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/launch-window-plannerWrote 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/launch-window-planner)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/launch-window-planner"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/launch-window-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/aaron-he-zhu/aaron-marketing-skills/launch-window-planner"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/launch-window-planner.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 72 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 | $0.00144 | $0.02790 |
| Opus 5 | $0.00072 | $0.01395 |
| Sonnet 5 | $0.00029 | $0.00558 |
| Haiku 4.5 | $0.00014 | $0.00279 |
Grade A, and why
launch-window-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 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch Window Planner
Picks when to launch — the timing lever of the RAMP loop Research phase. It scans industry-event and conference cycles, maps the competitor launch calendar, pads for store-review latency, chooses a launch-week vs rolling format, and defines the embargo window (lift moment + timezone). It feeds the RAMP-R timing sub-item ("timing window chosen deliberately — event cycles, competitor calendar, review-latency buffers") and the RAMP-M embargo-coordination sub-item ("embargo & partner commitments coordinated against one authoritative date/stage") per ramp-benchmark.md. It works one lever — timing — and hands off.
The window this skill recommends is a proposal, not the record: date, stage, and embargo facts become authoritative only when launch-registry records them. This skill submits candidates and never writes the registry directly.
Scope guard: this skill picks the window only. It does not judge whether a cultural moment or trend is worth riding (that is trend-spotter), run the launch day itself (launch-day-conductor owns the hour-blocked runbook), declare the launch tier or own the risk register (launch-tier-planner), write the canonical date/stage/embargo record (launch-registry is the sole writer of memory/launch-registry/), or compute the RAMP profile result (launch-readiness-auditor). It works one lever and hands off.
Quick Start
Pick a launch window for [product] in [quarter]. Constraints: [team availability / store-review submission / partner commitments].
Map the competitor launch calendar and industry events around [candidate date] — should we move?
Define the embargo window for [launch]: lift moment, timezone, and who is committed to it.
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 · 91 lines · 144 tokens per session scan A c72365a213db
launch-window-planner is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed yesterday), licensed Apache-2.0. It adds 144 tokens to every session and 2,790 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 2026-09-03.
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.