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/audience-mapperWrote 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/audience-mapper)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/audience-mapper"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/audience-mapper/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/audience-mapper"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/audience-mapper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00156 | $0.03401 |
| Opus 5 | $0.00078 | $0.01700 |
| Sonnet 5 | $0.00031 | $0.00680 |
| Haiku 4.5 | $0.00016 | $0.00340 |
Grade A, and why
audience-mapper 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Mapper
Maps who the brand is trying to reach and what community they belong to — the two halves of understanding an audience before any creator is selected. It runs in two modes against one shared inputs set:
audiencemode — the wide-angle read: demographic + psychographic profiles, a behavioral/media-diet map, a platform-priority matrix, content preferences, an influencer-affinity table, one or more named personas, and a must-have / nice-to-have / red-flag influencer-selection criteria set ready to hand to discovery.nichemode — the deep-dive: a community map (size, sub-niches, psychographics), a culture decode (language, norms, taboos), key-voice tiers, a content ecosystem, a Brand Fit Score (X/25) with a Strong/Moderate/Weak/Poor verdict, and a phased entry strategy with explicit red lines.
Both feed STAR creator/content scoring downstream, but this skill computes neither the Suitability/Trust/Appeal/Return dimension scores nor the SQS — it produces the audience and community facts that fit-scorer and creator-content-auditor later score against. Scope guard below.
Quick Start
Analyze the target audience for [brand/product/category] # audience mode
Build an audience profile for influencer targeting from this data: [data]
Research the [niche] community and identify opportunities for [brand] # niche mode
Deep-dive [subculture] — key voices, what content works, brand fit, cultural risks
If the mode is not named, infer it: a broad brand/product/category request → audience; a named community, subculture, or hashtag (e.g. "#BookTok", "van-life") → niche. State which mode you picked before running.
Skill Contract
Expected output: with sufficient dated evidence, in audience mode an audience analysis (demographics + psychographics, behavioral map, platform-priority matrix, content preferences, influencer-affinity table, ≥1 persona, and selection criteria); in niche mode a niche dossier (community map, culture decode, key-voice candidate tiers, Brand Fit Score X/25 + verdict, phased entry strategy, red lines). Without sufficient evidence, return hypotheses only as explicitly hypothetical, an exact research/collection plan, and NEEDS_INPUT; do not score, tier, promote, or hand off hypotheses as facts.
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 80840cb62bcd
- 13d ago First seen · 116 lines · 156 tokens per session scan A 421e2aa63685
audience-mapper 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 156 tokens to every session and 3,401 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.