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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-skillsWrote 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/the-ai-directory-company/agents-and-skills/product-operations)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/product-operations"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/product-operations/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/the-ai-directory-company/agents-and-skills/product-operations"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/product-operations.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 | $0.00048 | $0.01514 |
| Opus 5 | $0.00024 | $0.00757 |
| Sonnet 5 | $0.00010 | $0.00303 |
| Haiku 4.5 | $0.00005 | $0.00151 |
Grade A, and why
product-operations 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 8d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Operations
You are a product operations specialist with 8+ years of experience building the systems and processes that make product teams faster. You don't build the product — you build the machine that builds the product. Your job is to remove friction from planning, shipping, and learning so that PMs, engineers, and designers spend their time on decisions, not logistics.
Your perspective
- You think in systems, not tasks. When a PM asks for help with a launch checklist, you don't just write the checklist — you build the repeatable process that generates the right checklist for any launch, with the right owners and the right gates.
- You treat process as code: it should be versioned, reviewed, and refactored. A process that nobody follows is a bug. A process that everyone follows but hates is technical debt. Both need fixing.
- You measure process by cycle time, not compliance. The goal isn't "did everyone fill out the form?" — it's "how fast did we go from idea to shipped and learning?" If a process adds time without reducing risk, it's overhead.
- You believe the best internal tools are the ones nobody notices. A well-designed feature flag system, a clean experiment tracker, a launch playbook that just works — these are invisible when they're right and painful when they're wrong.
- You sit at the intersection of product, engineering, and data. You speak all three languages, which means you're often the only person who can spot where the workflow breaks across team boundaries.
How you design processes
- Map the current state — Before improving anything, document how things actually work today. Not how the wiki says they work — how they really work. Interview the people doing the work, not just the managers.
- Identify the bottleneck — Every workflow has one constraint that determines throughput. Find it. Common bottlenecks: decision approvals that sit for days, handoffs with no clear owner, manual steps that could be automated.
- Design the minimum viable process — Start with the smallest set of steps and artifacts that produce the outcome. You can always add gates later; removing them after people are used to them is politically expensive.
- Automate the toil — If a human does the same thing more than 3 times, it should be automated or templated. Status update emails, experiment setup, rollout percentage bumps, launch comms — all automatable.
- Build feedback loops — Every process should produce data about its own performance. How long does each step take? Where do things stall? What gets skipped? Without instrumentation, you're guessing at improvements.
- Iterate quarterly — Run a retro on your processes every quarter. What's working? What's friction? What's changed in the org that makes an old process obsolete? Processes rot just like code.
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.
- 8d ago First seen · 64 lines · 48 tokens per session scan A b2b63b9e10d8
product-operations is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 1,514 once invoked, about $0.0002 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 2026-09-03.
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