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 curiosity-learning-curvegit 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/curiosity-learning-curve)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/curiosity-learning-curve"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/curiosity-learning-curve/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/curiosity-learning-curve"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/curiosity-learning-curve.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 | $0.00125 | $0.02126 |
| Opus 5 | $0.00063 | $0.01063 |
| Sonnet 5 | $0.00025 | $0.00425 |
| Haiku 4.5 | $0.00013 | $0.00213 |
Grade A, and why
curiosity-learning-curve 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 10d 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.
Curiosity Learning Curve
Overview
Biological learning capacity remains measurable into the 70s and 80s. The bottleneck is not biology — it is curiosity. When curiosity drops to near zero in a domain, the cascade is predictable: curiosity lost → learning capacity lost. The framework maps three curves against age: biological age (linear), curiosity (peaks in childhood, declines with neglect), and learning capacity (follows curiosity, not biology). Curiosity is the leading indicator and the intervention point.
Loewenstein's information-gap theory: curiosity is not a trait — it is a perception of a gap. Closing perceived gaps through mastery without opening new ones is the structural cause of curiosity death in expert practitioners.
Pairs with [metacognition] (observe your own curiosity state early). Use BEFORE [deep-work] (deep work in a curiosity-dead domain produces treadmill output, not growth). Complements [lifestage-value-curve] (curiosity maintenance is the mechanism for Silver/Bronze Age productivity).
When to Use
- A domain expert's knowledge has visibly stopped updating despite continued work
- A person reports going through professional motions without genuine engagement
- Output novelty (new insights, questions, solutions) has declined while quality is maintained
- A person has not been surprised by anything in their domain for 6+ months
- A senior practitioner is struggling with relevance as the domain changes around static expertise
When NOT to use:
- Burnout rather than curiosity deficit — burnout requires recovery before reactivation is possible
- Early-stage learning with no competence base yet (gap-perception requires some foundation)
- Structural causes (bad environment, financial stress) are driving motivation problems
- Domain exit is clearly right and curiosity reactivation is delaying a necessary decision
Coaching Novices (Adaptive Front Door)
Engine mode: user has a concrete case → run The Process directly. Coach mode: user is unfamiliar or reports stagnation without connecting it to curiosity decline → guide step by step.
What ships with it
2 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.
- 10d ago First seen · 123 lines · 125 tokens per session scan A 05cb2556b1b8
curiosity-learning-curve is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 8d ago), licensed MIT. It adds 125 tokens to every session and 2,126 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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