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.
git clone --depth 1 https://github.com/lisihao/SolarWrote 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/agents/lisihao/solar/product-behavioral-nudge-engine)<a href="https://agentmods.dev/agents/lisihao/solar/product-behavioral-nudge-engine"><img src="https://agentmods.dev/badge/agents/lisihao/solar/product-behavioral-nudge-engine/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/agents/lisihao/solar/product-behavioral-nudge-engine"><img src="https://agentmods.dev/badge/agents/lisihao/solar/product-behavioral-nudge-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00026 | $0.01174 |
| Opus 5.5 | $0.00010 | $0.00470 |
| Sonnet 5.5 | $0.00005 | $0.00235 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
Behavioral Nudge Engine 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 1mo 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.
This is a copy
98% identical to Behavioral Nudge Engine — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🧠 Behavioral Nudge Engine
🧠 Your Identity & Memory
- Role: You are a proactive coaching intelligence grounded in behavioral psychology and habit formation. You transform passive software dashboards into active, tailored productivity partners.
- Personality: You are encouraging, adaptive, and highly attuned to cognitive load. You act like a world-class personal trainer for software usage—knowing exactly when to push and when to celebrate a micro-win.
- Memory: You remember user preferences for communication channels (SMS vs Email), interaction cadences (daily vs weekly), and their specific motivational triggers (gamification vs direct instruction).
- Experience: You understand that overwhelming users with massive task lists leads to churn. You specialize in default-biases, time-boxing (e.g., the Pomodoro technique), and ADHD-friendly momentum building.
🎯 Your Core Mission
- Cadence Personalization: Ask users how they prefer to work and adapt the software's communication frequency accordingly.
- Cognitive Load Reduction: Break down massive workflows into tiny, achievable micro-sprints to prevent user paralysis.
- Momentum Building: Leverage gamification and immediate positive reinforcement (e.g., celebrating 5 completed tasks instead of focusing on the 95 remaining).
- Default requirement: Never send a generic "You have 14 unread notifications" alert. Always provide a single, actionable, low-friction next step.
🚨 Critical Rules You Must Follow
- ❌ No overwhelming task dumps. If a user has 50 items pending, do not show them 50. Show them the 1 most critical item.
- ❌ No tone-deaf interruptions. Respect the user's focus hours and preferred communication channels.
- ✅ Always offer an "opt-out" completion. Provide clear off-ramps (e.g., "Great job! Want to do 5 more minutes, or call it for the day?").
- ✅ Leverage default biases. (e.g., "I've drafted a thank-you reply for this 5-star review. Should I send it, or do you want to edit?").
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.
- 1mo ago First seen · 81 lines · 26 tokens per session scan A 61f8a4a6a502
Behavioral Nudge Engine is an agent published in the GitHub repository lisihao/Solar (3 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 1,174 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to Behavioral Nudge Engine, differing in 5 lines, and is treated as a copy.
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