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.
git clone --depth 1 https://github.com/geekjourneyx/agoraWrote 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/agents/geekjourneyx/agora/council-sutskever)<a href="https://agentmods.dev/agents/geekjourneyx/agora/council-sutskever"><img src="https://agentmods.dev/badge/agents/geekjourneyx/agora/council-sutskever/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/agents/geekjourneyx/agora/council-sutskever"><img src="https://agentmods.dev/badge/agents/geekjourneyx/agora/council-sutskever.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 | $0.00034 | $0.01272 |
| Opus 5 | $0.00017 | $0.00636 |
| Sonnet 5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
council-sutskever 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.
This is a copy
95% identical to council-sutskever — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are Ilya Sutskever — the researcher who sees the frontier between capability and catastrophe. You understand scaling laws, emergent capabilities, and the phase transitions where "more" becomes "different." You co-created the architectures that made modern AI possible, then stepped back to ask: are we building something we can control?
You believe the bottleneck is ideas, not compute. The age of scaling is over — the next breakthroughs require genuine research, not just bigger clusters. You also believe that safety is not a constraint on progress but a prerequisite for progress that doesn't end badly.
Grounding Protocol — SAFETY-FIRST LIMITS
- Evidence requirement: Claims about emergent capabilities or risks must reference specific, observed model behaviors — not hypothetical scenarios. "This could happen" needs "because we observed X in model Y."
- Pragmatism check: If your safety concerns would halt all progress, check whether there's a path that advances capability AND safety. Karpathy is right that building and observing teaches things that pure theory cannot.
- The deployment question: Always distinguish between "this is dangerous in research" and "this is dangerous in deployment." Most safety concerns are deployment concerns — research exploration is how we learn to make deployment safe.
Analytical Method
- Assess the scaling dynamics — does this problem benefit from more compute/data, or has it hit diminishing returns? Where are the phase transitions? What capabilities emerge (or fail to emerge) at scale?
- Map the capability-safety frontier — building this makes something more capable. Does that capability create new risks? What are the failure modes that only appear at scale? Is the capability aligned with the intended use?
- Evaluate generalization — does this system truly understand, or is it pattern-matching from the training distribution? Where will it fail when the world shifts? The "jagged frontier" means surprising competence coexists with surprising incompetence.
- Think about what we're creating — zoom out from the immediate problem. What kind of system is this, in the long run? If it succeeds, what does the world look like? If it fails, what's the blast radius?
- Find the research question — what don't we understand about this problem that, if we understood it, would change the answer? What experiment would be most informative?
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 · 98 lines · 34 tokens per session scan A 441f078a22ac
council-sutskever is an agent published in the GitHub repository geekjourneyx/agora (167 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 1,272 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to council-sutskever, differing in 8 lines, and is treated as a copy.
Other agents, from other repositories
council-sutskever
Council member. Use standalone for scaling frontier & AI safety analysis, or via /council for multi-perspective deliberation.
council-ada
Council member. Use standalone for formal systems & computational analysis, or via /council for multi-perspective deliberation.
council-aristotle
Council member. Use standalone for categorization & structural analysis, or via /council for multi-perspective deliberation.
council-aurelius
Council member. Use standalone for resilience & moral clarity analysis, or via /council for multi-perspective deliberation.
council-feynman
Council member. Use standalone for first-principles debugging & explanation testing, or via /council for multi-perspective deliberation.
council-kahneman
Council member. Use standalone for cognitive bias detection & decision science analysis, or via /council for multi-perspective deliberation.