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-evalWrote 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-eval)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/content-eval"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/content-eval/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-eval"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/content-eval.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 | $0.00000 | $0.01948 |
| Opus 5 | $0.00000 | $0.00974 |
| Sonnet 5 | $0.00000 | $0.00390 |
| Haiku 4.5 | $0.00000 | $0.00195 |
Grade A, and why
content-eval 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 11d 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 — 217 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.
name: content-eval description: >- Generate and score content ideas using an expert panel. Pulls from podcast transcripts, meeting notes, competitor analysis, and trending topics to produce a ranked content menu with production schedule. Use when asked to: "content eval", "score content ideas", "weekly content menu", "what should I film", "content ideas", "rate these video ideas".
Content Eval
Content ideation + expert panel scoring pipeline. Ingests raw material, generates ideas across your messaging pillars, scores them via a 7-expert panel, and outputs a ranked list with production schedule.
Step 1: Gather Raw Material
Collect signal from all available sources. Skip any source that's unavailable.
Podcast episodes
- Read recent episodes from your podcast transcript directory (last 7 days)
- Extract: topics covered, guest insights, audience questions, contrarian takes
- Note episode titles for dedup against new ideas
Meeting notes
- Check your meeting notes directory for recent notes
- Extract: client questions, recurring themes, interesting moments, pain points
- Focus on what your target buyers are actually asking about
Sales call insights
- Check your call recording platform data for recent calls
- Extract: objection patterns, recurring questions, competitor mentions
- Note what prospects are confused about or struggling with
Trending topics
- Note any topics the user mentions directly
- Check competitor scan results (Step 2) for trending formats/topics
- Look for news hooks or industry shifts you could react to
What ships with it
6 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.
- 11d ago First seen · 217 lines · 0 tokens per session scan A 5802d4526f85
content-eval is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,517 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,948 tokens. 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.
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