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 swan-gtm/gtm-skills --skill newsletter-formatgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/newsletter-format)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/newsletter-format"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/newsletter-format/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/swan-gtm/gtm-skills/newsletter-format"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/newsletter-format.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 | $0.00179 | $0.02919 |
| Opus 5 | $0.00089 | $0.01460 |
| Sonnet 5 | $0.00036 | $0.00584 |
| Haiku 4.5 | $0.00018 | $0.00292 |
Grade A, and why
newsletter-format 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 — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newsletter Format Designer
What This Skill Does
Walks the user through the $10,000 Newsletter Format — a proven 8-step system for building an analysis-style newsletter that positions you as the go-to expert in your niche and consistently attracts high-ticket clients.
This framework works even with:
- A small list (under 1,000 subscribers)
- No direct promotion of services
- Inconsistent publishing history
The 8 Steps:
- Choose Your Signature Asset
- Create Your Breakdown Framework
- Create Your Newsletter Value Proposition
- Name Your Newsletter
- Find Examples to Analyze
- Create Your Analysis Email Template
- Write Your Weekly Newsletter
- Build Your Content Calendar & Systems
Before Starting — Collect These Inputs
Ask the user for these 3 things upfront before running any step:
- Type of service provider — What do they do? (e.g. copywriter, designer, coach, consultant)
- Ideal client — Who do they want to attract with this newsletter?
- Main deliverables — What are the 2-4 main assets/outputs they create for clients?
Step 1: Choose Your Signature Asset (~10-15 min)
The foundation of the entire newsletter. The right asset makes content creation easy, positions them as the expert, and attracts exactly the clients they want.
Run this prompt with their inputs filled in:
Context:
I'm implementing the $10,000 Newsletter Format, which involves writing weekly newsletters that analyze examples of successful assets in my niche.
The goal is to demonstrate my expertise and attract high-ticket clients by:
- Breaking down what works and why
- Identifying potential improvements and explaining why they matter
- Showing deep understanding of my craft through detailed analysis
- Building authority without directly promoting my services
About me: I am a [TYPE OF SERVICE PROVIDER] who wants to identify the best type of asset to analyze in my newsletter. My target clients are [IDEAL CLIENT DESCRIPTION].
My service/offer: [NAME AND BRIEF DESCRIPTION OF MAIN SERVICE]
My deliverables: Here are the main assets/deliverables I create for my clients as part of this service:
1. [DELIVERABLE 1]
2. [DELIVERABLE 2]
3. [DELIVERABLE 3]
Based on the information above, please help me identify the 5 best types of assets I could analyze in my weekly newsletter.
For each asset, please provide:
1. Potential asset you could break down
2. Rationale for writing about this type of asset
3. How easy it is to find examples (score from 1-10)
4. 2-3 specific ways to consistently find examples of this asset in the wild
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 · 369 lines · 179 tokens per session scan A 22b2c75bfa8d
newsletter-format is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 179 tokens to every session and 2,919 once invoked, about $0.0009 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
teaser-block-writer
Writes "read next," "related articles," or "previously on" teaser blocks for web pages, newsletters, or app interfaces — short, click-worthy summaries that drive readers from one piece of content to another.
email-marketing
Email marketing automation - campaign creation, sequence building, A/B testing, deliverability optimization, and analytics.
Mailchimp Automation
Automate Mailchimp email marketing campaigns, audience management, automations, and analytics.
fastmail-imap-newsletter-digest
Read emails from Fastmail via IMAP and process them (e.g., newsletter digest). Handles HTML→text extraction for newsletters where plain text fallbacks are useless. Summarization via self-contained Python script calling Qwen 3.5 (NanoGPT) and delivery via AgentMail.
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…