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.
git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkitWrote 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/plugins/techwolf-ai/ai-first-toolkit/ai-adoption)<a href="https://agentmods.dev/plugins/techwolf-ai/ai-first-toolkit/ai-adoption"><img src="https://agentmods.dev/badge/plugins/techwolf-ai/ai-first-toolkit/ai-adoption/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/plugins/techwolf-ai/ai-first-toolkit/ai-adoption"><img src="https://agentmods.dev/badge/plugins/techwolf-ai/ai-first-toolkit/ai-adoption.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
ai-adoption 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 29d 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.
What it actually says
{
"name": "ai-adoption",
"version": "2.1.0",
"description": "AI Adoption, two skills for working with your Claude history. token-doctor diagnoses where your Claude Code + Cowork spend goes (length distribution, marathon share, sub-agent fan-out, model mix, cache rebuilds, per-project health), writes a doctor-style terminal report, and optionally fans out parallel Haiku subagents over your top sessions for habit-level recommendations. task-profile mines those same sessions into a structured task profile: what you do with AI, how often, how successfully, where friction lives; it emits a shareable CSV, an interactive HTML explorer with progressive disclosure and a code-vs-cowork token chart, AI-first coaching cards, and up to five task-centric skill proposals. (session-search moved to the session-tools plugin.)",
"author": {
"name": "Jeroen Van Hautte",
"email": "[email protected]"
},
"keywords": ["ai-adoption", "token-doctor", "task-profile", "sessions", "subagents", "analytics", "coaching", "cost", "skills"]
}
What it installs
The manifest is a name and a version. 2 skills travel with it, and installing the plugin installs all of them — 246 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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.
- 29d ago Changed 50ffd9916e8e
- 1mo ago First seen · 11 lines scan A b4a201183406
ai-adoption is a plugin published in the GitHub repository techwolf-ai/ai-first-toolkit (134 stars, last pushed 16d ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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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