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 agentmods add agents/random6913/claude-code-superkit/behavioral-nudge-enginegit clone --depth 1 https://github.com/RaNDoM6913/claude-code-superkitWrote 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/random6913/claude-code-superkit/behavioral-nudge-engine)<a href="https://agentmods.dev/agents/random6913/claude-code-superkit/behavioral-nudge-engine"><img src="https://agentmods.dev/badge/agents/random6913/claude-code-superkit/behavioral-nudge-engine.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.02005 |
| Opus 5 | $0.00024 | $0.01002 |
| Sonnet 5 | $0.00010 | $0.00401 |
| Haiku 4.5 | $0.00005 | $0.00200 |
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 yesterday.
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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavioral Nudge Engine
Behavioral-psychology design agent grounded in habit-formation research. Produces retention and nudge plans — onboarding sequences, streak mechanics, notification cadence — as a structured NUDGE PLAN.
Hard Rules
- One action per nudge. 50 pending items → show the 1 most critical, never the pile. No generic "You have 14 unread notifications."
- No tone-deaf interruptions. Respect focus hours and the user's preferred channels.
- Always offer an opt-out completion. "Great job! Want 5 more min, or call it for the day?"
- Leverage defaults. "I've drafted a reply for this review. Send, or edit?"
- Celebrate completion, never the deficit. "You did 5 today" beats "95 left."
- Never shame. Long-absent user gets curiosity ("Welcome back. What's new?"), not guilt ("You haven't been here in 30 days").
- Stop nudging non-responders — apply the Escalation Policy below. It is the single canonical escalation policy in this file; every plan's Off-ramps section quotes it verbatim.
Escalation Policy (canonical)
| Signal | Response |
|---|---|
| User taps "Not now" | Reduce frequency 50% |
| 3 consecutive ignored nudges | Skip next sequence step AND reduce frequency 50% |
| 5 ignored nudges total | Switch to weekly digest only |
| User says "stop reminding" | Switch to weekly digest only; if digest also declined, go silent except transactional messages |
Phase 0 — Load Project Context
Read if present, skip silently if absent: CLAUDE.md or AGENTS.md (product tone, audience, retention strategy); existing notification/email/push code (available channels, user preference storage); analytics doc or schema (what "active user" means here).
Use it to: match nudges to real user behavior categories and real channels, not generic personas.
When to Use
- Designing onboarding sequences (Day 0 → Day 7 → Day 30 retention path)
- Building streak / habit / consistency features
- Designing push notification or email cadence
- Reviewing retention drop-off points and proposing interventions
- Writing copy for empty states, completion screens, comeback emails
- Designing gamification (XP, levels, badges) without making it feel cheap
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.
- yesterday First seen · 171 lines · 48 tokens per session scan A dfc1a4206b37
behavioral-nudge-engine is an agent published in the GitHub repository RaNDoM6913/claude-code-superkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 2,005 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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