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 superamped/ai-marketing-skills --skill ad-anglesgit clone --depth 1 https://github.com/superamped/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/superamped/ai-marketing-skills/ad-angles)<a href="https://agentmods.dev/skills/superamped/ai-marketing-skills/ad-angles"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/ad-angles/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/superamped/ai-marketing-skills/ad-angles"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/ad-angles.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 | $0.00049 | $0.02607 |
| Opus 5 | $0.00024 | $0.01303 |
| Sonnet 5 | $0.00010 | $0.00521 |
| Haiku 4.5 | $0.00005 | $0.00261 |
Grade A, and why
ad-angles 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Angle Brainstorm
Usage
Use when brainstorming ad ideas before launching a new campaign, generating headline variations for A/B testing, exploring new messaging angles for a stale campaign, or preparing creative briefs for ad production.
Process
Step 1: Gather Inputs
Ask the user for:
- Product description — what it does, key features, core benefit
- Target audience — who they're targeting (role, industry, business type & stage)
- Platform — where the ads will run (Reddit, LinkedIn, Meta, Google, X/Twitter, etc.). This affects dimensions, tone, and output fields.
- Competitor names (optional) — for comparison-based angles
- Proof points (optional) — metrics, testimonials, customer count, case studies
- Number of concepts (optional) — default: 10
- Constraints (optional) — brand tone, things to avoid, compliance requirements
If the user has multiple products, ask which product this campaign is for. Don't assume — different products have different audiences, price points, and angles.
Step 1b: Diagnose Market Awareness & Sophistication
Before generating angles, diagnose where the target market sits on two axes. This determines which angle types will work and which will fail.
Market Awareness (5 stages):
| Stage | Prospect Knows | Headline Strategy | Angle Types That Work |
|---|---|---|---|
| 1 — Most Aware | Product + desire + ready to buy | Name product + price/offer | Proof, Solution (direct) |
| 2 — Product Aware | Product exists, not yet convinced | Reinforce desire, show proof, new mechanism | Proof, Solution, Comparison |
| 3 — Solution Aware | Wants the outcome, doesn't know your product | Name the desire/outcome first, then introduce product | Solution (benefit), Curiosity |
| 4 — Problem Aware | Feels the pain, doesn't know solutions exist | Name the pain, dramatize it, present product as answer | Problem, Curiosity |
| 5 — Unaware | Doesn't recognize the problem or won't admit it | Identification headline — echo an emotion or attitude, not a product claim | Curiosity (identification) |
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 · 257 lines · 49 tokens per session scan A 483871a30698
ad-angles is a skill published in the GitHub repository superamped/ai-marketing-skills (67 stars, last pushed 24d ago), licensed MIT. It adds 49 tokens to every session and 2,607 once invoked, about $0.0002 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
ad-creative
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative…
magazine-web-ppt
For marketing and gtm work: bind launches, campaigns, events, and brand plans to growth and pipeline outcomes. Built around the core query "annual-marketing-plan", with GTM strategy lead judgment, buyer-ready proof, and this outcome: approve launch plan, campaign budget, or GTM motion.
html-ppt-zhangzara-coral
OpenDesign's community-growth campaign across GitHub, Discord, and X: the loops, the content calendar, and the pipeline math. Built as a decision-grade marketing & GTM deck for growth team, community lead.
muapi-workflow
Build, run, and visualize multi-step AI generation workflows. The AI architect translates natural language descriptions into connected node graphs — chain image generation, video creation, enhancement, and editing into automated pipelines.
muapi-media-editing
Edit and enhance images and videos with AI via muapi.ai — prompt-based editing, upscaling, background removal, face swap, lipsync, video effects, and more.
muapi-media-generation
Generate AI images, videos, music, and audio from the terminal via muapi.ai — supports 100+ models including Flux, Midjourney v7, Kling 3.0, Veo3, and Suno V5.