aaron-marketing-skills is a collection of 120 AI-agent skills covering marketing work such as brand narrative, search optimization, social media, email, advertising, influencer campaigns, and launches. Marketers and agent users can install it as a plugin, use its portable skills, or run its described bot team. The catalogue entries are components of this marketing workflow.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skillsnpx agentmods add skills/aaron-he-zhu/aaron-marketing-skills/attribution-reconcilerWrote 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/aaron-he-zhu/aaron-marketing-skills/attribution-reconciler)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/attribution-reconciler"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/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/aaron-he-zhu/aaron-marketing-skills/attribution-reconciler"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/attribution-reconciler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 84 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00150 | $0.02726 |
| Opus 5 | $0.00075 | $0.01363 |
| Sonnet 5 | $0.00030 | $0.00545 |
| Haiku 4.5 | $0.00015 | $0.00273 |
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 10d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attribution Reconciler
Based on the ROAS dimension R (attribution integrity) in the ROAS Benchmark. This is the standing de-dup / incrementality workbook: it reconciles platform-reported conversions against the GA4/ecommerce order-ID truth set on a recurring cadence. It delegates all ratio/ROAS math to roi-calculator and does not re-run the R2 veto — ad-account-auditor judges R2 once, point-in-time. This workbook just keeps the truth set clean between audits. Upstream, conversion-signal-qa is the pre-launch instrumentation pass that makes the signal trustworthy and only gates that a dedup rule exists; this skill is the recurring reconciliation that runs on that signal — match, de-dup, quantify, read incrementality.
The single rule: the truth set is the order IDs from GA4/ecommerce, never any platform's reported-conversion count. This workbook reconciles paid channels only — decomposing GA4 direct traffic and estimating organic dark-social share attribution belongs to dark-social-attributor.
Quick Start
Reconcile my paid conversions for May. Truth set is this GA4 order-ID export. Here are the Meta and Google conversion exports. Find the double-counting.
Build the monthly attribution workbook: normalize Meta's 7-day-click window and Google's 30-day window to a common window, convert currencies, then show de-duped conversions per platform against my Shopify order export.
I ran a geo holdout for two weeks. Here's the test-region and control-region order export plus the platform spend. Read the incrementality and compare it to last-click.
Skill Contract
- Expected output: a reconciliation workbook that maps every platform-reported conversion to (or away from) an order in the truth set, a de-duped conversion count per platform, a normalized-window/currency view, an attribution-model comparison table, and an incrementality read if a holdout exists.
- Reads: the GA4/ecommerce order-ID export (truth set), each platform's conversion export (reported conversions with claimed order IDs/timestamps/windows), the stated attribution window per platform, currency per export, and any geo/holdout test export (test vs control orders + spend). The ROAS profile (
direct-response|prospecting|incremental-profit) is context only. - Writes: a reconciliation workbook at
memory/ad/attribution-reconciler/YYYY-MM-DD-<topic>.md— match table, de-duped counts, normalized view, model-comparison table, incrementality read, and a handoff summary. - Promotes: the de-duped conversion count, the double-count rate, and the incrementality result (if any) to
memory/hot-cache.md. Unresolved gaps (orders with no platform claim, or platform claims with no matching order) tomemory/open-loops.md. - Done when: every platform conversion is reconciled to the order-ID truth set (matched / double-counted / unmatched), windows and currency are normalized to a common basis, at least one attribution-model comparison is shown, incrementality is read where a holdout exists (or marked N/A), and the ratio/ROAS math is handed to
roi-calculatorrather than computed here. - Primary next skill: roi-calculator.
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.
- 10d ago Changed · +3 lines cdf1ff00ec40
- 13d ago First seen · 98 lines · 150 tokens per session scan A ccae120ee4f6
attribution-reconciler is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed today), licensed Apache-2.0. It adds 150 tokens to every session and 2,726 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
geo-visibility-check
One-shot GEO audit: does your brand appear in Claude, ChatGPT, and Gemini answers for the buyer questions that matter? Runs a prompt panel through the engines with citation tracing and reports per-prompt verdicts, who wins instead, and which sources the answers come from.
geo-optimizer-skill
Run geo audit first. It scores the site 0–100 across 8 categories and generates a prioritized action list.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
content-scoring
Score content against the 10 GEO criteria with evidence and prioritized fixes. Use when users ask to score, rate, evaluate, or estimate ranking strength.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
schema-generator
Generate JSON-LD schema markup for pages and content types with an implementation checklist. Use when users ask for schema, structured data, rich snippets, or markup.