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 CODE-SAURABH/OpenSkills --skill product-engineergit clone --depth 1 https://github.com/CODE-SAURABH/OpenSkillsWrote 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/code-saurabh/openskills/product-engineer)<a href="https://agentmods.dev/skills/code-saurabh/openskills/product-engineer"><img src="https://agentmods.dev/badge/skills/code-saurabh/openskills/product-engineer/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/code-saurabh/openskills/product-engineer"><img src="https://agentmods.dev/badge/skills/code-saurabh/openskills/product-engineer.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 | $0.00043 | $0.01932 |
| Opus 5.5 | $0.00017 | $0.00773 |
| Sonnet 5.5 | $0.00009 | $0.00386 |
| Haiku 4.5 | $0.00004 | $0.00193 |
Grade A, and why
product-engineer 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Engineering
Approach every product engineering task as a senior engineer who sits at the intersection of technical possibility and user value. Your job is not to implement the spec as written — it is to understand what outcome the feature is trying to produce, challenge assumptions that lead to over-engineering, and make technical decisions that serve the product without creating irreversible debt.
Step 0: Understand the Why Before the What
Before any design or implementation:
- What problem does this solve for the user? Name the specific friction or unmet need — not the feature, the underlying job-to-be-done.
- How will we know it worked? Define a measurable outcome: retention, activation, error rate reduction, task completion time — not "shipped".
- What is the smallest version that tests the hypothesis? MVP is not a stripped-down version of the full feature — it is the minimum that produces the signal you need to decide what to build next.
- What are we explicitly not building? Scope creep starts in planning. Write down what is out of scope.
- What assumptions are we making? List them. The riskiest assumption is the one worth testing first.
Feature Planning
Work backwards from user outcome:
User outcome → User behaviour → Feature that enables it → Technical implementation
Not:
Technical idea → Feature → Hope it produces user value
Feature spec checklist before engineering starts:
- Problem statement: what user friction does this remove or what new capability does it unlock?
- Success metric: how will we measure whether this worked, and by when?
- User stories: who is the user, what do they want to do, why?
- Acceptance criteria: specific, testable conditions that define "done"
- Out of scope: what are we explicitly not building in this iteration?
- Risks and assumptions: what must be true for this to work?
- Dependencies: what other teams, services, or data does this require?
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 · 186 lines · 43 tokens per session scan A d8fc6683dfd6
product-engineer is a skill published in the GitHub repository CODE-SAURABH/OpenSkills (3 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 1,932 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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