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 adimango/ai-adoption-playbook --skill data-readiness-checkgit clone --depth 1 https://github.com/adimango/ai-adoption-playbookWrote 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/adimango/ai-adoption-playbook/data-readiness-check)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/data-readiness-check"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/data-readiness-check/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/adimango/ai-adoption-playbook/data-readiness-check"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/data-readiness-check.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 | $0.02006 |
| Opus 5 | $0.00022 | $0.01003 |
| Sonnet 5 | $0.00009 | $0.00401 |
| Haiku 4.5 | $0.00004 | $0.00201 |
Grade A, and why
data-readiness-check 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 4d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Readiness Check
Purpose
Structured audit of whether the data behind one specific AI use case is usable: it exists, you can get to it, it's clean enough, it reflects current reality, and you're allowed to use it. This is not a general "how's your data" health check — it's anchored to one named use case.
Core principle: Audit the use case's actual data needs, not data in general. A generic data-quality assessment doesn't map to a decision; this does.
Context Intake
For Department: and Currency:, use the first available source: the current fluency scorecard → adoption.local.md (the department this run covers; by default the one marked (primary) — see CLAUDE.md Local Configuration) → ask the leader (currency defaults to USD).
Process
Step 1: Confirm the Use Case
If arriving from first-use-case-picker, reference the Use Case Brief directly:
"You picked [use case] as your first use case. Before we build a 90-day plan around it, let's check whether the data it needs actually exists and is usable."
If running standalone, ask:
"Which specific AI use case do you want to check the data for? Be specific — not 'our customer data,' but the exact use case (e.g., 'AI-drafted renewal emails using our CRM history')."
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.
- 4d ago Changed d5c5d644291b
- 12d ago First seen · 162 lines · 44 tokens per session scan A d14ad4437f48
data-readiness-check is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 5d ago), licensed MIT. It adds 44 tokens to every session and 2,006 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.