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 joao-aires/agent-skills-public --skill product-managementgit clone --depth 1 https://github.com/joao-aires/agent-skills-publicWrote 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/joao-aires/agent-skills-public/product-management)<a href="https://agentmods.dev/skills/joao-aires/agent-skills-public/product-management"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/product-management/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/joao-aires/agent-skills-public/product-management"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/product-management.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 | $0.00046 | $0.01003 |
| Opus 5.5 | $0.00018 | $0.00401 |
| Sonnet 5.5 | $0.00009 | $0.00201 |
| Haiku 4.5 | $0.00005 | $0.00100 |
Grade A, and why
product-management 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 yesterday.
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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decide which product outcome to pursue
Read composition guidance for optional expertise, fallback and scope.
Help choose a worthwhile user problem and a bounded response, with evidence that can change the decision. Apply directly to a product question or as support in an authorized development engagement. Scale the work to the uncertainty: a personal tool may need a short purpose and scope decision, while an uncertain commercial proposition needs deeper investigation. A defined bug or accepted feature does not restart discovery.
Establish the problem and evidence
Read existing product decisions, supplied research, feedback and actual behavior.
- Identify the intended users, their job and current workaround, the difficulty or unmet outcome, and the intended personal, internal or commercial use.
- Separate observed facts, interpretations and untested demand or solution assumptions.
- Reuse answers already established.
Question the proposed feature when a simpler product, process or existing tool could achieve the outcome. Investigate decision-changing technical facts; ask about unresolved user priorities, usage or commitments. Use business strategy for competitive position, value capture and business viability when those are the actual question. Product direction works within accepted strategy and can supply evidence that reopens it.
Choose a useful slice and what to defer
Compare responses to the same user problem, including doing less or changing the surrounding workflow. Establish who benefits, how often the problem occurs, how much it prevents the intended outcome, and how strong the evidence is. Then consider what each response costs to learn about, deliver and maintain. Separate a low-cost idea from one whose adoption depends on changing several teams or external systems.
Use these considerations to make a choice, rather than presenting an unranked list of attractive features. Explain which evidence supports the recommendation and which uncertain belief could reverse it. A scoring framework helps when its inputs can be defended; invented precision can conceal the actual judgment. State what is deferred and why. A feature request is evidence of a need, not an automatic backlog commitment.
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.
- yesterday Changed · +27 lines 9c734f3dde57
- 2d ago First seen · 44 lines · 46 tokens per session scan A 9ba0d3fda167
product-management is a skill published in the GitHub repository joao-aires/agent-skills-public (2 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,003 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 2026-10-20.
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