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
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.
npx skills add ericosiu/ai-marketing-skills --skill deck-generatorgit clone --depth 1 https://github.com/ericosiu/ai-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/skills/ericosiu/ai-marketing-skills/deck-generator)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/deck-generator"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/deck-generator/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/deck-generator"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/deck-generator.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 | $0.00068 | $0.00901 |
| Opus 5.5 | $0.00027 | $0.00360 |
| Sonnet 5.5 | $0.00014 | $0.00180 |
| Haiku 4.5 | $0.00007 | $0.00090 |
Grade A, and why
deck-generator 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deck Generator
Generate complete presentations where every slide is an AI-generated image in a consistent visual style.
Quick Start
- Read the content spec (user provides slide content or a markdown file)
- Read
references/styles.mdto pick or customize a visual style - Run
scripts/generate-deck.pywith content + style
Workflow
Step 1: Content Spec
Accept slide content in any format. Normalize to this structure per slide:
- Title: Bold headline
- Body: Key points, stats, or narrative
- Visual cues: Icons, diagrams, layouts described in words
If user provides a markdown file with --- separators, parse each section as a slide.
If user provides a topic only, generate 10-14 slides following standard deck structures.
Step 2: Style Selection
Available style presets:
| Style | Description |
|---|---|
whiteboard |
Hand-drawn sketch on white. Black ink, orange accents. |
corporate |
Navy/white/gold. Clean sans-serif. Professional. |
minimalist |
Pure white, electric blue accent. Maximum negative space. |
dark-tech |
Near-black background, neon green. Terminal aesthetic. |
playful |
Bright pastels, rounded shapes. Modern startup vibe. |
editorial |
Black/white with red spot color. Magazine aesthetic. |
Default: whiteboard. User can specify any preset or describe a custom style.
Step 3: Generate
# Set your API key
export GEMINI_API_KEY="your-gemini-api-key"
# Run the generator
python3 scripts/generate-deck.py \
--content slides.json \
--style whiteboard \
--title "Deck Title" \
[--output-dir ./output] \
[--aspect 16:9]
The script:
- Generates each slide image via Imagen 4.0 API
- Saves all images to the output directory
- Optionally creates a Google Slides presentation (requires Google Slides API credentials)
- Returns paths to all generated images
Step 4: Review & Iterate
To regenerate individual slides:
python3 scripts/generate-deck.py \
--content slides.json \
--style whiteboard \
--slides 3,7 \
--output-dir ./output
What ships with it
3 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.
- 2mo ago First seen · 107 lines · 68 tokens per session scan A 736216cd5a58
deck-generator is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,784 stars, last pushed 5d ago), licensed MIT. It adds 68 tokens to every session and 901 once invoked, about $0.0003 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.
deck-generator compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| deck-generatorericosiu/ai-marketing-skills | Skill | 3.8k | 5d ago | MIT | A | 68 | 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 |