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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add aaron-he-zhu/aaron-marketing-skills/plugin install aaron-marketingWrote 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/competitor-analysis)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/competitor-analysis"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/competitor-analysis/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/competitor-analysis"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/competitor-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00069 | $0.01805 |
| Opus 5 | $0.00034 | $0.00903 |
| Sonnet 5 | $0.00014 | $0.00361 |
| Haiku 4.5 | $0.00007 | $0.00180 |
Grade A, and why
competitor-analysis 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Analysis
Analyzes competitor SEO and GEO strategies to reveal repeatable wins, weak spots, and market gaps.
Quick Start
Analyze SEO strategy for [competitor URL]
Compare my site [URL] against [competitor 1], [competitor 2], [competitor 3]
Skill Contract
Expected output: a prioritized competitor brief plus the standard handoff summary for memory/research/.
- Reads: competitor URLs/domains, your own site metrics, business model, target audience, industry context, and any user-provided or tool data.
- Writes: a user-facing analysis and reusable summary.
- Promotes: durable competitor facts, keyword priorities, entity candidates, and pending strategy decisions to
memory/hot-cache.md,memory/open-loops.md, andmemory/research/. - Done when: 3-5 competitors are benchmarked across keywords, backlinks, and traffic share in one comparison table; each strength-to-learn and weakness-to-exploit cites evidence; and the deliverable closes with an Immediate / Short-term / Long-term plan.
- Primary next skill: content-gap-analysis when the competitive landscape is clear.
Handoff Summary
Emit the standard shape from skill-contract.md §Handoff Summary Format.
Data Sources
Optional integrations: ~~SEO tool, ~~analytics, ~~AI monitor. Without tools, ask for competitor URLs, your site metrics, and industry context. See CONNECTORS.md.
Zero-dependency competitor fetch (keyless): python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/firecrawl.py" scrape <competitor-url> returns the rendered page as LLM-ready markdown (JavaScript-heavy pages included), firecrawl.py map <competitor-domain> --limit 500 inventories their URL surface fast, and firecrawl.py search "<brand or topic>" --tbs qdr:m finds their fresh coverage — all on Firecrawl's keyless free tier (~1,000 credits/mo). The connector pre-flights the target's robots.txt locally and refuses on a Disallow per SECURITY.md §Scraping Boundaries. See scripts/connectors/README.md.
What ships with it
4 files 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 · 124 lines · 69 tokens per session scan A 905922fb722b
competitor-analysis 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 69 tokens to every session and 1,805 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 2026-09-03.
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.