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 attribution-reconcilergit 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/attribution-reconciler)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/attribution-reconciler"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/attribution-reconciler/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/attribution-reconciler"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/attribution-reconciler.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 | $0.00103 | $0.05057 |
| Opus 5 | $0.00051 | $0.02528 |
| Sonnet 5 | $0.00021 | $0.01011 |
| Haiku 4.5 | $0.00010 | $0.00506 |
Grade A, and why
attribution-reconciler 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 9d 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 — 586 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Platform Attribution Reconciler
Purpose
Help advertisers understand and reconcile the conversion discrepancies they see between Meta, Google Ads, GA4, TikTok, and LinkedIn. This is the #1 pain point across all advertising platforms - different platforms report different numbers for the same conversions.
When to Use This Skill
Invoke when user mentions:
- Discrepancies: "Why do Meta and GA4 show different numbers?"
- Trust questions: "Which platform's data should I believe?"
- Budget decisions: "How do I allocate budget across channels?"
- Reconciliation: "How do I reconcile attribution across channels?"
- Specific gaps: "What's causing the 30% discrepancy I'm seeing?"
Required Tools
Use these MCP tools to pull live data when diagnosing attribution discrepancies:
| Tool | Purpose |
|---|---|
ga4_run_report |
Pull GA4 Key Events (conversions) as neutral baseline |
meta_query |
Pull Meta campaign conversions by attribution window |
google_ads_run_gaql |
Pull Google Ads conversion data for comparison |
tiktok_get_report |
Pull TikTok conversion data |
linkedin_get_analytics |
Pull LinkedIn conversion data |
Recommended diagnostic sequence:
1. ga4_run_report(property_id="...", start_date="2026-03-08", end_date="2026-04-05", metrics=["keyEvents"], dimensions=["date"])
2. meta_get_insights(account_id="act_...", level="campaign", date_preset="last_28d", fields=["spend","actions","impressions"])
3. google_ads_run_gaql(customer_id="...", query="SELECT campaign.name, metrics.conversions, metrics.cost_micros FROM campaign WHERE segments.date DURING LAST_28_DAYS")
Quick Reference: Expected Discrepancies
These discrepancy ranges are normal and don't necessarily indicate a problem:
| Platform Comparison | Expected Difference | Primary Cause |
|---|---|---|
| Meta vs GA4 | Meta +15-30% higher | View-through + modeled conversions |
| Google Ads vs GA4 | Google +10-25% higher | Enhanced Conversions + modeling |
| TikTok vs GA4 | TikTok +20-40% higher | VTA attribution (30% of conversions) |
| LinkedIn vs GA4 | LinkedIn +15-35% higher | Long B2B cycles, cross-device |
| GA4 vs All | GA4 -18-35% lower | Cookie blocking, consent mode |
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.
- 9d ago First seen · 586 lines · 103 tokens per session scan A fb54a2375290
attribution-reconciler is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 11d ago), licensed MIT. It adds 103 tokens to every session and 5,057 once invoked, about $0.0005 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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