Borrowing it
Nothing to install: this file belongs to davidmosiah/google-ads-intent-mcp. 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/davidmosiah/google-ads-intent-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/davidmosiah/google-ads-intent-mcpWrote 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/instructions/davidmosiah/google-ads-intent-mcp/agents-md)<a href="https://agentmods.dev/instructions/davidmosiah/google-ads-intent-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/davidmosiah/google-ads-intent-mcp/agents-md.svg" alt="Measured on agentmods" 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 | $0.00167 | $0.00167 |
| Opus 5 | $0.00084 | $0.00084 |
| Sonnet 5 | $0.00033 | $0.00033 |
| Haiku 4.5 | $0.00017 | $0.00017 |
Grade A, and why
google-ads-intent-mcp AGENTS.md 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 6d 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.
What it actually says
Agent Development Notes
Scope
This repo is a Python CLI plus optional MCP server for dry-run Google Ads search-term intent analysis and negative-keyword planning.
Commands
- Install dev deps:
pip install -e ".[dev]" - Test:
pytest - Build package:
python -m build - Check dist:
twine check dist/* - CLI smoke:
google-ads-intent doctor
Rules
- Never commit Google Ads credentials, OAuth tokens, customer IDs, real search-term exports, or private campaign data.
- Keep live mutation out of the default path; v0.1 should remain dry-run first.
- Keep buyer/conversion intent protected in negative-keyword logic.
- Keep PyPI publishing on Trusted Publishing; do not add long-lived PyPI tokens.
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.
- 6d ago First seen · 21 lines · 167 tokens per session scan A 23c5d5bc64fe
google-ads-intent-mcp AGENTS.md is an instructions file published in the GitHub repository davidmosiah/google-ads-intent-mcp (2 stars, last pushed 8d ago), licensed MIT. It adds 167 tokens to every session, about $0.0008 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 2026-08-31.
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