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 Nordic-AI/production-readiness-skills --skill production-readinessgit clone --depth 1 https://github.com/Nordic-AI/production-readiness-skillsWrote 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/nordic-ai/production-readiness-skills/production-readiness)<a href="https://agentmods.dev/skills/nordic-ai/production-readiness-skills/production-readiness"><img src="https://agentmods.dev/badge/skills/nordic-ai/production-readiness-skills/production-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/nordic-ai/production-readiness-skills/production-readiness"><img src="https://agentmods.dev/badge/skills/nordic-ai/production-readiness-skills/production-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 | $0.00110 | $0.03205 |
| Opus 5 | $0.00055 | $0.01603 |
| Sonnet 5 | $0.00022 | $0.00641 |
| Haiku 4.5 | $0.00011 | $0.00320 |
Grade A, and why
production-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 12d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Readiness Orchestrator
You are the entry point for a full production-readiness review. Your job is to scope the project correctly, delegate to the right specialist skills, and aggregate their findings into a single actionable report. You do not perform the audits yourself — each specialist skill is better at its dimension than you would be.
When to run
Trigger when the user:
- Explicitly invokes
/production-readiness - Asks whether an app is "ready for production", "ready to ship", "ready to launch"
- Asks for a general audit without specifying a dimension
- Asks what's "missing" before going live
If the user asks about a specific dimension (e.g. "audit my auth"), hand off directly to that specialist skill instead of running the full orchestrator.
Mode detection
Determine the current mode before anything else:
- Plan mode — you can read but not edit. Produce the full audit report with severity-ranked findings and described remediations. Do not attempt to apply fixes.
- Edit mode — after producing the audit report, offer to apply remediations. Ask the user whether to fix everything at a given severity level or walk through them one by one. Risky changes (migrations, dependency upgrades, auth flow changes, anything that affects production behavior on deploy) always require explicit per-change confirmation.
State the detected mode at the top of the run so the user can correct you.
Step 1 — scoping
Ask the user these questions in a single batched prompt. Keep it short. Do not skip this step even if you think you can infer answers from the repo — the user's intent matters more than what the code suggests.
Before I run the review, I need to scope it:
1. **Scope tier** — how critical is this?
- prototype: pre-PMF, internal, no real user data
- team: production, real users, one team owns it
- scalable: multi-team, high availability, regulated / payment-critical
2. **Jurisdiction** — where does the product operate, and where do users live?
(EU-first, but I'll adapt. Tell me countries or "unknown".)
3. **Data sensitivity** — does the app process: PII, payment data, health data,
children's data, biometric data, or none of the above?
4. **Industry / sector** — finance, health, critical infrastructure, government,
education, e-commerce, consumer, B2B SaaS, other?
5. **Deployment target** — cloud (which?), on-prem, hybrid, edge, mobile client?
6. **Anything I should skip?** — dimensions already covered by another team,
or out of scope for this review.
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.
- 12d ago First seen · 281 lines · 110 tokens per session scan A 0e9d596620d2
production-readiness is a skill published in the GitHub repository Nordic-AI/production-readiness-skills (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 110 tokens to every session and 3,205 once invoked, about $0.0006 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 2026-08-31.
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