checkpostman dataset <action> -hand the actual source capability before promising a workflow; source support can vary with the installed CLI. - Use the default federated SQLite query path to join supported sources.
--source <name>instead sends SQL to one source in its native dialect. Switching modes to silence a SQL error changes query semantics. JDBC requires the native path and cannot join in the federated layer; native database connectors can support federation. - Choose credentials before choosing a database connector. JDBC variables
and properties accept shared-vault references such as
vault:<vaultId>/<secretId>, resolved at query time. Native database credentials are stored literally in dataset configuration on the documented CLI surface; inspect current support before use, and keep secret-bearing configuration out of Git. Literal values passed through stdin avoid argv exposure but can still be persisted in YAML. See variables for vault handling. A connector that enables a join may have a worse credential boundary than JDBC; do not hide that tradeoff. - Choose iteration or script access deliberately. The paired
--iteration-data-dataset <path-or-id>and--iteration-data-view <name-or-id>flags feed one row per collection iteration, with columns exposed as variables. They exclude-dand require a parameter-free view because the runner has no query-parameter input. Repeatable--dataset <path-or-dir>instead exposespm.datasets()to scripts; it does not itself create row-driven iterations.
What ships with it
1 file 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.
- today First seen · 99 lines · 67 tokens per session scan A 0845b92bc914
datasets is a skill published in the GitHub repository Aryan-Jhaveri/mcp-statcan (7 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 1,211 once invoked, about $0.0003 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 2026-10-27.
Other skills, from other repositories
review-prs
Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
reproduce-bug
Reproduce a reported bug in googleapis/mcp-toolbox and decide whether it is real, delivering an evidence-backed verdict: confirmed, already fixed, misconfiguration, client-side, works as intended, not reproducible, or blocked. Use whenever a maintainer asks you to reproduce, verify, confirm, or investigate a bug…
fetch-data
A workflow that turns a plain-language request for business data into a database query, runs it in a data warehouse or lake, and returns the results for analysis.
dashboard-design
Design and code a self-contained HTML dashboard for DeepSQL — ground on the schema, verify SQL, then emit it as a shell plus one verified widget block at a time so the canvas builds progressively.
query-odps
A set of rules for writing and running read-only MaxCompute, also called ODPS, SQL queries. MaxCompute is a cloud data-warehouse service; the workflow covers query structure, partitions, filtering, joins, performance, and recovery from failures.