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 minsky-momentgit 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/minsky-moment)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/minsky-moment"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/minsky-moment/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/minsky-moment"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/minsky-moment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 110 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00124 | $0.02073 |
| Opus 5 | $0.00062 | $0.01037 |
| Sonnet 5 | $0.00025 | $0.00415 |
| Haiku 4.5 | $0.00012 | $0.00207 |
Grade A, and why
minsky-moment 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Minsky Moment
Overview
A Minsky Moment is the point at which a leveraged financial system tips from apparent stability into rapid self-reinforcing collapse — caused by the internal dynamic of debt accumulation that sustained stability itself produced. Minsky identified three debt stages systems cycle through: Hedge (income covers principal + interest), Speculative (income covers interest only; must roll over principal), Ponzi (income covers neither; depends on asset appreciation). Stability breeds instability: prolonged calm leads rational actors to accumulate more risk until debt cannot be serviced from income alone.
Compose with neighbors: Use black-swan to distinguish Minsky dynamics from genuine tail events — a Minsky Moment is predictable, not random. Use antifragile to identify who gains from Minsky collapses. Use margin-of-safety to operationalize the hedge financing requirement.
When to Use
Apply when:
- Analyzing credit cycles, asset price levels, or financial system fragility after prolonged stability
- Evaluating debt structure of a company, sector, or economy
- Building a risk scenario for an investment, loan, or counterparty exposure
- Someone says: "this time is different," "leverage has been stable," "what's the tail risk," "is this a bubble?"
- Assessing an AI-capex / AI-infrastructure boom, circular ("round-trip") vendor financing, GPU-collateralized debt, or whether prolonged AI-boom stability is hiding an "AI bubble"
When NOT to use:
- Entity has no meaningful leverage — Minsky dynamics require debt
- Stability period is less than 18–24 months — risk-accumulation requires sustained calm
- Collapse risk is primarily operational, not financial
- You need to predict when collapse will occur — Minsky identifies structural fragility, not timing
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific leveraged entity, market, or credit cycle → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
What ships with it
3 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 · 125 lines · 124 tokens per session scan A 6a885b75c9b9
minsky-moment is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 124 tokens to every session and 2,073 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 2026-09-03.
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