Borrowing it
Nothing to install: this file belongs to sprine/ontario-data-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sprine/ontario-data-mcp/main/.claude/skills/generating-smoke-tests/SKILL.mdgit clone --depth 1 https://github.com/sprine/ontario-data-mcpWrote 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/sprine/ontario-data-mcp/generating-smoke-tests)<a href="https://agentmods.dev/skills/sprine/ontario-data-mcp/generating-smoke-tests"><img src="https://agentmods.dev/badge/skills/sprine/ontario-data-mcp/generating-smoke-tests/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/sprine/ontario-data-mcp/generating-smoke-tests"><img src="https://agentmods.dev/badge/skills/sprine/ontario-data-mcp/generating-smoke-tests.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 | $0.00044 | $0.02239 |
| Opus 5.5 | $0.00018 | $0.00896 |
| Sonnet 5.5 | $0.00009 | $0.00448 |
| Haiku 4.5 | $0.00004 | $0.00224 |
Grade A, and why
generating-smoke-tests 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 2mo 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generating Smoke Tests
Generate a temporary Python smoke-test script that exercises multiple MCP tools against the live Ontario, Toronto, and Ottawa data APIs, run it, verify all assertions pass, then clean up.
When to Use
- During the release process (after unit tests pass, before version bump)
- After refactoring tool code, server wiring, or lifespan/context changes
- When verifying end-to-end connectivity to data.ontario.ca
Instructions
1. Generate the smoke test script
Write a file called smoke_test.py in the project root. The script must:
- Discover the latest dataset dynamically — fetch
https://data.ontario.ca/feeds/dataset.atom, parse the first<entry>to extract the dataset UUID from its<id>tag (format:https://data.ontario.ca/dataset/{uuid}) - Set up real dependencies —
httpx.AsyncClient,CKANClient,CacheManagerwith a temp DuckDB path - Mock only the FastMCP context — use the pattern from
tests/conftest.py:from unittest.mock import AsyncMock, MagicMock from ontario_data.portals import PORTALS ctx = MagicMock() ctx.report_progress = AsyncMock() ctx.lifespan_context = { "http_client": http_client, "portal_configs": PORTALS, "portal_clients": {}, "cache": cache, } - Access tools via the FastMCP API:
tool = await mcp.get_tool("tool_name")thenawait tool.fn(...) - Exercise this tool chain (each step asserts success before continuing):
- Ontario:
search_datasets(query="ontario", portal="ontario")— asserttotal_count > 0 - Ontario:
get_dataset_info(dataset_id=<uuid from feed>)— assert returns id or name - Ontario:
download_resource(resource_id=...)— pick first datastore-active resource; if none found, try the next feed entry (up to 5) until one with a datastore-active resource is found - Ontario:
query_cached(sql=...)—SELECT COUNT(*) as cntfrom the cached table - Ontario:
cache_info()— asserttable_count > 0 - Toronto:
search_datasets(query="toronto", portal="toronto")— asserttotal_count > 0 - Ottawa:
search_datasets(query="ottawa", portal="ottawa")— asserttotal_count > 0
- Ontario:
- Clean up — close the
httpx.AsyncClient;CacheManageruses short-lived connections and needs no close - Print progress — each step prints a summary line prefixed with two spaces
- Print "All smoke tests passed!" on success
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.
- 2mo ago First seen · 209 lines · 44 tokens per session scan A 5a602ba3b0f0
generating-smoke-tests is a skill published in the GitHub repository sprine/ontario-data-mcp (1 stars, last pushed 12d ago), licensed MIT. It adds 44 tokens to every session and 2,239 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 2026-08-31.
generating-smoke-tests compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| generating-smoke-testssprine/ontario-data-mcp | Skill | 1 | 12d ago | MIT | A | 44 | Claude Code |
| verify-omnigentomnigent-ai/omnigent | Skill | 11k | yesterday | Apache-2.0 | A | 100 | Claude Code, Codex |
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