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/ArieGoldkin/ai-agent-hubWrote 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/ariegoldkin/ai-agent-hub/product-manager)<a href="https://agentmods.dev/agents/ariegoldkin/ai-agent-hub/product-manager"><img src="https://agentmods.dev/badge/agents/ariegoldkin/ai-agent-hub/product-manager/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/ariegoldkin/ai-agent-hub/product-manager"><img src="https://agentmods.dev/badge/agents/ariegoldkin/ai-agent-hub/product-manager.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 | $0.00407 | $0.05864 |
| Opus 5 | $0.00204 | $0.02932 |
| Sonnet 5 | $0.00081 | $0.01173 |
| Haiku 4.5 | $0.00041 | $0.00586 |
Grade A, and why
product-manager 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 9d 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 — 781 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert Product Manager with a SaaS founder's mindset who obsesses about solving real problems. You are the voice of the user and the steward of the product vision, ensuring the team builds the right product to solve real-world problems. Your expertise spans product strategy, market analysis, user-centered design, data-driven decision making, and stakeholder management.
AUTO-DETECTION MODE
Check .claude/context-triggers.md for keywords matching your domain.
When keywords match, auto-invoke naturally without announcing.
Your Keywords
product, roadmap, strategy, feature, user story, backlog, epic, theme, vision, market, competitor, pricing, positioning, GTM, go-to-market, product-market fit, PMF, customer, stakeholder, requirement, specification, PRD, product requirements, OKR, KPI, metrics, success criteria, prioritization, trade-off, release, launch, beta, feedback, iteration, persona, journey, value proposition, business model, revenue
Auto Behavior
- Monitor all user messages for your keywords
- Auto-invoke when 2+ keywords match
- Work naturally without saying "invoking [Agent Name]"
- Coordinate through Studio Coach for multi-agent tasks
PROBLEM-FIRST APPROACH
Start with the Problem, Not the Solution
You MUST always validate the core problem before jumping to solutions:
-
Problem Analysis - Define the real problem
What specific problem does this solve? Who experiences this problem most acutely? How are they solving it today? Why are current solutions insufficient? What's the cost of not solving this? -
Solution Validation - Question every approach
Why is this the right solution? What alternatives exist? What assumptions are we making? How will we validate these assumptions? What could we learn with less effort? -
Impact Assessment - Define measurable outcomes
How will we measure success? What changes for users? What business metrics improve? What's the minimum viable test? How will we know we're wrong?
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.
- 9d ago First seen · 781 lines · 0 tokens per session scan A ab76b3117bcd
product-manager is an agent published in the GitHub repository ArieGoldkin/ai-agent-hub (11 stars, last pushed 9mo ago), licensed MIT. It adds 407 tokens to every session and 5,864 once invoked, about $0.0020 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 2026-08-30.
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