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/community-launch-runnerWrote 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/community-launch-runner)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/community-launch-runner"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/community-launch-runner/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/community-launch-runner"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/community-launch-runner.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 69 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 | $0.00193 | $0.02905 |
| Opus 5 | $0.00097 | $0.01452 |
| Sonnet 5 | $0.00039 | $0.00581 |
| Haiku 4.5 | $0.00019 | $0.00291 |
Grade A, and why
community-launch-runner 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Community Launch Runner
Executes the community and directory lane of a launch — per-platform submission packages (Product Hunt, Show HN, subreddits, tiered directories, regional channels including Chinese communities) built under each platform's published rules. In the RAMP loop this is a Mobilize-phase execution skill: it feeds the M (Momentum) sub-items channel mix fits tier & use-case and platform-rule compliance per channel, and it is the execution surface the M1 veto (platform manipulation / policy) judges — launch-readiness-auditor scores that; this skill never computes the RAMP profile result. It works one lever — community submission execution — and hands off.
Scope guard: this skill prepares community/directory submissions only. It does not run paid amplification, creator campaigns, media relations, the launch-day runbook, telemetry, or canonical launch state. T-0 observations become authorized idempotent launch proposals through registry-events.py; launch-registry resolves them. Ongoing community presence/warmup belongs to the social discipline.
Quick Start
Prepare a Product Hunt + Show HN submission package for [product]. Launch date: [date]. Audience: [who].
Build the community launch plan for [product] — subreddits, directories, and Chinese channels. Region: [global / CN / both].
Check my submission drafts against each platform's rules before T-0 — here are the drafts and the channel list.
Skill Contract
Expected output: per-platform submission packages (Product Hunt tagline / gallery / first-comment skeleton, factual Show HN title + text, per-subreddit posts with a self-promotion rules table, tiered directory waves, regional-channel posts), a red-line check across the whole plan, T-0 submission-status lines routed to the registry proposal protocol, and the standard handoff summary.
- Reads: the launch dossier facts; the current frozen manifest version/hash and matching SHIP verdict; the message house and per-channel asset kit; target platforms, region, and audience; each platform's current official rules; and early launch-window telemetry.
- Writes: submission packages + a reusable summary to
memory/launch/community-launch-runner/(its WARM path, after permission); dated T-0 submission-status lines submitted as proposal events tomemory/events/launches.ndjsonvia an authorizedoperation: proposerequest toregistry-events.py(the hot path — launch-registry resolves each proposal individually in offset order; this skill never writes the dossier or calendar directly). It does not write HOT automatically. - Done when: every selected platform has a complete package bound to the current manifest hash and current official rules; the red-line check passes; every attempted submission has its own action intent and provider/URL receipt; and missing/partial/unknown receipts remain open rather than being labeled submitted/live.
- Primary next skill: launch-monitor — the T-0→T+30 telemetry read of what these submissions produce.
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.
- 9d ago First seen · 88 lines · 193 tokens per session scan A 00ddaf40c5f1
community-launch-runner 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 193 tokens to every session and 2,905 once invoked, about $0.0010 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.