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-literacy-curriculum)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/people-literacy-curriculum"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/people-literacy-curriculum/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-literacy-curriculum"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/people-literacy-curriculum.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 | $0.00173 | $0.02935 |
| Opus 5 | $0.00086 | $0.01468 |
| Sonnet 5 | $0.00035 | $0.00587 |
| Haiku 4.5 | $0.00017 | $0.00294 |
Grade A, and why
people-literacy-curriculum 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People — Workforce AI Literacy Curriculum
Design a role-anchored AI literacy program that satisfies EU AI Act Art. 4 (mandatory literacy duty, in force since 2 Feb 2025) AND fixes the "chatbot-only" usage pattern that strands organizations on the wrong side of the MIT 95% GenAI divide.
Anchor: BCG 88/25 manager role-modeling — 88% of managers say role-modeling AI matters, only 25% do it visibly; weekly-AI-use rates jumped 55%→72% YoY in cohorts where managers used the tools themselves. Training without manager role-modeling is theatre.
Verdict vocabulary (stable output contract): Compliant-and-effective / Compliant-not-effective / Non-compliant. This designs the enterprise-wide program; for choosing which single tool to put in front of a specific group, use people-tool-selection.
Step 1: Mental-Model Taxonomy
Core question: Do learners know which of the four AI patterns fits their problem — or are they defaulting to chatbot for everything?
The four canonical patterns. Every learner must distinguish them by capability, failure mode, and one example in their own job:
- Chatbot (single-turn / multi-turn LLM). Capability: open-ended generation, summarization, brainstorming. Limit: no memory across sessions, no access to your data, hallucinates facts. Canonical example: "Draft a customer email from these bullets."
- RAG (retrieval-augmented). Capability: grounded answers over your documents/policies/tickets. Limit: only as good as the corpus; cannot act. Canonical example: "Answer HR policy questions over the employee handbook."
- Workflow AI (deterministic chain / function-calling). Capability: scripted multi-step automation with AI in specific nodes; predictable. Limit: brittle to off-path inputs. Canonical example: "Extract invoice fields → validate → post to ERP."
- Agent (autonomous, tool-using, planning). Capability: decomposes goals, calls tools, iterates. Limit: blast radius; needs guardrails (see
imda-4-dimensions-agentic.md). Canonical example: "Resolve this Tier-1 support ticket end-to-end."
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 · 171 lines · 173 tokens per session scan A 8ee0b6105816
people-literacy-curriculum is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 173 tokens to every session and 2,935 once invoked, about $0.0009 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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