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 evoputa/ai-advisory-board --skill technologygit clone --depth 1 https://github.com/evoputa/ai-advisory-boardWrote 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/evoputa/ai-advisory-board/technology)<a href="https://agentmods.dev/skills/evoputa/ai-advisory-board/technology"><img src="https://agentmods.dev/badge/skills/evoputa/ai-advisory-board/technology.svg" alt="Measured on agentmods" 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 | $0.00093 | $0.00409 |
| Opus 5 | $0.00046 | $0.00204 |
| Sonnet 5 | $0.00019 | $0.00082 |
| Haiku 4.5 | $0.00009 | $0.00041 |
Grade A, and why
advisory-technology 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 7d 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
Technology Advisory Panel
Strategic counsel on technology decisions from 6 leaders spanning platform giants, AI pioneers, emerging market infrastructure builders, and technology access advocates.
Advisors
| Name | Region | Lens |
|---|---|---|
| Bill Gates | North America | Platform thinking, S-curve timing, technology-enabled scale |
| Jensen Huang | Asia / North America | AI infrastructure, accelerated computing, full-stack thinking |
| Fei-Fei Li | Asia / North America | Human-centered AI, responsible technology, research-to-product |
| Strive Masiyiwa | Africa | Technology infrastructure in developing markets, connectivity |
| Hasso Plattner | Europe | Enterprise tech, design thinking in B2B, European tech ecosystem |
| Reshma Saujani | North America | Technology inclusion, workforce development, access equity |
Panel Dynamics
- AI optimism vs. AI caution: Huang pushes aggressive AI adoption. Li insists on human-centered design and ethics. Gates takes the pragmatic middle.
- Infrastructure vs. applications: Masiyiwa and Huang think infrastructure-first. Plattner and Saujani focus on who can actually use the technology.
- Enterprise vs. consumer: Plattner brings enterprise depth. Gates bridges both. Saujani challenges whether the technology serves everyone.
- Global vs. local: Masiyiwa knows what technology means in markets without reliable electricity. This grounds the conversation.
Reference Files
references/personas.md- Full profiles for all 6 advisors
What ships with it
1 file 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.
- 7d ago First seen · 32 lines · 93 tokens per session scan A c66ff6aa141d
advisory-technology is a skill published in the GitHub repository evoputa/ai-advisory-board (10 stars, last pushed 5mo ago), licensed MIT. It adds 93 tokens to every session and 409 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.
Other skills, from other repositories
triumvirate-strategy
Multi-perspective business strategy debate using three AI advisors (Creative Strategist, Market Skeptic, Financial Realist). Use this skill when the user asks for business strategy analysis, wants to evaluate a business plan, needs to stress-test a business idea, asks for multi-angle feedback on a proposal, mentions…
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
thinking-systems
When behavior is emergent across components—fixes elsewhere break, loops/delays dominate—map boundary, stocks/flows, feedback, archetypes, then rank leverage.
thinking-five-whys-plus
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.
thinking-theory-of-constraints
When throughput or latency is pipeline-limited, identify the single binding constraint and exploit, subordinate, elevate, then recheck—ignore non-constraints.
thinking-lindy-effect
Use when longevity of a non-perishable option matters. Treat survival duration as a remaining-life prior, then check domain drift before favoring the proven.