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.
npx agentmods add commands/ad-superpowers/ad-superpowers-plugin/performance-forecastergit clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-pluginWhat 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 | $0.00053 | $0.01899 |
| Opus 5 | $0.00026 | $0.00949 |
| Sonnet 5 | $0.00011 | $0.00380 |
| Haiku 4.5 | $0.00005 | $0.00190 |
Grade A, and why
performance-forecaster 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 2d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Platforms: meta, google_ads, linkedin, tiktok, google_analytics 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.
AI-Powered Performance Forecasting
Generate performance forecasts with confidence intervals for [specify company_name] ([specify industry]).
Forecast Period: Next 90 days Monthly Budget: EUR10,000
Conditional: if meta_account_id Meta Account: [specify meta_account_id]
Conditional: if google_ads_customer_id Google Ads: [specify google_ads_customer_id]
Conditional: if ga4_property_id GA4 Property: [specify ga4_property_id]
OUTPUT FORMAT (CRITICAL - follow this EXACT structure)
EXECUTIVE SUMMARY
| Metric | Projection | 70% Confidence Range |
|---|---|---|
| Total Spend | [planned amount] | Fixed |
| Conversions | [expected] | [low] - [high] |
| Revenue | [expected] | [low] - [high] |
| Blended ROAS | [expected]x | [low]x - [high]x |
| Avg CPA | [expected] | [low] - [high] |
Forecast Confidence: [High/Medium/Low]
MONTHLY BREAKDOWN
| Month | Budget | Conversions (P/E/O) | ROAS (P/E/O) | Seasonality |
|---|---|---|---|---|
| M1 | [amount] | [low]/[exp]/[high] | [low]/[exp]/[high]x | [index] |
| M2 | [amount] | [low]/[exp]/[high] | [low]/[exp]/[high]x | [index] |
| M3 | [amount] | [low]/[exp]/[high] | [low]/[exp]/[high]x | [index] |
| P=Pessimistic (10th %ile), E=Expected (50th), O=Optimistic (90th) |
SCENARIO SUMMARY
| Scenario | Probability | Conversions | Revenue | ROAS |
|---|---|---|---|---|
| Downside | 15% | [low] | [low] | [low]x |
| Base Case | 60% | [expected] | [expected] | [exp]x |
| Upside | 25% | [high] | [high] | [high]x |
Expected Value: EV = (0.15 x Low) + (0.60 x Expected) + (0.25 x High)
KEY RISKS
| Risk | Probability | Impact | Mitigation |
|---|
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.
- 2d ago First seen · 158 lines · 53 tokens per session scan A 27c4b0a119de
performance-forecaster is a command published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 4d ago), licensed MIT. It adds 53 tokens to every session and 1,899 once invoked, about $0.0003 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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