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/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/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/random6913/claude-code-superkit/behavioral-nudge-engine"><img src="https://agentmods.dev/badge/agents/random6913/claude-code-superkit/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 | $0.00048 | $0.02005 |
| Opus 5.5 | $0.00019 | $0.00802 |
| Sonnet 5.5 | $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 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.
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.
- 1mo ago 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 (3 stars, last pushed 4d ago), licensed MIT. It adds 48 tokens to every session and 2,005 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
apm-expert
Expert on APM (Agent Package Manager). Helps users install, configure, author, and troubleshoot APM packages, dependencies, compilation, MCP servers, and governance policies.
deep-research-writer
Writes the single report.md of a nav-deep-research run from the run's source notes, following the readable report layout and the citation contract the ship gate checks. Spawned once by the nav-deep-research skill at step 3 (draft). Writes the report exactly once; later changes are the patcher's.
deep-research-critic
Adversarial reader of a nav-deep-research draft. Finds ignored counter-evidence, unsupported claims, verbatim quotes from paraphrase-only sources, and structural mismatch against the decomposition. Writes a findings JSON the patcher applies; cannot edit the report. Spawned once at step 4 by the nav-deep-research skill.
task-planner
Implementation planning and task breakdown specialist. Use PROACTIVELY when planning features, refactoring, or complex changes. Creates detailed implementation plans with effort estimates.
Lyra
Writer/builder instrument. Takes a complete spec, returns a diff or built artifact. Use for schema migrations, adapter ports, dashboard patches, vault note synthesis, and any code edit with bounded scope. Reports honesty ledger on completion.
Hestia
Vault custodian instrument. Routine hygiene — sweeps DELEGATIONTRACKER for stale/closed items, flips cold-bottom-20 notes to dormant in HEATINDEX, finds and reports broken wikilinks. Tends the hearth. Voice is terse, factual, lists what was swept. Cadence: 7-day. Edit-capable for status field updates.