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
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.
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skillsWrote 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/commands/aaron-he-zhu/aaron-marketing-skills/launch)<a href="https://agentmods.dev/commands/aaron-he-zhu/aaron-marketing-skills/launch"><img src="https://agentmods.dev/badge/commands/aaron-he-zhu/aaron-marketing-skills/launch/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/commands/aaron-he-zhu/aaron-marketing-skills/launch"><img src="https://agentmods.dev/badge/commands/aaron-he-zhu/aaron-marketing-skills/launch.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 | $0.00064 | $0.01213 |
| Opus 5 | $0.00032 | $0.00607 |
| Sonnet 5 | $0.00013 | $0.00243 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
launch 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 12d 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch Command
Run the product-launch lifecycle along the RAMP loop (Research → Assemble → Mobilize → Prove). Skills operate from the user's own plan, project memory, and keyless public telemetry — keyed launch platforms and commercial ASO suites are never required. The auditor selects one typed lifecycle read: preflight, execution, or outcome; it links but never averages results across time horizons.
Route
Infer the RAMP-loop phase from the goal (or honor --phase) and route to the matching skill:
- Research — positioning-mapper (Dunford-style canvas: alternatives, unique attributes, beachhead), launch-tier-planner (tier/type + risk register + kill criteria), launch-window-planner (dates, competitor calendar, embargo windows), early-access-designer (waitlist→GA stage ladder + graduation criteria); record decided dates/stages via launch-registry (
memory/launch-registry/) - Assemble — message-house-builder (tagline/pillars/PR-FAQ spine; unresolved claims become
operation: proposeevents), launch-asset-packager (tier-scoped manifest: press kit, store listing specs, technical go-live items), pricing-packaging-planner (tiers, launch offers, guarantees), sales-enablement-kit (battle cards, talk track — sales-led only); reuse landing-optimizer for the launch page UX and technical-seo-checker for the go-live pass - Mobilize — launch-readiness-auditor (typed preflight profile + T-1 go/no-go; R1 judged against launch projection, A1 against claims projection), launch-day-conductor (hour-blocked runbook, requires SHIP plus separate execution approval), community-launch-runner (platform-rule-bound submissions), press-media-relations (media/embargo drafts; outreach remains separate)
- Prove — launch-monitor (T-0→T+30 telemetry via
hn.py/producthunt.py/appstore.py/gdelt.py, spike-vs-sustain), launch-feedback-synthesizer (theme triage + compliant social proof), launch-retro-analyzer (D1/W1/M1 actual-vs-target + 5-Whys), momentum-planner (anti second-week cliff, next moment); reuse roi-calculator / report-generator / performance-analyzer
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.
- 12d ago First seen · 32 lines · 64 tokens per session scan A 2bd05b540f62
launch is a command published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed today), licensed Apache-2.0. It adds 64 tokens to every session and 1,213 once invoked, about $0.0003 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 commands, from other repositories
geo:loop
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geo:optimize
Optimize a local content file for GEO without full audit.
geo
Full GEO optimization pipeline - analyze, rank, rewrite, and generate schema for any URL or content.
geo:audit
Analyze content for GEO optimization opportunities without making changes.
geo:batch
Process multiple content files in a folder.
geo:compete
Analyze competitive landscape for a query or topic.