Borrowing it
Nothing to install: this file belongs to adimango/ai-adoption-playbook. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adimango/ai-adoption-playbook/main/CLAUDE.mdgit 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/instructions/adimango/ai-adoption-playbook/claude-md)<a href="https://agentmods.dev/instructions/adimango/ai-adoption-playbook/claude-md"><img src="https://agentmods.dev/badge/instructions/adimango/ai-adoption-playbook/claude-md/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/instructions/adimango/ai-adoption-playbook/claude-md"><img src="https://agentmods.dev/badge/instructions/adimango/ai-adoption-playbook/claude-md.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 | $0.02911 | $0.02911 |
| Opus 5 | $0.01456 | $0.01456 |
| Sonnet 5 | $0.00582 | $0.00582 |
| Haiku 4.5 | $0.00291 | $0.00291 |
Grade A, and why
ai-adoption-playbook CLAUDE.md 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Adoption Playbook
A skills framework for leaders responsible for AI adoption — founders, CTOs, CAIOs, VPs of Engineering, COOs, or anyone who needs to show the board that AI investment is producing results. Not a coding tool. Not a PM tool. A consulting methodology in agent-readable skills.
Who This Is For
Any leader who got handed the "make AI work here" mandate and has to report progress to leadership. This includes:
- Founders and CTOs — board is asking about AI strategy, need structured answers
- VPs/Directors of Engineering — got "AI adoption" added to their OKRs, need to move 50-200 engineers
- Chief AI Officers and fractional CAIOs — need a repeatable framework across teams or clients
- COOs at non-tech companies — no CTO exists, AI adoption landed on their desk
- Consultants and advisors — need a structured diagnostic and planning methodology for client engagements
Skills adapt to company size and role: lighter touch for small teams, deeper process for large orgs.
NOT for: companies with established AI/ML teams already driving adoption, engineers seeking coding tools, ML researchers.
The Problem
Founders buy AI tool licenses, tell teams to use them, nothing happens. Board asks "what's your AI strategy?" — no good answer. This playbook breaks that loop.
Three Pillars
Every AI adoption failure maps to one of these. Every skill diagnoses or addresses them.
- Psychological barriers — fear of replacement, identity threat ("I don't need a crutch"), perfectionism, social signaling
- Integration failures — tools don't fit workflows, wrong first use case, too much friction, tried once and gave up
- Ownership gaps — nobody owns it, no metrics, no accountability, no feedback loop, leadership doesn't model behavior
Board-Readiness Principle
The throughline. Every skill output must be expressible in terms a VC understands: specific numbers, clear timelines, named owners, ROI framing. No "we're exploring." No "our developers love it." Numbers or it didn't happen.
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 · +4 lines · +267 tokens per session 4ffb5fbca287
- 9d ago First seen · 179 lines · 2,644 tokens per session scan A 46a88c80c579
ai-adoption-playbook CLAUDE.md is an instructions file published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 3d ago), licensed MIT. It adds 2,911 tokens to every session, about $0.0146 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 instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.