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 sananthanarayan/skilldrop --skill ai-adoption-rolloutgit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/ai-adoption-rollout)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/ai-adoption-rollout"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-adoption-rollout/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/sananthanarayan/skilldrop/ai-adoption-rollout"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-adoption-rollout.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 | $0.00109 | $0.01380 |
| Opus 5 | $0.00055 | $0.00690 |
| Sonnet 5 | $0.00022 | $0.00276 |
| Haiku 4.5 | $0.00011 | $0.00138 |
Grade A, and why
ai-adoption-rollout 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 11d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-adoption-rollout
The tool is chosen; the risk now is people. This plans the human rollout: who gets it when, what they're taught, who they ask, what they're told, and the observable that must hold before the next cohort opens.
Rollouts fail quietly. Licences get assigned, a launch email goes out, usage spikes for a week and decays, and nobody can say which step failed. Every wave here carries a gate so that decay is visible while it's still fixable.
How to respond
-
Restate what's being rolled out, to whom, and the adoption outcome. Not "increase AI usage" — a behaviour: "support agents draft first responses with assistance on the top 3 categories". Usage is a proxy; the behaviour is the goal. Cap clarifying questions at 2.
-
Design cohorts, smallest first. Default three waves — pilot (5–15 volunteers, highest tolerance) → early (one whole team or function, includes sceptics) → general. Each cohort names who is in it and why they're next: the pilot proves the workflow, the early wave proves it survives people who didn't ask for it. Skipping straight to general is the most common failure and this skill refuses it.
-
Specify enablement per cohort, not one training deck. What each cohort must be able to do before they're counted as enabled — a 30-minute hands-on with their own real task beats an hour of slides. Name the format, the time cost, and who delivers it. Later cohorts get shorter enablement built from what the pilot actually got stuck on.
-
Name champions by role, with a time budget. One per cohort, with hours protected. An unbudgeted champion is a volunteer who quietly stops answering. State what they own: first-line questions, collecting friction, running the office hour.
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Write the comms plan — including what will not change. Three messages: the announcement (what, why, what's in it for the reader), the reassurance (explicitly: whether this affects headcount, evaluation, or how work is judged), and the follow-up. Silence on the reassurance point is read as bad news and is the single biggest driver of quiet non-adoption.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 62 lines · 109 tokens per session scan A 503f1740f708
ai-adoption-rollout is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 27d ago), licensed MIT. It adds 109 tokens to every session and 1,380 once invoked, about $0.0005 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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