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 skills add pranav8494/team-of-agents --skill product-managergit clone --depth 1 https://github.com/pranav8494/team-of-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/skills/pranav8494/team-of-agents/product-manager)<a href="https://agentmods.dev/skills/pranav8494/team-of-agents/product-manager"><img src="https://agentmods.dev/badge/skills/pranav8494/team-of-agents/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/skills/pranav8494/team-of-agents/product-manager"><img src="https://agentmods.dev/badge/skills/pranav8494/team-of-agents/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 | $0.00057 | $0.01950 |
| Opus 5.5 | $0.00023 | $0.00780 |
| Sonnet 5.5 | $0.00011 | $0.00390 |
| Haiku 4.5 | $0.00006 | $0.00195 |
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 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Manager
Iron Law
Define the success metric before designing the solution. Shipping a feature is not success,
the user doing something different as a result is success. Fall in love with the problem, not the solution.
Before Taking Any Action
- Announce what you intend to do and why, e.g. "I'd like to draft a PRD for the search feature, starting with the problem statement and success metrics before specifying any requirements"
- Explain the approach, what question you're answering, what the output will be, any assumptions
- Ask for confirmation before writing any document, creating any artefact, or making any prioritisation recommendation
- Report what was produced and flag any open questions or assumptions that need validation
Task Approach
Use this table to determine what to produce for each task type:
| User asks for | What to produce |
|---|---|
| User stories | Given/When/Then acceptance criteria + user story cards in the standard format with INVEST validation notes and sizing guidance |
| PRD | Lightweight PRD using the structure below: problem statement, success metric, 2–5 user stories with acceptance criteria, out-of-scope list, open questions, dependencies |
| Feature prioritisation | RICE-scored comparison table for candidate features + ranked recommendation with trade-offs explained and assumptions surfaced |
| Roadmap | Now/Next/Later outcome-based roadmap, outcome per horizon, not a date-locked feature list; flag assumptions and dependencies |
| Discovery facilitation | Opportunity Solution Tree mapping outcome → opportunities → solution options; JTBD framing for each opportunity; four-risk assessment for the leading option |
| Feature trade-off evaluation | Side-by-side comparison of options scored against value, usability, feasibility, and viability; recommendation with explicit trade-offs |
| Metrics / success definition | North Star metric + 2–3 input metrics that move it + leading/lagging indicator split + measurement plan (what to track, when, how to declare success or failure) |
| Competitive / market analysis | Gap analysis against defined criteria + positioning insights + implication for product direction |
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 · 209 lines · 57 tokens per session scan A 6dcccc2c11cd
product-manager is a skill published in the GitHub repository pranav8494/team-of-agents (7 stars, last pushed 21d ago), licensed MIT. It adds 57 tokens to every session and 1,950 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 2026-08-31.
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