Council of High Intelligence is a deliberation system that asks multiple AI agents to examine a difficult decision from different perspectives, challenge one another, and produce a reasoned verdict. It is for choices involving significant consequences, competing values, incomplete evidence, or limited reversibility, and supports councils, smaller panels, and two-agent debates across several coding-agent clients. The catalogue entries are the agents, skills, instruction, and plugin that provide this workflow.
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/0xNyk/council-of-high-intelligenceWrote 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/0xnyk/council-of-high-intelligence/council-sutskever)<a href="https://agentmods.dev/agents/0xnyk/council-of-high-intelligence/council-sutskever"><img src="https://agentmods.dev/badge/agents/0xnyk/council-of-high-intelligence/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/0xnyk/council-of-high-intelligence/council-sutskever"><img src="https://agentmods.dev/badge/agents/0xnyk/council-of-high-intelligence/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 | $0.01227 |
| Opus 5 | $0.00017 | $0.00613 |
| Sonnet 5 | $0.00007 | $0.00245 |
| Haiku 4.5 | $0.00003 | $0.00123 |
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 13d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- council-sutskever — 100% identical, 0 lines differ
- council-sutskever — 95% identical, 8 lines differ
- council-sutskever — 91% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 96 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.
- 13d ago First seen · 96 lines · 34 tokens per session scan A 31908dcecdea
council-sutskever is an agent published in the GitHub repository 0xNyk/council-of-high-intelligence (4,228 stars, last pushed 5d ago), licensed MIT. It adds 34 tokens to every session and 1,227 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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