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 vasilyu1983/AI-Agents-public --skill foundations-decision-theorygit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-decision-theory)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-decision-theory"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-decision-theory/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/vasilyu1983/ai-agents-public/foundations-decision-theory"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-decision-theory.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 | $0.00041 | $0.07099 |
| Opus 5 | $0.00020 | $0.03549 |
| Sonnet 5 | $0.00008 | $0.01420 |
| Haiku 4.5 | $0.00004 | $0.00710 |
Grade A, and why
foundations-decision-theory 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Theory Foundations
11 canonical decision-theory primitives for decisions under uncertainty. Each primitive is a formal tool with defined inputs, outputs, and failure modes. Primitives are domain-agnostic: the same expected-utility calculation that gates a product launch gates a capital investment; the same EVPI formula that sizes a market research study sizes a pre-launch pilot.
When to Apply
Apply decision-theory when:
- Single irreversible call under uncertainty (launch / kill / restructure)
- Value-of-information question — "is the next experiment worth running?"
- Real-options framing — staged investment with kill criteria
- Multi-criteria choice with explicit weights (MCDA, AHP)
- Multi-armed bandit allocation between treatments under regret minimisation
Skip and use simpler alternatives when:
- Decision is reversible and low-cost — just try it; analysis paralysis costs more than the wrong choice
- Multiple agents with strategic interaction — use foundations-game-theory
- Causal "did X cause Y" question — use foundations-causal-inference
- A clear oracle exists (test suite, KPI threshold) — use the oracle
- All candidate options are dominated by one option on every criterion — no decision-theory needed
- EVPI is much smaller than the cost of acquiring info — skip the study and decide now
Contents
What ships with it
19 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.
- agents/openai.yaml 362 B
- assets/templates/decision-theory/01-expected-utility.md 2.7 KB
- assets/templates/decision-theory/02-bayesian-decision.md 3.0 KB
- assets/templates/decision-theory/03-minimax-regret.md 4.4 KB
- assets/templates/decision-theory/04-value-of-information.md 8.2 KB
- assets/templates/decision-theory/05-multi-criteria.md 5.5 KB
- assets/templates/decision-theory/06-risk-aversion.md 5.6 KB
- assets/templates/decision-theory/07-real-options.md 5.0 KB
- assets/templates/decision-theory/08-prospect-theory.md 7.1 KB
- assets/templates/decision-theory/09-ellsberg-allais.md 4.6 KB
- assets/templates/decision-theory/10-multi-armed-bandit.md 6.4 KB
- assets/templates/decision-theory/11-stochastic-dominance.md 4.0 KB
- assets/templates/decision-theory/README.md 3.9 KB
- data/sources.json 24 KB
- learnings.consolidated.md 1.0 KB
- learnings.md 1.6 KB
- references/formal-theory-map.md 2.3 KB
- references/patterns-scenarios-traps.md 3.3 KB
- references/primitives-overview.md 9.0 KB
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 · 334 lines · 41 tokens per session scan A 7af055e0fd11
foundations-decision-theory is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 41 tokens to every session and 7,099 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 2026-09-03.
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