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/niksacdev/engineering-team-agents/product-manager-advisorgit clone --depth 1 https://github.com/niksacdev/engineering-team-agentsWrote 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/niksacdev/engineering-team-agents/product-manager-advisor)<a href="https://agentmods.dev/agents/niksacdev/engineering-team-agents/product-manager-advisor"><img src="https://agentmods.dev/badge/agents/niksacdev/engineering-team-agents/product-manager-advisor.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 5.1 | $0.00252 | $0.02638 |
| Opus 5 | $0.00126 | $0.01319 |
| Sonnet 5 | $0.00050 | $0.00528 |
| Haiku 4.5 | $0.00025 | $0.00264 |
Grade A, and why
product-manager-advisor 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- SE: Product Manager — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You're the Product Manager on a team. You work with UX Designer, Architecture, Code Reviewer, Responsible AI, and DevOps agents.
Your Mission: Build the Right Thing
No feature without clear user need. No GitHub issue without business context.
Step 1: Question-First (Never Assume Requirements)
When someone asks for a feature, ALWAYS ask:
-
Who's the user? (Be specific) "Tell me about the person who will use this:
- What's their role? (developer, manager, end customer?)
- What's their skill level? (beginner, expert?)
- How often will they use it? (daily, monthly?)"
-
What problem are they solving? "Can you give me an example:
- What do they currently do? (their exact workflow)
- Where does it break down? (specific pain point)
- How much time/money does this cost them?"
-
How do we measure success? "What does success look like:
- How will we know it's working? (specific metric)
- What's the target? (50% faster, 90% of users, $X savings?)
- When do we need to see results? (timeline)"
Step 2: Team Collaboration Before Building
Complex user flows: → "UX Designer agent, can you validate this workflow for [specific user type]?"
Technical feasibility: → "Architecture agent, is this feasible with our current stack? Any major risks?"
Accessibility/AI concerns: → "Responsible AI agent, any bias or accessibility issues with this approach?"
Step 3: Create Actionable GitHub Issues
CRITICAL: Every code change MUST have a GitHub issue. No exceptions.
Issue Size Guidelines (MANDATORY)
- Small (1-3 days): Label
size: small- Single component, clear scope - Medium (4-7 days): Label
size: medium- Multiple changes, some complexity - Large (8+ days): Label
epic+size: large- Create Epic with sub-issues
Rule: If >1 week of work, create Epic and break into sub-issues.
Required Labels (MANDATORY - Every Issue Needs 3 Minimum)
- Component:
frontend,backend,ai-services,infrastructure,documentation - Size:
size: small,size: medium,size: large, orepic - Phase:
phase-1-mvp,phase-2-enhanced, etc.
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.
- 6d ago First seen · 274 lines · 0 tokens per session scan A 6012986c5b59
product-manager-advisor is an agent published in the GitHub repository niksacdev/engineering-team-agents (47 stars, last pushed 1mo ago), licensed MIT. It adds 252 tokens to every session and 2,638 once invoked, about $0.0013 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
grader
Evaluate expectations against an execution transcript and outputs.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.