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 using-playbookgit 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/using-playbook)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/using-playbook"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/using-playbook/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/using-playbook"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/using-playbook.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 | $0.00046 | $0.02116 |
| Opus 5 | $0.00023 | $0.01058 |
| Sonnet 5 | $0.00009 | $0.00423 |
| Haiku 4.5 | $0.00005 | $0.00212 |
Grade A, and why
using-playbook 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 yesterday.
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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using the AI Adoption Playbook
Purpose
Routes leaders to the right skill. Works for founders, CTOs, VPs of Engineering, CAIOs, COOs, consultants — anyone responsible for AI adoption. Enforces the rule that fluency-assessment runs first. If they've already completed the assessment, routes based on their request and scorecard results.
Flow
digraph router {
"Leader arrives" [shape=box];
"Mentions prior scores?" [shape=diamond];
"Start fluency-assessment" [shape=box];
"Has existing data?" [shape=diamond];
"Bridge: map data to pillars" [shape=box];
"Match request to skill" [shape=box];
"Request unclear?" [shape=diamond];
"Route by lowest pillar" [shape=box];
"Invoke matched skill" [shape=doublecircle];
"Leader arrives" -> "Mentions prior scores?";
"Mentions prior scores?" -> "Match request to skill" [label="yes, has scorecard"];
"Mentions prior scores?" -> "Start fluency-assessment" [label="no"];
"Start fluency-assessment" -> "Has existing data?";
"Has existing data?" -> "Bridge: map data to pillars" [label="yes — surveys, dashboards, etc."];
"Has existing data?" -> "Invoke matched skill" [label="no — run assessment from scratch"];
"Bridge: map data to pillars" -> "Invoke matched skill";
"Match request to skill" -> "Request unclear?";
"Request unclear?" -> "Route by lowest pillar" [label="yes"];
"Request unclear?" -> "Invoke matched skill" [label="no"];
"Route by lowest pillar" -> "Invoke matched skill";
}
Process
Step 1: Welcome and Start the Assessment
Do NOT ask "have you done an assessment before?" Most people haven't, and the question creates confusion. Instead, acknowledge what they're dealing with and go straight into the first question.
"Let's figure out where your team actually stands with AI. I've got a quick quiz — 11 questions, under 5 minutes. You pick A/B/C/D for each one, and at the end you'll have a scorecard with actual numbers across three areas: your team's mindset, how tools fit your workflow, and who owns making this work.
After that, I'd recommend sending a 5-minute survey to your team in parallel — your perspective is valuable, but hearing directly from the people using (or avoiding) the tools gives us a sharper picture.
First, two quick context questions. Tell me about your team — how big, what they do, and what's your role?"
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.
- yesterday Changed · +5 lines ff8e733ac99b
- 9d ago First seen · 124 lines · 46 tokens per session scan A 4ad528714e36
using-playbook is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 3d ago), licensed MIT. It adds 46 tokens to every session and 2,116 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.
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