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/Ad-Superpowers/ad-superpowers-pluginWrote 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/ad-superpowers/ad-superpowers-plugin/competitive-auction-insights)<a href="https://agentmods.dev/commands/ad-superpowers/ad-superpowers-plugin/competitive-auction-insights"><img src="https://agentmods.dev/badge/commands/ad-superpowers/ad-superpowers-plugin/competitive-auction-insights/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/commands/ad-superpowers/ad-superpowers-plugin/competitive-auction-insights"><img src="https://agentmods.dev/badge/commands/ad-superpowers/ad-superpowers-plugin/competitive-auction-insights.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 | $0.00022 | $0.01111 |
| Opus 5.5 | $0.00009 | $0.00444 |
| Sonnet 5.5 | $0.00004 | $0.00222 |
| Haiku 4.5 | $0.00002 | $0.00111 |
Grade A, and why
competitive-auction-insights 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Platforms: google_ads Included in trial and paid plans. Your existing quota and permissions still apply.
This command requires the Ad Superpowers MCP connector to access your ad account data. Connect at https://app.adsuperpowers.ai if you haven't already.
Competitive Auction Insights
Analyze the competitive landscape in Google Ads using auction insights data.
- Focus: all campaigns
OUTPUT FORMAT (CRITICAL - follow this EXACT structure)
Competitive Position
| Metric | Your Score | Benchmark | Status |
|---|---|---|---|
| Search Impression Share | XX% | 30-40% (non-brand) / 80%+ (brand) | Good/Poor |
| Top of Page Rate | XX% | 40-60% | Good/Poor |
| Absolute Top Rate | XX% | 15-25% | Good/Poor |
| Avg Outranking Share | XX% | - | - |
Top Competitors (by overlap rate)
| Competitor | Overlap | Pos Above Rate | Outranking Share | Threat |
|---|---|---|---|---|
| domain.com | XX% | XX% | XX% | HIGH/MED/LOW |
Threat level: HIGH = overlap >60% AND position above >50%. MED = overlap >40%. LOW = all others.
Competitive Strength Score
Score = (Impression Share * 0.3) + (Top of Page Rate * 0.3) + (Avg Outranking Share * 0.4)
90-100: Dominant | 70-89: Strong | 50-69: Competitive | <50: Weak
Recommendations
Defensive (protect your position):
- [Brand protection actions]
- [Position defense actions]
Offensive (gain ground):
- [Competitor weakness to exploit]
- [Market gap identified]
Budget Implication: To gain 10% more impression share: +EUR X,XXX/month estimate.
EXECUTION STEPS
Step 1: List accounts
google_ads_list_accounts()
Identify the target account. If user specified an account, use it directly.
Step 2: Pull auction insights for search campaigns
google_ads_run_gaql(
customer_id="CUSTOMER_ID",
query="SELECT campaign.name, campaign.campaign_budget, metrics.search_impression_share, metrics.search_top_impression_share, metrics.search_absolute_top_impression_share, metrics.search_budget_lost_impression_share, metrics.search_rank_lost_impression_share FROM campaign WHERE campaign.advertising_channel_type = 'SEARCH' AND campaign.status = 'ENABLED' AND segments.date DURING LAST_30_DAYS ORDER BY metrics.impressions DESC LIMIT 20"
)
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.
- 1mo ago Changed · -5 tokens per session 76424791ebca
- 1mo ago First seen · 106 lines · 27 tokens per session scan A ce80c358d548
competitive-auction-insights is a command published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 4d ago), licensed MIT. It adds 22 tokens to every session and 1,111 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other commands, from other repositories
create-campaign
Create a new ad campaign with AI-generated strategy.
optimize
Get optimization recommendations for your campaigns.
audit
Google Ads command — audit.
logout
Google Ads command — logout.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.