Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add geledek/enterprise-ai-transformation-skills/plugin install enterprise-ai-transformation-skillsWrote 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/geledek/enterprise-ai-transformation-skills/people-tool-selection)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/people-tool-selection"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/people-tool-selection/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/geledek/enterprise-ai-transformation-skills/people-tool-selection"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/people-tool-selection.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 | $0.00196 | $0.02159 |
| Opus 5 | $0.00098 | $0.01079 |
| Sonnet 5 | $0.00039 | $0.00432 |
| Haiku 4.5 | $0.00020 | $0.00216 |
Grade A, and why
people-tool-selection 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 12d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People — AI Tool Selection
Choose which AI tool to introduce to a specific user group, or whether to introduce a new tool at all. The unit of analysis is the group's adoption, not the technology. Six sequential roles. Maintain all prior reasoning as state — each role builds on the previous.
Two structural rules that govern the whole diagnosis:
- Constraints eliminate before capability ranks. A tool that fails a hard constraint (cost, ecosystem, setup, language, data, IT policy) is out regardless of how capable it is. Do not build a weighted matrix that lets capability buy back a failed constraint.
- Measure delta from the group's baseline, not absolute capability. A tool that improves artifacts the group can already make competes with "just teach better prompting." A tool that unlocks a new artifact class at near-zero effort wins the session.
Mode vocabulary: Deepen / Extend / Introduce. Verdict vocabulary (stable output contract): Adopt-now / Adopt-with-scaffolding / Pilot-with-subgroup / Skip. For build/buy/partner sourcing use tech-buy-vs-build; for enterprise-wide curriculum design use people-literacy-curriculum.
After each role, output a clearly labeled section, then proceed to the next role. Do not stop until all six are complete.
Role 1: Audience Profiler (Who is this group, really?)
BASELINE. Which AI tools does the group already use, and at what fluency? Name them. If the group already uses general chatbots competently, re-teaching basics is a wasted session and "a better chatbot" is a weak increment.
ARTIFACT INVENTORY. What does this group actually produce in their job, weekly? (Lesson plans, slide decks, videos, quizzes, reports, client emails, care notes.) The tool must map to artifacts on this list — not to abstract "productivity."
LANGUAGE AND DEVICE REALITY. Working language(s). Managed or personal devices. Connectivity. Existing organizational subscriptions (Google Workspace, Microsoft 365, LMS).
TIME BUDGET. How long is the training window, and how much learning time exists after it? A one-session engagement and a semester program justify different tools.
What ships with it
2 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.
- 12d ago First seen · 129 lines · 196 tokens per session scan A 72037a1714a5
people-tool-selection is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 196 tokens to every session and 2,159 once invoked, about $0.0010 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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