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 regret-minimizationgit 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/regret-minimization)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/regret-minimization"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/regret-minimization/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/regret-minimization"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/regret-minimization.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 | $0.00137 | $0.02294 |
| Opus 5 | $0.00068 | $0.01147 |
| Sonnet 5 | $0.00027 | $0.00459 |
| Haiku 4.5 | $0.00014 | $0.00229 |
Grade A, and why
regret-minimization 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Regret Minimization
Overview
Most frameworks optimize expected value — pick the highest probability-weighted payoff. That math breaks down for large, asymmetric, life-defining choices where the true unit is not money: career pivots, founding decisions, relocations, relationship exits. There, the decisive question is not "what's the expected payoff?" but "which regret will I be unable to live with at 80?"
Associated with Jeff Bezos (D.E. Shaw → Amazon, 1994); rooted in Stoic Premeditatio Malorum (Seneca, Epistulae Morales 91) and formalized in Regret Theory (Loomes & Sugden, The Economic Journal, 1982).
Compose with neighbors: use first-principles to clarify what is at stake; inversion to surface failure modes; second-order-thinking to verify downstream consequences; then use regret minimization to choose when EV analyses come out close and the true cost is psychological.
When to Use
Apply when: decision is major, hard-to-reverse, asymmetric (career pivot, founding, relocation, relationship, children); EV math feels insufficient because units are joy/meaning/identity; hesitation is emotional, not analytical; user says "what would my 80-year-old self think?", "if I never try this will I regret it?", "I keep hesitating but the math is clear," "should I quit big tech to go all-in on AI / join the AI wave / start a company now (bubble or export-control fears notwithstanding)?"
When NOT to use: routine reversible decisions; EV is genuinely the right unit (portfolio allocation, pricing); framework being re-run weekly (procrastination); both regrets are unlivable (redesign the choice instead).
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete major decision → 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 what-it-is: for big life decisions where math is close or doesn't fit, project to age 80 and ask which path will haunt you more. Commit to the option whose regret you can live with.
- Check fit against When to Use / When NOT to use — if it doesn't fit, say so and point elsewhere.
- Elicit their real decision. Ask for one if missing; never run the audit on a hypothetical.
[WAIT — do not advance until user responds]
- Walk The Process one step per turn: describe 80-year-old self → name each path's regret → test asymmetry. Never project for them.
[WAIT — do not advance until user responds]
- Close by naming the regret they identified as harder to live with, plus the commit date.
[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 · 123 lines · 137 tokens per session scan A c46a7be1c024
regret-minimization is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 137 tokens to every session and 2,294 once invoked, about $0.0007 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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