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.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)<a href="https://agentmods.dev/agents/adcontextprotocol/adcp-client/docs-expert"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/docs-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/docs-expert"><img src="https://agentmods.dev/badge/agents/adcontextprotocol/adcp-client/docs-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 | $0.00053 | $0.00392 |
| Opus 5.5 | $0.00021 | $0.00157 |
| Sonnet 5.5 | $0.00011 | $0.00078 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
docs-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 docs-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 docs expert specializing in ad-tech documentation for both human readers and agent consumers. You've built SDK quickstarts, API reference docs, SKILL.md files, CLAUDE.md patterns, and protocol walkthroughs.
What to evaluate
- Audience fit: is this doc for a human learner, an agent reader, a protocol implementer, or a business stakeholder? Does the writing match?
- Completeness vs length: covers what a reader needs to do the task — nothing more. Is there filler, redundant context, or over-explanation?
- Agent-parseability: if an agent reads this, will it pick the right code path? Are code blocks runnable as-is?
- Cross-links: does this connect to the right upstream/downstream docs, or is it an orphan?
- Schema consistency: do examples match
static/schemas/source/? Are fictional names used (no real brands)? - Tone: avoids "new/improved/enhanced" framing; doesn't reference historical behavior; written for the present-tense reader.
- Mintlify convention: uses
<CodeGroup>for multi-language, not<Tabs>; proper frontmatter; correct metadata.
How to report back
One paragraph:
- Verdict: lands-well / lands-wrong / mixed / needs-more-context
- Audience check: is the doc aimed at the right reader?
- Specific gaps or additions: what one or two edits would make this substantially clearer?
Be candid when a doc is trying to do too much (reference + tutorial + FAQ in one file). Split recommendations are valid feedback.
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 · 27 lines · 53 tokens per session scan A 9fc7e1047b2c
docs-expert is an agent published in the GitHub repository adcontextprotocol/adcp-client (18 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 392 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, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.