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 hyperbolic-discountinggit 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/hyperbolic-discounting)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/hyperbolic-discounting"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/hyperbolic-discounting/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/hyperbolic-discounting"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/hyperbolic-discounting.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 | $0.00132 | $0.01879 |
| Opus 5 | $0.00066 | $0.00940 |
| Sonnet 5 | $0.00026 | $0.00376 |
| Haiku 4.5 | $0.00013 | $0.00188 |
Grade A, and why
hyperbolic-discounting 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 8d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hyperbolic Discounting
Overview
People discount the near future far more steeply than the distant future, producing dynamically inconsistent preferences: patient choices for next month reverse when next month arrives. Formalized by Laibson's 1997 β-δ model: any future outcome is shrunk by β ≈ 0.7 relative to the present, then discounted exponentially. The fix is structural: commitment devices that bind the future-impatient self — auto-enrollment, forfeits, friction removal, public accountability.
Composes with loss-aversion-prospect-theory, regret-minimization, compound-interest, and okr-goal-setting.
When to Use
- "I'll start tomorrow / next week / next month" has been said multiple times on the same goal
- Savings, investment, or health behaviors are below the person's own stated intent
- Procrastination is the dominant pattern on a recurring task
- Subscriptions, free trials, or "today only" offers are producing unexpected lock-in
- An org fails to execute long-horizon strategy due to short-term firefighting
- A team chases the immediate AI-demo/launch spike over durable moats, evals, and infra — over-discounting long-term reliability amid AI capex, AI valuations, or fast AI adoption pressure
Not when: apparent impatience reflects real new information; discounting is rational due to genuine uncertainty about future receipt; cost of commitment device exceeds benefit.
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: if you reliably want X for future-you but choose ~X for present-you, that gap is hyperbolic discounting — the fix is a commitment device, not willpower.
- Check fit: if the inconsistency reflects new information, this is a rational update, not present bias.
- Elicit their real case: what does future-you want? What does present-you actually do? How long has the gap persisted?
[WAIT — do not advance until user responds]
- Run The Process one step at a time — map asymmetry, estimate β, choose device.
[WAIT — do not advance until user responds]
- Close: name the specific commitment device chosen + first re-check date.
[WAIT — do not advance until user responds]
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.
- 8d ago First seen · 121 lines · 132 tokens per session scan A 13caab09670c
hyperbolic-discounting is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 132 tokens to every session and 1,879 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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