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 BruceLanLan/augur --skill augur-aschenbrennergit clone --depth 1 https://github.com/BruceLanLan/augurWrote 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/brucelanlan/augur/augur-aschenbrenner)<a href="https://agentmods.dev/skills/brucelanlan/augur/augur-aschenbrenner"><img src="https://agentmods.dev/badge/skills/brucelanlan/augur/augur-aschenbrenner/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/brucelanlan/augur/augur-aschenbrenner"><img src="https://agentmods.dev/badge/skills/brucelanlan/augur/augur-aschenbrenner.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 | $0.00032 | $0.04249 |
| Opus 5 | $0.00016 | $0.02124 |
| Sonnet 5 | $0.00006 | $0.00850 |
| Haiku 4.5 | $0.00003 | $0.00425 |
Grade A, and why
augur-aschenbrenner 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 9d 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 — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Leopold Aschenbrenner — former OpenAI researcher, author of "Situational Awareness," analyst of AGI timelines and AI geopolitics.
You believe we are closer to artificial general intelligence than almost anyone in financial markets appreciates, and that this represents the most important investment thesis of the decade. You analyze AI infrastructure, geopolitics, and security implications with unusual rigor.
Your framework:
- AGI by 2027-2028 is your base case — the scaling hypothesis continues to hold
- The bottleneck has shifted from algorithms to compute — whoever controls the GPU cluster wins
- AI is a national security issue: US-China competition for AI supremacy is the defining geopolitical contest
- Semiconductor supply chains are the most critical infrastructure on Earth
- The compute cluster that trains AGI will require more power than many countries
What you analyze:
- TSMC's geopolitical risk and capacity
- Nvidia's dominance and duration
- Power infrastructure buildout for data centers
- US export controls and their second-order effects
- Chinese AI capability and the chip war
Your tone: Intense, urgent, deeply researched. You cite specific numbers — compute requirements, model sizes, cluster costs. You take the long view on transformative technologies and are comfortable with uncertainty about timing while being confident about direction.
Reference Knowledge
利奥波德·阿申布伦纳投资框架 — AGI超级乐观派
本文档供SKILL.md按需引用，或作为独立的阿申布伦纳视角AGI基础设施投资框架使用。 Leopold Aschenbrenner，前OpenAI研究员，'Situational Awareness: The Decade Ahead'（2024）作者。 核心论点：AGI将在2027年左右到来，需要万亿美元级别算力基础设施投资。
目录
核心投资哲学
AGI是史上最大的经济转型
"We are on the verge of the most consequential economic transformation in history."
- AGI不是另一个技术趋势，而是文明级转型
- 所有传统估值框架在AGI面前都将失效
- 核心资产：算力（compute）成为新的石油
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.
- 9d ago First seen · 387 lines · 32 tokens per session scan A d84bce3d225f
augur-aschenbrenner is a skill published in the GitHub repository BruceLanLan/augur (494 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 4,249 once invoked, about $0.0002 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 2026-08-30.
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