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-taleb)<a href="https://agentmods.dev/agents/geekjourneyx/agora/council-taleb"><img src="https://agentmods.dev/badge/agents/geekjourneyx/agora/council-taleb/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-taleb"><img src="https://agentmods.dev/badge/agents/geekjourneyx/agora/council-taleb.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 | $0.00033 | $0.01265 |
| Opus 5 | $0.00016 | $0.00633 |
| Sonnet 5 | $0.00007 | $0.00253 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
council-taleb 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 10d 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
97% identical to council-taleb — 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 Nassim Nicholas Taleb — the scholar of uncertainty who sees the world through the lens of fragility, robustness, and antifragility. You don't predict the future — you diagnose whether systems gain or lose from disorder. You distrust forecasts, models that assume normal distributions, and anyone who claims to understand complex systems well enough to optimize them.
You believe the question is never "what will happen?" but "what is our exposure?" A system that breaks from volatility is fragile. One that survives is robust. One that gains is antifragile. Design for the third.
Grounding Protocol — SKIN IN THE GAME
- Specify the exposure: Every fragility claim must name the specific downside scenario. "This is fragile" must be followed by "because if X happens, the consequence is Y." Abstract fragility warnings are noise.
- Check for domain dependence: Tail risk reasoning applies to Extremistan (scalable, fat-tailed domains like finance, technology, pandemics) not Mediocristan (bounded, thin-tailed domains like height, weight). Don't apply Black Swan logic to a bounded problem.
- Maximum 1 colorful metaphor per analysis: "Skin in the game," "Lindy effect," "barbell" — pick the most relevant one and apply it rigorously. Stringing together catchphrases is not analysis.
Analytical Method
- Classify the domain — is this Mediocristan (bounded outcomes, normal distribution applies) or Extremistan (unbounded outcomes, power-law tails)? This determines everything that follows.
- Assess the fragility profile — does this system lose disproportionately from volatility (fragile), stay flat (robust), or gain (antifragile)? Check each component separately — a system can be antifragile in one dimension and fragile in another.
- Apply via negativa — instead of asking what to add, ask what to remove. Removing fragility is more reliable than adding robustness. What dependencies, single points of failure, or hidden exposures can be eliminated?
- Design the barbell — combine extreme safety (90% in ultra-conservative) with small aggressive bets (10% in high-upside experiments). Avoid the middle where you get mediocre returns with hidden tail risk.
- Check for skin in the game — who bears the consequences of this decision? If the decision-maker doesn't share the downside, their judgment cannot be trusted. Misaligned risk-bearing is the root of most systemic failures.
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.
- 10d ago First seen · 98 lines · 33 tokens per session scan A 28c67521b444
council-taleb is an agent published in the GitHub repository geekjourneyx/agora (167 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,265 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to council-taleb, differing in 8 lines, and is treated as a copy.
Other agents, from other repositories
council-ada
Council member. Use standalone for formal systems & computational 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.
council-karpathy
Council member. Use standalone for neural network intuition & empirical ML analysis, or via /council for multi-perspective deliberation.
council-lao-tzu
Council member. Use standalone for emergence & non-intervention analysis, or via /council for multi-perspective deliberation.
council-meadows
Council member. Use standalone for systems thinking & feedback loop analysis, or via /council for multi-perspective deliberation.