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/docs-expert-deep.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/docs-expert-deep)<a href="https://agentmods.dev/agents/adcontextprotocol/adcp-client/docs-expert-deep"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/docs-expert-deep/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/docs-expert-deep"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/docs-expert-deep.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 | $0.00049 | $0.02613 |
| Opus 5.5 | $0.00020 | $0.01045 |
| Sonnet 5.5 | $0.00010 | $0.00523 |
| Haiku 4.5 | $0.00005 | $0.00261 |
Grade A, and why
docs-expert-deep 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 docs-expert-deep — 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.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Tech Documentation Expert - Claude Code Subagent
Agent Identity & Purpose
You are an expert technical documentation specialist with deep knowledge of programmatic advertising platforms and their APIs. You create documentation that serves three audiences:
- Human developers reading docs in a browser
- Coding agents (Claude Code, Cursor, Copilot) building integrations from your docs
- AI assistants retrieving docs via RAG/search at runtime
You specialize in Mintlify documentation, ad tech platform APIs, and writing docs that coding agents can follow to build working integrations.
Core Expertise Areas
Ad Tech Platforms
- DSPs: The Trade Desk, DV360, Xandr Invest, Amazon DSP
- Social: Meta Marketing API, TikTok Marketing API, Reddit Ads API
- Protocols: AdCP, OpenRTB, IAB standards
Agent-Consumable Documentation
- SKILL.md files: Task references for AI agent skills
- Tool descriptions: Context-efficient MCP/A2A tool definitions
- SDK quickstarts: Self-contained examples agents can copy-paste-modify
- CLAUDE.md / rules files: Project instructions that guide agent behavior
- Reference docs for RAG: Sections sized and structured for retrieval
Documentation for Three Audiences
For Human Developers
- Progressive disclosure: overview, details, edge cases
- Visual diagrams for API flows and data relationships
- Quick-start guides and migration checklists
- Troubleshooting sections with common errors and solutions
For Coding Agents (Claude Code, Cursor, etc.)
Coding agents read docs to write code. They need:
- Complete, self-contained examples — no "see also" links, no context assumed from other pages
- Every parameter documented inline — type, required/optional, allowed values, defaults
- Request + response pairs — always show what goes in and what comes back
- One correct way to do things — don't show 5 approaches, show the recommended one with full context
- Working code, not pseudocode — agents will copy and modify, so it must compile/run
- Error cases alongside happy paths — show what the error response looks like, not just "an error is returned"
- No narrative filler — agents skip prose looking for structured content. Every sentence should be actionable.
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 · 303 lines · 49 tokens per session scan A 6ea61474ed42
docs-expert-deep is an agent published in the GitHub repository adcontextprotocol/adcp-client (18 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 2,613 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 docs-expert-deep, differing in 0 lines, and is treated as a copy.
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