create-linkedin-content

create-linkedin-content is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 79 tokens per session (1,618 once invoked), scanned A, original, MIT.

A writing workflow for drafting several versions of a LinkedIn post from a short brief. It uses a personal voice guide when available and applies conventions suited to LinkedIn posts.

In plain words
What is it for?
Create LinkedIn post variants with explanations of why the topic matters, arrow-style bullets, and lengths suited to the platform.
Why use it?
It helps turn one idea into posts with different angles while keeping the writing closer to the author's own voice.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code.

Good fit Create LinkedIn post variants with explanations of why the topic matters, arrow-style bullets, and lengths suited to the platform.

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Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/create-linkedin-content
About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,202 stars · on GitHub · gooseworks.ai

Install

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.

Any agent
npx skills add gooseworks-ai/goose-skills --skill create-linkedin-content
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

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 create-linkedin-content

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/create-linkedin-content/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/create-linkedin-content)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/create-linkedin-content"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/create-linkedin-content/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 create-linkedin-content

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/create-linkedin-content"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/create-linkedin-content.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,618 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00079 $0.01618
Opus 5 $0.00039 $0.00809
Sonnet 5 $0.00016 $0.00324
Haiku 4.5 $0.00008 $0.00162

Measured 8d ago against content hash 96d6e94211b7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

create-linkedin-content 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 8d 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.

skills/content/capabilities/create-linkedin-content/SKILL.md · 154 lines

How it starts

The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Create LinkedIn Content

Draft LinkedIn post variants that sound like a real human practitioner, not a LinkedIn thought-leader persona. Reads a voice guide the user has already generated (or prompts to create one), produces multiple framings of the same idea, and saves each variant as its own markdown file with frontmatter.

This is an agent-executed skill — the agent does the drafting and self-check inline. No Python script.

Mirrors create-x-content structurally; differences are marked ★.

Quick Start

/create-linkedin-content --brief "New open-source CLI that turns Figma files into React components. Called figma2react. Free, MIT licensed."

Or interactively:

/create-linkedin-content

Inputs

Flag Required Default
--brief Yes (asked interactively if missing)
--variants No Skill decides based on brief richness (2–5)
--voice-guide No Resolved via chain below
--output No ./content/YYYY-MM-DD-<topic-slug>/
--topic No Derived from brief

Voice Guide Resolution

Resolve in this order, stop at first hit:

  1. --voice-guide <path> flag
  2. ~/.goose-skills/config.jsonvoice_guides.linkedin
  3. ~/.goose-skills/voice-guides/voice-linkedin.md (default path)
  4. Fallback prompt — no guide found. Three options:
    • (a) Paste a path to an existing guide
    • (b) Run /generate-voice-guide --platforms linkedin now to create one (recommended)
    • (c) Proceed with a neutral default (warn that variants will sound generic)

LinkedIn-Specific Defaults ★

Apply these unless the voice guide explicitly says otherwise:

  • Length: 150–500 words per variant (LinkedIn rewards longer than X but shorter than a blog post)
  • Bullets: arrow style () for workflow steps — this is LinkedIn-native
  • Numbered steps: emoji numbers 1️⃣ 2️⃣ 3️⃣ allowed sparingly for installation/quickstart type posts
  • "Why this matters" beat: every post should have one — LinkedIn audiences want the so what? more explicitly than X audiences
  • Hashtags: 0–2 max. Prefer zero.
  • CTAs: "Link in comments" pattern is LinkedIn-native; inline links are fine too. Avoid "Follow me for more."
  • Openings: more scene-setting is ok (1–2 lines of context before the hook). Unlike X, you don't have to hook-first.

Read the full file on GitHub · 154 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 8d ago First seen · 154 lines · 79 tokens per session scan A 96d6e94211b7

Subscribe to this mod's changes

create-linkedin-content is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 79 tokens to every session and 1,618 once invoked, about $0.0004 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.