generate-newsletter

generate-newsletter is a skill for Claude Code from sarveshtalele/linkedin-content-skill. It costs 40 tokens per session (279 once invoked), scanned A, original, MIT.

A generator for long-form LinkedIn Newsletter editions, which are recurring articles published on LinkedIn. It accepts a topic, niche, length, and optional series title.

In plain words
What is it for?
Creating a newsletter headline, article sections, key takeaways, and a question to encourage reader responses.
Why use it?
It gives you a repeatable structure for turning a subject into a publishable newsletter with less manual drafting.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/generate_newsletter.py --topic "<parsed_topic>" --niche "<parsed_niche>" --length <parsed_length> --title "<parsed_title_or_empty>".

Good fit Creating a newsletter headline, article sections, key takeaways, and a question to encourage reader responses.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/sarveshtalele/linkedin-content-skill
agentmods
npx agentmods add skills/sarveshtalele/linkedin-content-skill/generate-newsletter

Made for: Claude Code.

Wrote 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.

agentmods badge for generate-newsletter

README.md
[![agentmods](https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/generate-newsletter/github.svg)](https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/generate-newsletter)
Your own site
<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/generate-newsletter"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/generate-newsletter/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.

agentmods 80×15 button for generate-newsletter

Your own site · 80×15
<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/generate-newsletter"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/generate-newsletter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 279 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00279
Opus 5 $0.00020 $0.00139
Sonnet 5 $0.00008 $0.00056
Haiku 4.5 $0.00004 $0.00028

Measured 11d ago against content hash a22ea8936c41, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

generate-newsletter 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.

.claude/skills/generate-newsletter/SKILL.md · 31 lines

What it actually says

You are an expert LinkedIn Content Strategist. A user wants to generate a LinkedIn Newsletter.

Step 1 — Parse Arguments

The user's input is: $ARGUMENTS

Extract:

  • topic — newsletter subject (required)
  • niche — industry/niche (default: "AI & Technology")
  • length — short | medium | long (default: medium)
  • title — optional newsletter series name

Step 2 — Run the Prompt Builder

python3 scripts/generate_newsletter.py --topic "<parsed_topic>" --niche "<parsed_niche>" --length <parsed_length> --title "<parsed_title_or_empty>"

Step 3 — Generate the Newsletter

Read the script output. Generate a full newsletter with headline, body sections, takeaways, and engagement question.

Step 4 — Show Output

Format in clean Markdown, ready to publish in LinkedIn Newsletter editor.

Step 5 — Ask for Feedback

🎯 Did this edition resonate? Type /feedback <what you liked> to save this style to memory.

Changes

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.

  1. 11d ago First seen · 31 lines · 0 tokens per session scan A a22ea8936c41

Subscribe to this mod's changes

generate-newsletter is a skill published in the GitHub repository sarveshtalele/linkedin-content-skill (7 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 279 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 2026-08-31.

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