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/inbox-placement-monitorWrote 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/inbox-placement-monitor)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/inbox-placement-monitor"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/inbox-placement-monitor/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/inbox-placement-monitor"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/inbox-placement-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 71 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 | $0.00166 | $0.03427 |
| Opus 5 | $0.00083 | $0.01714 |
| Sonnet 5 | $0.00033 | $0.00685 |
| Haiku 4.5 | $0.00017 | $0.00343 |
Grade A, and why
inbox-placement-monitor 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inbox Placement Monitor
Post-send placement telemetry: where mail actually landed per mailbox provider (inbox vs spam vs promotions from a seed-list test), the domain/IP reputation trend from Gmail Postmaster Tools and Microsoft SNDS, and the send-over-send delta with named regressions — delivered as a per-provider placement read plus a reusable SEND S (Sender-integrity / Deliverability) placement snapshot, with each number labeled Measured / User-provided / Estimated. This is the after half of SEND-S: deliverability-qa verifies the signal before a send (auth pre-flight, static reputation, one placement test); this skill tracks what happened after it and how reputation moves across sends. Scope guard: this skill tracks post-send placement + reputation trend and hands off a SEND-S placement snapshot; it does NOT run the S1 SPF/DKIM/DMARC auth pre-flight (that is deliverability-qa) and does NOT compute the profile-weighted EQS or enforce the S1/S2/N1/D1 vetoes (that is email-quality-auditor). Build/trend the telemetry here; let the gate render the verdict.
Quick Start
Track inbox placement for [sending domain] after my last send. Here is my seed-list test (inbox/spam/promotions per provider) and my Gmail Postmaster + Microsoft SNDS export: [paste/path].
Trend my sender reputation over the last [N] sends and flag any placement regression. Profile: [promotional / retention / cold-outbound / newsletter]. Prior baseline: [paste/path].
Did placement drop after my last campaign? Compare this seed test against the prior one and tell me which provider regressed and by how much.
Skill Contract
Expected output: a per-provider placement read (inbox / spam / promotions %, per Gmail, Outlook/Microsoft, Yahoo, Apple) from the seed-list test; a domain/IP reputation trend from Gmail Postmaster Tools and Microsoft SNDS (high/medium/low/bad, complaint-rate curve, IP status); a send-over-send delta naming each regression with its number; the SEND-S placement sub-item read (inbox-placement ≥ threshold, spam-complaint < 0.1%) with the typed profile named; and the standard handoff summary. Every metric is labeled Measured / User-provided / Estimated — never invent a placement number; if a provider's export is missing, mark that provider NEEDS_INPUT.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · +2 lines b800ed91bee0
- 13d ago First seen · 88 lines · 166 tokens per session scan A f95935743643
inbox-placement-monitor 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 166 tokens to every session and 3,427 once invoked, about $0.0008 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.