Generate newsletter
Skill sarveshtalele/linkedin-content-skill/.claude/skills/generate-newsletter
Generate a long-form LinkedIn Newsletter edition. Usage: /generate-newsletter <topic> [in <niche>] [length: short|medium|long] [title: "<title>"]From its SKILL.md
npx -y skills add sarveshtalele/linkedin-content-skill --skill generate-newsletterAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 1 command, including `python3 scripts/generate_newsletter.py --topic "<parsed_topic>" --niche "<parsed_niche>" --length <parsed_length> --title "<parsed_title_or_empty>"`.
SKILL.md
1.1 KB, 230 tokens by cl100k_base, as published. Nobody here has run it
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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most marketing audience skills give in 230 tokens
Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07
- Apply Poppins font to headingsin 41 of 690, across 6 files
- Apply Lora font to body textin 41 of 690, across 6 files
- Use Arial fallback for headingsin 39 of 690, across 4 files
- Use Georgia fallback for body textin 39 of 690, across 4 files
- Maintain text hierarchy and formattingin 39 of 690, across 4 files
- Use accent colors for non-text shapesin 38 of 690, across 3 files
- Use RGB values for precise color matchingin 38 of 690, across 3 files
- Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
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- Use active voice instead of passive voicein 26 of 690, across 10 files
- Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
- Prioritize clarity over clevernessin 22 of 690, across 8 files
Said here and by no other author read
- parse arguments from user input
- extract the required topic argument
- run the prompt builder script
- read the script output
- include a headline and body sections
- include takeaways and an engagement question
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.