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 ai-readinessgit 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/ai-readiness)<a href="https://agentmods.dev/skills/aryan-jhaveri/mcp-statcan/ai-readiness"><img src="https://agentmods.dev/badge/skills/aryan-jhaveri/mcp-statcan/ai-readiness/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/ai-readiness"><img src="https://agentmods.dev/badge/skills/aryan-jhaveri/mcp-statcan/ai-readiness.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 | $0.00064 | $0.00577 |
| Opus 5.5 | $0.00026 | $0.00231 |
| Sonnet 5.5 | $0.00013 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
ai-readiness 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Readiness
An agent must choose an operation, supply valid inputs, and interpret failures from the contract it can see. Postman's readiness check exposes gaps in that context—such as missing examples or ambiguous parameters—so improvements live in the shared contract instead of being rediscovered in each agent session.
Skip scoring for a typo fix, a one-off endpoint check, or a task that changes neither the contract nor agent usability; inspect the relevant fields or use api-testing instead. Score when prioritizing consumability improvements, checking their effect, or enforcing an agreed readiness gate.
- Score the artifact consumers actually use.
postman collection ai-readiness <path-or-id>reads a collection, including a local v3 directory;postman spec ai-readiness <path-or-id>reads an OpenAPI spec directly. Generating a collection solely to score a spec adds a second artifact without improving the evidence. Use the installed command's-hfor options. - Use the reported
readinessscore and bucket (Limited, Fair, Good, Excellent). Keepconfidenceseparate: low confidence means signals could not be measured, so even a high score may leave consequential unknowns. Report low documentation coverage when flagged; it modifies the score rather than acting as an independent pass/fail gate. - Prioritize gaps that would change a caller's behavior: unclear required inputs, missing auth instructions, or errors with no recovery guidance. Establish the actual behavior before adding descriptions or examples; invented certainty can raise apparent completeness while misleading agents. See api-documentation for improving the authoritative contract and collection-schema-v3 for saved examples.
- Re-run against the edited artifact to show which measured gaps closed. A readiness score measures consumability, not live correctness; use api-testing when the claim also depends on API behavior.
- A
--min-scoregate is useful when the team wants to prevent readiness regressions. Choose the threshold from its baseline and expectations; keep it separate from tests and lint through ci-integration so a failure identifies the kind of regression.
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 · 47 lines · 64 tokens per session scan A ac93e3850604
ai-readiness is a skill published in the GitHub repository Aryan-Jhaveri/mcp-statcan (7 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 577 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.
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