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 adoption-scorecardgit 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/adoption-scorecard)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/adoption-scorecard"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/adoption-scorecard/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/adoption-scorecard"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/adoption-scorecard.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 | $0.00037 | $0.01941 |
| Opus 5 | $0.00018 | $0.00971 |
| Sonnet 5 | $0.00007 | $0.00388 |
| Haiku 4.5 | $0.00004 | $0.00194 |
Grade A, and why
adoption-scorecard 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 3d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adoption Scorecard
Purpose
Produces a one-page adoption snapshot with hard numbers — who's using what, how often, and what's changed. This is a measurement tool, not a diagnostic. It reports the current state without analyzing why or recommending what to do next.
Core principle: Adoption is behavior change, not tool access. This scorecard measures what people actually do, not what licenses they have.
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
Required Inputs
If not available from prior skills, ask for:
- Team size: Total team size by role
- Tools in use: Which AI tools, how many seats each
- Usage data: Who uses each tool, how often (daily/weekly/rarely/never)
- Use cases: What tasks are people using AI for
- Comparison point: Any prior scorecard or baseline to compare against
Three Levels of Measurement
| Level | What it means | How to count |
|---|---|---|
| Access | Has a license or account | Count of provisioned seats |
| Usage | Opens the tool at least weekly | Count from admin dashboard or founder estimate |
| Adoption | Work has visibly changed — tasks start differently, output is different | Count of people whose workflow has shifted |
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.
- 3d ago Changed c74d75c1fbbc
- 11d ago First seen · 188 lines · 37 tokens per session scan A 126c34b0d2a7
adoption-scorecard is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 4d ago), licensed MIT. It adds 37 tokens to every session and 1,941 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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