AI Marketing Skills is a collection of open-source workflows that help AI coding agents handle marketing and sales work, including growth experiments, pipeline management, content operations, outbound outreach, SEO, and finance analysis. It is intended for marketing and sales teams that want reusable agent-driven processes. The catalogue entries package these workflows as skills for compatible coding agents.
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/ericosiu/ai-marketing-skillsnpx agentmods add skills/ericosiu/ai-marketing-skills/content-opsWrote 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/ericosiu/ai-marketing-skills/content-ops)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/content-ops"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/content-ops/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/ericosiu/ai-marketing-skills/content-ops"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/content-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00154 | $0.02072 |
| Opus 5.5 | $0.00062 | $0.00829 |
| Sonnet 5.5 | $0.00031 | $0.00414 |
| Haiku 4.5 | $0.00015 | $0.00207 |
Grade A, and why
expert-panel 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 2mo 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (runs on skill start)
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true
# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true
Privacy: This skill logs usage locally to
~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. Seetelemetry/README.md.
Expert Panel
General-purpose scoring and iterative improvement engine. Auto-assembles the right experts for whatever is being evaluated, scores it, and loops until 90+.
Step 1: Intake — Understand What's Being Scored
Collect or infer from context:
- Content/artifact — The thing(s) to score (paste, file path, or URL)
- Content type — Copy, sequence, landing page, strategy, title, chart, candidate eval, etc.
- Offer context — What's being sold/promoted? To whom? What domain/industry?
- Variants — Are there multiple versions to compare? (A/B/C)
- Source skill — Is this output from another skill? (e.g., cold-outbound-optimizer) If yes, note the source for feedback-to-source routing in Step 6.
If context is obvious from the conversation, don't ask — just proceed.
Step 2: Auto-Assemble the Expert Panel
Build a panel of 7–10 experts tailored to the content type and domain.
Assembly rules
-
Start with content-type experts. Read
experts/directory for pre-built panels matching the content type. If an exact match exists (e.g.,experts/linkedin.mdfor a LinkedIn post), use it as the base. -
Add domain/offer experts. Based on the offer context, add 1–3 experts who understand the specific industry or domain. Examples:
- Scoring bakery marketing → add Food & Beverage Marketing Expert
- Scoring SaaS landing page → add SaaS Conversion Expert
- Scoring recruiting outreach → add Agency Recruiter + Talent Market Expert
- Scoring medical device copy → add Healthcare Compliance Expert
What ships with it
25 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.
- .env.example 1.1 KB
- config/feeds.example.json 146 B
- experts/humanizer.md 7.3 KB
- experts/instagram.md 2.5 KB
- experts/linkedin.md 1.9 KB
- experts/newsletter.md 1.6 KB
- experts/podcast-quotes.md 2.9 KB
- experts/recruiting.md 1.5 KB
- experts/seo-strategy.md 1.4 KB
- experts/x-articles.md 2.4 KB
- experts/youtube-shorts.md 1.6 KB
- README.md 6.5 KB
- references/expert-assembly.md 3.2 KB
- references/patterns.md 503 B
- requirements.txt 370 B
- scoring-rubrics/content-quality.md 823 B
- scoring-rubrics/conversion-quality.md 1.1 KB
- scoring-rubrics/evaluation-quality.md 1.0 KB
- scoring-rubrics/strategic-quality.md 452 B
- scoring-rubrics/visual-quality.md 980 B
- scripts/content-quality-gate.py 8.8 KB runs code
- scripts/content-quality-scorer.py 18 KB runs code
- scripts/content-transform.py 27 KB runs code
- scripts/editorial-brain.py 18 KB runs code
- scripts/quote-mining-engine.py 15 KB runs code
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.
- 2mo ago First seen · 247 lines · 154 tokens per session scan A fe56ba3adc38
expert-panel is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,784 stars, last pushed 5d ago), licensed MIT. It adds 154 tokens to every session and 2,072 once invoked, about $0.0006 per session on Opus 5.5. 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.
expert-panel compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| expert-panelericosiu/ai-marketing-skills | Skill | 3.8k | 5d ago | MIT | A | 154 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 2d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |