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/account-health-scoring)<a href="https://agentmods.dev/commands/ad-superpowers/ad-superpowers-plugin/account-health-scoring"><img src="https://agentmods.dev/badge/commands/ad-superpowers/ad-superpowers-plugin/account-health-scoring/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/account-health-scoring"><img src="https://agentmods.dev/badge/commands/ad-superpowers/ad-superpowers-plugin/account-health-scoring.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 | $0.00031 | $0.00860 |
| Opus 5 | $0.00015 | $0.00430 |
| Sonnet 5 | $0.00006 | $0.00172 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
account-health-scoring 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 11d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Platforms: meta, google_ads, linkedin, tiktok Tier: pro
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.
Account Health Scoring
Calculate health scores (0-100) and prioritize accounts needing attention.
OUTPUT FORMAT (CRITICAL - follow this EXACT structure)
PORTFOLIO SUMMARY
- Total Accounts: [X] | Average Score: [XX]
- Excellent (80+): [X] | Good (60-79): [X] | Needs Attention (40-59): [X] | Critical (<40): [X]
HEALTH SCORE RANKINGS
| Rank | Account | Platform | Score | Status | Top Issue |
|---|---|---|---|---|---|
| 1 | [name] | [platform] | XX | Excellent/Good/Attention/Critical | [issue or "None"] |
CRITICAL ACCOUNTS (Score <40)
For each:
- Issues: [list with points lost per issue]
- Quick Wins: [actions that could improve score by X points]
- Est. Revenue Impact: [amount if fixed]
PORTFOLIO INSIGHTS
- Health score trend: [improving/stable/declining]
- Most common issue: [issue affecting most accounts]
- Estimated total revenue impact of fixes: [amount]
EXECUTION STEPS
Step 1: Discover All Accounts
meta_list_ad_accounts()google_ads_list_accounts()linkedin_list_ad_accounts()tiktok_get_advertiser_info()
Step 2: Pull Performance Data (last 30 days + previous 30 days)
Meta: meta_get_insights(account_id="...", date_preset="last_30d", level="account", fields=["spend","impressions","clicks","actions","action_values","purchase_roas","frequency","cpm","cpc","ctr"])
Google Ads: google_ads_run_gaql(customer_id="...", query="SELECT metrics.cost_micros, metrics.impressions, metrics.clicks, metrics.conversions, metrics.conversions_value, metrics.search_impression_share FROM customer WHERE segments.date DURING LAST_30_DAYS")
TikTok: tiktok_get_report(start_date="30_DAYS_AGO", end_date="TODAY", level="account")
LinkedIn: linkedin_get_analytics(account_id="...", start_date="30_DAYS_AGO", end_date="TODAY")
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.
- 11d ago First seen · 76 lines · 31 tokens per session scan A 98879bbd248d
account-health-scoring is a command published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 860 once invoked, about $0.0002 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.
Other commands, from other repositories
meta-audit
Full Meta Ads account audit, tailored to ecommerce or lead-gen automatically.
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.
specify
Create or update the feature specification from a natural language feature description.