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 deciqAI/knowledge-skills --skill black-swangit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/black-swan)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/black-swan"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/black-swan/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/deciqai/knowledge-skills/black-swan"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/black-swan.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 | $0.00125 | $0.02189 |
| Opus 5 | $0.00063 | $0.01094 |
| Sonnet 5 | $0.00025 | $0.00438 |
| Haiku 4.5 | $0.00013 | $0.00219 |
Grade A, and why
black-swan 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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Black Swan
Overview
Taleb (2007): a black swan is (1) outside all prior expectations, (2) extreme impact, (3) obvious in hindsight only. Many domains (markets, careers, tech) are Extremistan (power-law / fat-tail), yet most models assume Mediocristan (Gaussian / thin-tail) — underestimating tail risk by orders of magnitude. The Turkey Problem: 1000 days of feeding creates confidence; day 1001 is Thanksgiving.
Composes with antifragile, probabilistic-thinking, inversion, first-principles.
When to Use
Use when: a risk model assumes normality in a fat-tailed domain; "never happened in N years" dismisses tail risk; strategy assumes stable environment; you're constructing a retrospective narrative; stress-testing against extreme scenarios; someone says "fat tails / Taleb / narrative fallacy / turkey problem"; a thesis rests on a one-directional trend like "AI demand can only go up," AI-capex payoff, or concentrated mega-cap / AI-bubble exposure.
Not when: domain is genuinely Mediocristan; "black swan" is being used to excuse a foreseeable planning failure.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete case → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: some domains have rare events that dominate everything (markets, careers, tech); models assuming "normal" distributions miss them — and we always invent stories afterward that make them look predictable.
- Check fit: genuinely thin-tailed domain (heights, commutes)? Point elsewhere.
- Elicit the real situation — what decision or system are we auditing?
[WAIT — do not advance until user responds]
- One question at a time: is this Extremistan or Mediocristan? What's the tail-survival design? What narrative am I constructing post-hoc?
[WAIT — do not advance until user responds]
- Close: name the specific tail-event preparation — not prediction — they've uncovered.
[WAIT — do not advance until user responds]
What ships with it
4 files 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.
- 10d ago First seen · 133 lines · 125 tokens per session scan A 5561ce362425
black-swan is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 8d ago), licensed MIT. It adds 125 tokens to every session and 2,189 once invoked, about $0.0006 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.
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