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 fatihguner/foreman --skill ai-learning-for-leadersgit clone --depth 1 https://github.com/fatihguner/foremanWrote 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/fatihguner/foreman/ai-learning-for-leaders)<a href="https://agentmods.dev/skills/fatihguner/foreman/ai-learning-for-leaders"><img src="https://agentmods.dev/badge/skills/fatihguner/foreman/ai-learning-for-leaders/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/fatihguner/foreman/ai-learning-for-leaders"><img src="https://agentmods.dev/badge/skills/fatihguner/foreman/ai-learning-for-leaders.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.03588 |
| Opus 5.5 | $0.00039 | $0.01435 |
| Sonnet 5.5 | $0.00020 | $0.00718 |
| Haiku 4.5 | $0.00010 | $0.00359 |
Grade A, and why
ai-learning-for-leaders 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 1mo 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read runtime and advisory rules before applying this skill. Other Foreman layers and the catalog are in ../../content/, relative to this SKILL.md.
AI Learning for Leaders
Between what leaders know about AI and what they actually do with it lies a gap that widens by the quarter. Research conducted in Singapore -- a country ranked fourth globally in digital competitiveness -- found that two-thirds of managers considered their organisations ineffective at using AI systems they had already purchased. The technology was not the problem. The leaders were. They had disengaged from AI adoption projects, paralysed by a sense of inadequacy, and handed the keys to technologists who understood algorithms but not the business. This is the confidence problem: the more leaders learn about AI, the more they become aware of what they do not know, and the less inclined they are to lead. The antidote is not expertise. It is savviness -- knowing just enough to ask the right questions, make sound decisions, and refuse to be sidelined by jargon.
The Framework
The Gap: Understanding vs. Deployment
The framework identifies a structural tension at the heart of AI-era leadership. AI technology evolves at a pace that outstrips any individual's ability to master it, yet organisations expect their leaders to be proactive in its use and governance. This creates what he terms the AI knowledge-usage gap: leaders fall further behind in understanding even as deployment accelerates around them.
The gap produces a predictable leadership failure mode:
- Enthusiasm -- Leaders hear about AI's transformative potential and invest heavily in infrastructure, talent, and tooling.
- Inadequacy -- Once engagement begins, leaders discover they cannot speak the language of data scientists or evaluate technical trade-offs. Doubt sets in.
- Deference -- Leaders quietly withdraw from AI decision-making, ceding authority to technologists who lack business context.
- Failure -- Without leadership connecting AI to organisational purpose, employees see no reason to adopt the tools. The investment stalls. Boards pull the plug.
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.
- 1mo ago Changed · +201 lines · +98 tokens per session 7a6e837e4d8f
- 1mo ago First seen · 1 lines · 0 tokens per session scan A ae511ff1da28
ai-learning-for-leaders is a skill published in the GitHub repository fatihguner/foreman (63 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 3,588 once invoked, about $0.0004 per session on Opus 5.5. 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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