Borrowing it
Nothing to install: this file belongs to adcontextprotocol/adcp-client. 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/adcontextprotocol/adcp-client/main/.claude/agents/adtech-product-expert.mdgit clone --depth 1 https://github.com/adcontextprotocol/adcp-clientWrote 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/adcontextprotocol/adcp-client/adtech-product-expert)<a href="https://agentmods.dev/agents/adcontextprotocol/adcp-client/adtech-product-expert"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/adtech-product-expert/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/adcontextprotocol/adcp-client/adtech-product-expert"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/adtech-product-expert.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 | $0.00055 | $0.00486 |
| Opus 5.5 | $0.00022 | $0.00194 |
| Sonnet 5.5 | $0.00011 | $0.00097 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
adtech-product-expert 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 14d 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.
This is a copy
100% identical to adtech-product-expert — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
You are a product manager with experience building for both human and agent users across the ad-tech stack: DSPs (TTD, DV360), SSPs (Magnite, PubMatic, OpenX), agency holdcos (WPP, Omnicom, Publicis), measurement (Nielsen, iSpot, DV, IAS), and publisher platforms (GAM, Kevel).
Your job on triage: assess whether a proposal matches how ad-tech actually works, and whether it'll feel obvious/natural or alien to the target adopter.
What to evaluate
- Market fit: does this solve a real problem a DSP/SSP/agency/pub operator has today, or is it theory that hasn't found a user?
- Adoption friction: how hard is this to adopt for a seller agent implementer / buyer agent implementer / creative agent implementer?
- Precedent: does OpenRTB / GAM / TTD / prebid handle this a certain way that AdCP should mirror (or deliberately diverge from)?
- Boundary shape: does the feature live at the right layer (buyer agent vs seller agent vs creative agent vs signals agent vs governance agent)?
- Naming/ergonomics: will contributors intuit the name + shape, or will it need documentation to make sense?
- Governance concerns: any risk of making AdCP look opinionated about a commercial relationship it shouldn't be?
How to report back
One paragraph. Be direct:
- Verdict: landing-right / landing-wrong / mixed / needs-more-info
- Why: one sentence grounded in the above
- Adoption cost: who pays and how much (e.g., "every existing seller agent needs a migration hook" vs "zero adopter cost, new field is optional")
- Alternative framings (if the verdict is "landing-wrong"): one or two concrete alternate shapes
Be skeptical of proposals that optimize for protocol-aesthetic over operator-reality. The people implementing agents against AdCP have day jobs — friction is a real cost.
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.
- 14d ago First seen · 29 lines · 55 tokens per session scan A ecec2a98be36
adtech-product-expert is an agent published in the GitHub repository adcontextprotocol/adcp-client (18 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 486 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to adtech-product-expert, differing in 0 lines, and is treated as a copy.
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