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-attribution-window-advisorgit 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-attribution-window-advisor)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/meta-attribution-window-advisor"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/meta-attribution-window-advisor/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-attribution-window-advisor"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/meta-attribution-window-advisor.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 | $0.00098 | $0.03103 |
| Opus 5.5 | $0.00039 | $0.01241 |
| Sonnet 5.5 | $0.00020 | $0.00621 |
| Haiku 4.5 | $0.00010 | $0.00310 |
Grade A, and why
meta-attribution-window-advisor 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 — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attribution Window Advisor
Advisor for choosing the optimal Meta Ads attribution configuration based on product, buying cycle, and goals.
January 2026 Attribution Change: Meta removed 7-day view and 28-day view attribution windows. The new default is 7-day click + 1-day view. Unique reach counts now have a 13-month retention limit. If your reports compare to periods before January 2026, expect metric discontinuities for view-based attribution.
Attribution Basics
What Is Attribution?
Attribution determines which ads receive credit for conversions and how long after interaction a conversion is still attributed.
Attribution Types:
├── Click-Through: Conversion after clicking an ad
├── View-Through: Conversion after seeing an ad (no click)
└── Engaged-View: Conversion after 10+ sec video view
Available Windows (Current)
| Window Type | Options | Default |
|---|---|---|
| Click-Through | 1-day, 7-day | 7-day |
| View-Through | 1-day, None | 1-day |
| Engaged-View | 1-day, None | 1-day (if VT=1-day) |
Removed (no longer available):
- 28-day click
- 28-day view
- 7-day view
Attribution Window Selection
Decision Tree
WHAT IS YOUR PRODUCT/SERVICE?
│
├─► Impulse Purchases (<€50, quick decision)
│ └─► 1-day click, 1-day view
│ └─► Examples: Fast fashion, accessories, digital products
│
├─► Standard E-commerce (€50-200)
│ └─► 7-day click, 1-day view (DEFAULT)
│ └─► Examples: Clothing, electronics, home goods
│
├─► Considered Purchases (€200+, research phase)
│ └─► 7-day click, 1-day view
│ └─► Examples: Furniture, luxury items, tech
│
├─► Lead Generation (B2B/Services)
│ └─► 7-day click, 1-day view
│ └─► Long sales cycle = 7-day click minimum
│
└─► High-Ticket / Long Sales Cycle
└─► 7-day click, 1-day view
└─► Supplement with external attribution tools
Recommendation Matrix
| Scenario | Click Window | View Window | Rationale |
|---|---|---|---|
| Default (most cases) | 7-day | 1-day | Balanced data collection |
| Impulse/Flash sales | 1-day | 1-day | Short decision cycle |
| Brand awareness | 7-day | 1-day | Capture delayed action |
| Retargeting | 7-day | 1-day | Users already familiar |
| Strict ROAS targets | 7-day | None | Minimize inflated metrics |
| High-consideration B2B | 7-day | 1-day | Research-heavy buying |
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 · 442 lines · 98 tokens per session scan A 54195cde9322
meta-attribution-window-advisor is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 98 tokens to every session and 3,103 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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