generate-calendar

generate-calendar is a skill for Claude Code from sarveshtalele/linkedin-content-skill. It costs 47 tokens per session (290 once invoked), scanned A, original, MIT.

A tool for planning LinkedIn posts across a chosen number of days, posting schedule, topic area, and goal.

In plain words
What is it for?
Use it to create a LinkedIn content calendar with monthly themes, search keywords, and a breakdown of post formats.
Why use it?
It removes the need to decide topics and formats one post at a time. The result is a Markdown table that can be copied into Notion or Google Sheets.

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_calendar.py --niche "<parsed_niche>" --days <parsed_days> --frequency "<parsed_frequency>" --goal <parsed_goal>.

Good fit Use it to create a LinkedIn content calendar with monthly themes, search keywords, and a breakdown of post formats.

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-calendar

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-calendar

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/generate-calendar"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/generate-calendar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 290 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 $0.00047 $0.00290
Opus 5 $0.00023 $0.00145
Sonnet 5 $0.00009 $0.00058
Haiku 4.5 $0.00005 $0.00029

Measured 10d ago against content hash f27806ef7045, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

generate-calendar 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 10d 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-calendar/SKILL.md · 31 lines

What it actually says

You are an expert LinkedIn Content Strategist. A user wants a content calendar.

Step 1 — Parse Arguments

The user's input is: $ARGUMENTS

Extract:

  • niche — industry/niche (required — ask if missing)
  • days — number of days (default: 30)
  • frequency — posting frequency (default: "3 times a week")
  • goal — awareness | engagement | leads | authority | growth (default: growth)

Step 2 — Run the Prompt Builder

python3 scripts/generate_calendar.py --niche "<parsed_niche>" --days <parsed_days> --frequency "<parsed_frequency>" --goal <parsed_goal>

Step 3 — Generate the Calendar

Read the script output. Generate a full Markdown table calendar with monthly theme, top SEO keywords, and format breakdown.

Step 4 — Show Output

Clean Markdown table, ready to copy into Notion or Google Sheets.

Step 5 — Ask for Feedback

🎯 Useful calendar? Type /feedback <what worked> to save preferences 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. 10d ago First seen · 31 lines · 0 tokens per session scan A f27806ef7045

Subscribe to this mod's changes

generate-calendar is a skill published in the GitHub repository sarveshtalele/linkedin-content-skill (7 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 290 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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