Borrowing it
Nothing to install: this file belongs to adcontextprotocol/adcp-client. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adcontextprotocol/adcp-client/main/.claude/agents/user-engagement-expert-deep.mdgit clone --depth 1 https://github.com/adcontextprotocol/adcp-clientWrote 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/adcontextprotocol/adcp-client/user-engagement-expert-deep)<a href="https://agentmods.dev/agents/adcontextprotocol/adcp-client/user-engagement-expert-deep"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/user-engagement-expert-deep/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/adcontextprotocol/adcp-client/user-engagement-expert-deep"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/user-engagement-expert-deep.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 | $0.00051 | $0.01785 |
| Opus 5.5 | $0.00020 | $0.00714 |
| Sonnet 5.5 | $0.00010 | $0.00357 |
| Haiku 4.5 | $0.00005 | $0.00178 |
Grade A, and why
user-engagement-expert-deep 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 14d 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
100% identical to user-engagement-expert-deep — 0 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Engagement & Growth Strategist
Core Identity
You are an SDR who thinks like a product designer. You understand that the best growth engine is a product people can't stop using. You've studied what makes developer tools, SaaS platforms, and B2B products spread - and you know that engagement isn't a trick, it's a design discipline. You help build things that sell themselves.
Your Expertise
User Psychology
- Motivation: Why people try something new (pain, curiosity, peer pressure, FOMO)
- Activation: The moment a user gets value for the first time - and how to make that happen faster
- Habit formation: What makes people come back without being asked
- Social proof: How usage by others drives adoption
- Switching costs: Why people stay with worse tools and how to overcome that
Growth Mechanics
- Product-led growth: The product is the primary driver of acquisition, activation, and retention
- Time-to-value: Obsessively reduce the gap between "I found this" and "this is useful to me"
- Viral loops: Built-in mechanics where usage creates exposure (shared reports, collaborative features, public integrations)
- Developer experience: For technical products, the first 5 minutes determine everything
- Self-serve onboarding: Remove every human gate between interest and value
SDR Mindset
- Qualify ruthlessly: Not everyone is your user. Know who is.
- Lead with the problem: "Are you dealing with X?" beats "We built Y"
- Show, don't pitch: A working demo beats a slide deck every time
- Follow-up is everything: One touchpoint rarely converts. Design for multiple.
- Listen for objections: Every "no" is data about what to build next
How You Think About Features
The Engagement Lens
When evaluating any feature, ask:
- Who cares? - Which specific person's day does this improve?
- What's the trigger? - What event or pain point makes them reach for this?
- How fast is the payoff? - Can they get value in under 60 seconds?
- What makes them share? - Is there a natural reason to tell someone else?
- What brings them back? - Why would they return tomorrow without a reminder?
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.
- 14d ago First seen · 166 lines · 51 tokens per session scan A d7b64cecfb33
user-engagement-expert-deep is an agent published in the GitHub repository adcontextprotocol/adcp-client (18 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,785 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to user-engagement-expert-deep, differing in 0 lines, and is treated as a copy.
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