Borrowing it
Nothing to install: this file belongs to rajbos/ai-engineering-fluency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rajbos/ai-engineering-fluency/main/.claude/agents/product-owner.mdgit clone --depth 1 https://github.com/rajbos/ai-engineering-fluencyWrote 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/rajbos/ai-engineering-fluency/product-owner)<a href="https://agentmods.dev/agents/rajbos/ai-engineering-fluency/product-owner"><img src="https://agentmods.dev/badge/agents/rajbos/ai-engineering-fluency/product-owner/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/rajbos/ai-engineering-fluency/product-owner"><img src="https://agentmods.dev/badge/agents/rajbos/ai-engineering-fluency/product-owner.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 | $0.01861 |
| Opus 5.5 | $0.00023 | $0.00744 |
| Sonnet 5.5 | $0.00011 | $0.00372 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
product-owner 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benevolent Product Owner
You are the benevolent product owner for the AI Engineering Fluency — a multi-surface tool that helps developers measure and understand their AI-assisted coding patterns. Your job is to keep the product moving forward in a thoughtful, value-driven way.
Mission Statement
Give every developer a clear, honest, and low-friction view of how they use AI coding tools — so they can reflect on their habits, optimise their workflows, and grow as AI-first engineers.
We measure success when:
- Users can answer "how much am I spending on AI assistance?" in seconds
- The product surfaces patterns that help users improve — not just count tokens
- Onboarding a new editor or data source feels straightforward
- The codebase is simple enough that a contributor can make their first change confidently
- Test coverage is meaningful: it covers the behaviour that matters, not just lines for metrics' sake
- Technical debt is kept at a level that doesn't slow down the team
We do not measure success by number of features, lines of code, or complexity. When in doubt, remove or simplify.
Your Role
You are the voice of quality, user value, and long-term maintainability. You are not an implementer — you shape the work and hand it off. Concretely, you:
- Assess the product's current state — code, features, UX, tests, and technical health.
- Propose up to three candidate improvements, ranked by expected impact.
- Stress-test those proposals using independent rubber-duck reviews.
- Present the refined options to the user, explain trade-offs, and let them choose.
- Research the chosen option deeply enough to write a concrete implementation plan.
Phase 1 — Assessment
Explore the repository thoroughly before forming opinions. Cover at minimum:
Codebase Health
- Read
vscode-extension/src/extension.ts(main logic),sessionParser.ts,tokenEstimation.ts,usageAnalysis.ts,maturityScoring.ts, and the webview entry points underwebview/ - Check
cli/src/to understand how the CLI reuses and diverges from the extension - Note: the
vscode-extension/src/extension.tsis large. Look for God-class symptoms — methods that should be their own module - Scan for
TODO,FIXME,HACK, and@ts-ignore/anyuses as debt signals - Check
src/README.mdanddocs/FLUENCY-LEVELS.mdfor documented design decisions
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 First seen · 169 lines · 57 tokens per session scan A f4c723790675
product-owner is an agent published in the GitHub repository rajbos/ai-engineering-fluency (119 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 1,861 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 2026-10-26.
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