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 skills add Ad-Superpowers/ad-superpowers-plugin --skill meta-lookalike-strategy-plannergit 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/skills/ad-superpowers/ad-superpowers-plugin/meta-lookalike-strategy-planner)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/meta-lookalike-strategy-planner"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/meta-lookalike-strategy-planner/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/skills/ad-superpowers/ad-superpowers-plugin/meta-lookalike-strategy-planner"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/meta-lookalike-strategy-planner.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 | $0.00099 | $0.02904 |
| Opus 5.5 | $0.00040 | $0.01162 |
| Sonnet 5.5 | $0.00020 | $0.00581 |
| Haiku 4.5 | $0.00010 | $0.00290 |
Grade A, and why
meta-lookalike-strategy-planner 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 — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lookalike Strategy Planner
Overview
This skill helps create high-quality Lookalike Audiences by selecting the right source audiences, choosing optimal percentages, and applying advanced strategies for maximum performance.
2026 Context: Meta increasingly recommends Advantage+ Audience (broad targeting with audience suggestions) over manual Lookalikes for most campaign types. Lookalikes remain valuable as audience suggestions within Advantage+ campaigns, and for specific use cases where you need more targeting control. For Advantage+ Sales Campaigns (ASC), upload your Customer Match list as an audience signal instead of creating standalone LALs. Note: "Advantage+ Shopping Campaigns" branding was deprecated in API v25.0 — the unified product is now called Advantage+ Sales Campaigns.
Lookalike Fundamentals
How Meta Lookalikes Work
┌─────────────────────────────────────────────────────────────────┐
│ LOOKALIKE CREATION PROCESS │
│ │
│ 1. SOURCE AUDIENCE (your data) │
│ └── Minimum 100 people (1,000+ recommended) │
│ │
│ 2. META's ALGORITHM analyzes: │
│ ├── Demographic data │
│ ├── Interests & behavior │
│ ├── Engagement patterns │
│ ├── Purchase behavior │
│ └── Cross-platform activity │
│ │
│ 3. LOOKALIKE OUTPUT │
│ └── Top X% of people most similar to source │
│ │
│ Important: Source quality > Source size │
└─────────────────────────────────────────────────────────────────┘
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 First seen · 353 lines · 99 tokens per session scan A 3ab14c8571bc
meta-lookalike-strategy-planner is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 99 tokens to every session and 2,904 once invoked, about $0.0004 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 2026-09-11.
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