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/amekala/ads-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/commands/amekala/ads-mcp/performance-review)<a href="https://agentmods.dev/commands/amekala/ads-mcp/performance-review"><img src="https://agentmods.dev/badge/commands/amekala/ads-mcp/performance-review.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 | $0.00009 | $0.00185 |
| Opus 5 | $0.00005 | $0.00093 |
| Sonnet 5 | $0.00002 | $0.00037 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
performance-review 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.
What it actually says
Run a full cross-platform performance review for $ARGUMENTS (default: last 30 days).
- Read CLAUDE.md for KPI targets and brand context
- Pull live data from all connected platforms. Follow
adspirer-mcpfor the call contract:get_campaign_performance(Google) andget_meta_campaign_performance(Meta) are direct calls; LinkedIn, TikTok, Amazon, and ChatGPT Ads go through their router with{"action": "execute", "tool_name": "..."} - Compare actuals vs KPI targets from CLAUDE.md
- Present a unified scorecard table with all platforms
- Highlight top problems and recommend top 3 actions
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 · 12 lines · 9 tokens per session scan A bb2752d50cc3
performance-review is a command published in the GitHub repository amekala/ads-mcp (89 stars, last pushed today), licensed MIT. It adds 9 tokens to every session and 185 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
audit
Run a comprehensive 7-dimension account audit.
analyze
Analyze the Google Ads account using the google-ads-analysis skill. Pull campaign performance data, identify anomalies, and provide actionable recommendations.
awesome-chatgpt
Search awesome-ChatGPT-repositories for open-source GitHub repositories related to ChatGPT and LLMs.
ship-an-mcp-server
Workflow recipe — make your product agent-usable by chaining 4 skills, spec to pricing.
rescue-an-account
Workflow recipe — diagnose an at-risk customer and build the full save play through to renewal by chaining 4 skills.
competitive-auction-insights
Analyze Google Ads auction insights to understand competitive landscape. See who you're competing against and where you're winning or losing. (requires Pro subscription).