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 antifragilegit 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/antifragile)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/antifragile"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/antifragile/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/antifragile"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/antifragile.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 | $0.00123 | $0.01897 |
| Opus 5 | $0.00062 | $0.00949 |
| Sonnet 5 | $0.00025 | $0.00379 |
| Haiku 4.5 | $0.00012 | $0.00190 |
Grade A, and why
antifragile 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Antifragile
Overview
Nassim Nicholas Taleb (2012) identified a third response to stress beyond fragile/robust: antifragile — systems that gain from disorder, with bounded downside and unbounded upside.
- Fragile: concave — absorbs small stress, breaks catastrophically at the tail. (Over-leveraged banks, just-in-time supply chains.)
- Robust: linear — unchanged by stress. (Physical infrastructure, traditional skills.)
- Antifragile: convex — improves under stress. (Evolution, the immune system, the restaurant industry as a whole.)
Core warning: most modern complex systems are hidden-fragile — stable only because the tail event hasn't arrived yet. Composes with inversion, black-swan, expected-value-and-kelly, feedback-loops.
When to Use
- A system looks stable but may be hidden-fragile
- Designing a portfolio (financial, career, organizational) under uncertainty
- A "this can't happen" assumption is embedded in a strategic plan
- Recurrent small problems are suppressed rather than learned from
- A business depends on one AI/model vendor's API, pricing, or policy, or faces AI-native competition amid rapid AI capex and adoption shifts
- User says: "Taleb," "barbell strategy," "convex," "skin in the game," "via negativa"
Not when: decision is small and reversible; system is simple and low-stakes; you confuse high-variance with antifragile.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete system → run The Process directly.
- Coach mode: user is unfamiliar → 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 things break under stress, some survive, some get stronger — most "stable" things are in the first category, just before stress arrives.
- Check fit: small reversible decisions → not this lens.
- Elicit their real system — what specifically are they stress-testing?
[WAIT — do not advance until user responds]
- Walk through The Process one step at a time with their input.
[WAIT — do not advance until user responds]
- Close by naming the specific design move (barbell / via negativa / optionality / skin-in-the-game) that fits their case.
[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.
- 9d ago First seen · 124 lines · 123 tokens per session scan A 1df5c56c22ab
antifragile is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 7d ago), licensed MIT. It adds 123 tokens to every session and 1,897 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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