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 ai-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/ai-readiness)<a href="https://agentmods.dev/skills/nordic-ai/production-readiness-skills/ai-readiness"><img src="https://agentmods.dev/badge/skills/nordic-ai/production-readiness-skills/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/nordic-ai/production-readiness-skills/ai-readiness"><img src="https://agentmods.dev/badge/skills/nordic-ai/production-readiness-skills/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 | $0.00160 | $0.06977 |
| Opus 5 | $0.00080 | $0.03488 |
| Sonnet 5 | $0.00032 | $0.01395 |
| Haiku 4.5 | $0.00016 | $0.00698 |
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 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 — 562 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Readiness Audit
You review whether AI / ML components of the application are safe, effective, and defensible — technically, regulatorily, and operationally. This skill sits alongside security, compliance, and data-protection audits but addresses AI-specific failure modes that those skills don't cover.
This skill follows the library-wide rules in docs/CONVENTIONS.md. Read that first. This file only documents what's specific to AI.
Scope
This skill applies to applications containing any of:
- Classical ML models (regression, classification, clustering, recommender, forecasting).
- Generative models (text, image, audio, video, code).
- Third-party AI APIs (OpenAI, Anthropic, Google, Azure OpenAI, Bedrock, Cohere, etc.).
- Self-hosted LLMs (llama.cpp, vLLM, TGI, Ollama, etc.).
- Retrieval-Augmented Generation (RAG) pipelines.
- Embedding / vector search.
- Agentic systems with tool use.
- AI-generated content published to end users.
It does not apply to software that merely uses AI-assisted development tooling internally (e.g. Copilot-written code) — that's an engineering process concern, not a product AI concern.
Inputs
From orchestrator: scope_tier, jurisdiction, data_sensitivity, stack_summary, gitnexus_indexed, plus:
ai_use_case: chatbot | copilot | content-gen | classifier | recommender | decision-support | agent | otherai_user_facing: true | falseai_affects_individuals: true | false (makes or influences decisions about people)
If not provided, gather via scoping questions.
Finding ID prefix
AI — see CONVENTIONS.md §4.
Tier thresholds
| Tier | Evals | Prompt injection defense | Output filter | Human oversight | Drift monitoring | Model card |
|---|---|---|---|---|---|---|
| prototype | advisory | advisory | required for user-facing | optional | optional | optional |
| team | required (golden set + regression) | required | required | required for user-facing | recommended | required for public releases |
| scalable | required (golden set + adversarial + A/B) | required + output validation | required + content moderation | required, with loggable overrides | required + alerting | required + datasheet |
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 · 562 lines · 160 tokens per session scan A 0c4a70dddad9
ai-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 160 tokens to every session and 6,977 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 2026-08-31.
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