linkedin-hand-skill

A reference guide for managing LinkedIn through its API, the software interface used to read account data and publish content. It covers authentication, posts, comments, and likes.

In plain words
What is it for?
Use it to retrieve account information, publish text posts, comment on posts, and like posts when the required LinkedIn access is available.
Why use it?
It gives developers the endpoint and request details needed to connect an application to LinkedIn without searching through the API documentation each time.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/librefang/librefang-registry/linkedin
Any agent
npx skills add librefang/librefang-registry --skill linkedin
Clone the repo
git clone --depth 1 https://github.com/librefang/librefang-registry

Made for: Claude Code, Codex.

Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,685 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00028 $0.08685
Opus 5 $0.00014 $0.04342
Sonnet 5 $0.00006 $0.01737
Haiku 4.5 $0.00003 $0.00869

Measured 2d ago against content hash eee70aa10e7b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

linkedin-hand-skill scanned grade A with 1 finding 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
Origin

This is a copy

100% identical to linkedin-hand-skill — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

hands/linkedin/SKILL.md · 1,040 lines

How it starts

The opening of the file, as written. The whole thing — 1,040 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Management Expert Knowledge

LinkedIn API Reference

Authentication

LinkedIn API uses OAuth 2.0 with bearer tokens.

Bearer Token:

Authorization: Bearer $LINKEDIN_ACCESS_TOKEN

Core Endpoints

Get authenticated user info:

curl -s -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
  -H "LinkedIn-Version: 202405" \
  "https://api.linkedin.com/rest/userinfo"

Create a text post (Posts API):

curl -s -X POST "https://api.linkedin.com/rest/posts" \
  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -H "LinkedIn-Version: 202405" \
  -d '{
    "author": "urn:li:person:MEMBER_ID",
    "lifecycleState": "PUBLISHED",
    "commentary": "Your post content here",
    "visibility": "PUBLIC",
    "distribution": {
      "feedDistribution": "MAIN_FEED"
    }
  }'

Comment on a post:

curl -s -X POST "https://api.linkedin.com/rest/socialActions/URN/comments" \
  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -H "LinkedIn-Version: 202405" \
  -d '{
    "actor": "urn:li:person:MEMBER_ID",
    "message": {"text": "Your comment here"}
  }'

Like a post:

curl -s -X POST "https://api.linkedin.com/rest/socialActions/URN/likes" \
  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -H "LinkedIn-Version: 202405" \
  -d '{
    "actor": "urn:li:person:MEMBER_ID"
  }'

Rate Limits

Endpoint Limit Window
Posts 25 posts 24 hours
Comments 10 comments 1 minute
Likes 20 likes 1 minute
API calls (general) 100 requests 1 day

LinkedIn Content Strategy

The LinkedIn Algorithm (2024-2025)

Key factors that affect reach:

  1. Dwell time: How long people spend reading your post. LinkedIn tracks both "read dwell" (time spent on post text) and "click dwell" (time spent after clicking "see more"). Longer posts that hold attention get amplified. Ideal: 800-1300 characters that reward reading to the end.
  2. Early engagement: Comments in the first 60-90 minutes are weighted heavily. The algorithm decides distribution tiers within 2 hours of posting.
  3. Meaningful comments: Long comments (3+ sentences) signal quality far more than likes. One thoughtful comment is worth ~10 likes in the algorithm. Reply-to-reply threads (nested comments) further boost the post.
  4. No external links: Posts with links get 40-50% less reach. The algorithm deprioritizes anything that drives users off-platform.
  5. Personal stories: Narrative content outperforms promotional content. The algorithm favors "knowledge and advice" posts from individuals over brand content.

Read the full file on GitHub · 1,040 lines

Files

What ships with it

2 files 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. 2d ago First seen · 1,040 lines · 28 tokens per session scan A eee70aa10e7b

Subscribe to this mod's changes

linkedin-hand-skill is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 8d ago), licensed MIT. It adds 28 tokens to every session and 8,685 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to linkedin-hand-skill, differing in 0 lines, and is treated as a copy.

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