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Linkedin Reader

CommunityPopular
himself65
linkedin-reader

Read LinkedIn for financial research using opencli (read-only). Use this skill whenever the user wants to read their LinkedIn feed, search for jobs in the finance/trading industry, view professional posts about markets or earnings, or gather professional sentiment from LinkedIn. Triggers include: "check my LinkedIn feed", "search LinkedIn for", "LinkedIn posts about", "what's on LinkedIn about AAPL", "finance jobs on LinkedIn", "LinkedIn market sentiment", "who's posting about earnings on LinkedIn", "LinkedIn feed", "professional network buzz", "what are analysts saying on LinkedIn", any mention of LinkedIn in context of reading financial news, market research, job searches, or professional commentary. This skill is READ-ONLY — it does NOT support posting, liking, commenting, connecting, or any write operations.

Overview

Publisherhimself65
Repositoryfinance-skills
Skill namelinkedin-reader
Stars
3.3K
Forks
378
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by himself65 on GitHub. Read the source before you install it.

Installation

Install the Linkedin Reader AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/himself65/finance-skills.git /tmp/finance-skills
mkdir -p .claude/skills
cp -r /tmp/finance-skills/plugins/social-readers/skills/linkedin-reader .claude/skills/linkedin-reader
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkedin Reader in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Linkedin Reader on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Linkedin Reader is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

LinkedIn Skill (Read-Only)

Reads LinkedIn for financial research using opencli, a universal CLI tool that bridges web services to the terminal via browser session reuse.

This skill is read-only. It is designed for financial research: reading professional commentary on markets, monitoring analyst posts, searching finance/trading jobs, and tracking professional sentiment. It does NOT support posting, liking, commenting, connecting, messaging, or any write operations.

Important: opencli reuses your existing Chrome login session — no API keys or cookie extraction needed. Just be logged into linkedin.com in Chrome and have the Browser Bridge extension installed.


Step 1: Ensure opencli Is Installed and Ready

Current environment status:

!`(command -v opencli && opencli doctor 2>&1 | head -5 && echo "READY" || echo "SETUP_NEEDED") 2>/dev/null || echo "NOT_INSTALLED"`

If the status above shows READY, skip to Step 2. If NOT_INSTALLED, install first:

bash
# Install opencli globally
npm install -g @jackwener/opencli

If SETUP_NEEDED, guide the user through setup:

Setup

opencli requires Node.js >= 20 and a Chrome browser with the Browser Bridge extension:

  1. Install the Browser Bridge extension:
    • Download the latest opencli-extension-v{version}.zip from the GitHub Releases page
    • Unzip it, open chrome://extensions in Chrome, and enable Developer mode
    • Click Load unpacked and select the unzipped folder
  2. Login to linkedin.com in Chrome — opencli reuses your existing browser session
  3. Verify connectivity:
bash
opencli doctor

This auto-starts the daemon, verifies the extension is connected, and checks session health.

Common setup issues

SymptomFix
Extension not connectedInstall Browser Bridge extension in Chrome and ensure it's enabled
Daemon not runningRun opencli doctor — it auto-starts the daemon
No session for linkedin.comLogin to linkedin.com in Chrome, then retry
AuthRequiredErrorLinkedIn session expired — refresh linkedin.com in Chrome and log in again

Step 2: Identify What the User Needs

Match the user's request to one of the read commands below, then use the corresponding command from references/commands.md.

User RequestCommandKey Flags
Setup checkopencli doctor
Home feed / postsopencli linkedin timeline--limit N (default 20, max 100)
Search for jobsopencli linkedin search "QUERY"--location, --limit N (default 10, max 100), --details
Finance job searchopencli linkedin search "QUERY"--experience-level, --job-type, --remote, --company, --date-posted, --start

Step 3: Execute the Command

General pattern

bash
# Read LinkedIn feed posts
opencli linkedin timeline --limit 20 -f json

# Search for finance/trading jobs
opencli linkedin search "quantitative analyst" --limit 10 -f json
opencli linkedin search "portfolio manager" --location "New York" --limit 15 -f json

# Detailed job listings with descriptions
opencli linkedin search "financial analyst" --details --limit 10 -f json

Key rules

  1. Check setup first — run opencli doctor before any other command if unsure about connectivity
  2. Use -f json or -f yaml for structured output when processing data programmatically
  3. Use -f csv when the user wants spreadsheet-compatible output
  4. Use --limit N to control result count — start with 10-20 unless the user asks for more
  5. For job search, use filters--location, --experience-level, --job-type, --remote, --date-posted to narrow results
  6. NEVER execute write operations — this skill is read-only; do not post, like, comment, connect, message, or apply to jobs

Output format flag (-f)

FormatFlagBest for
Table-f table (default)Human-readable terminal output
JSON-f jsonProgrammatic processing, LLM context
YAML-f yamlStructured output, readable
Markdown-f mdDocumentation, reports
CSV-f csvSpreadsheet export

Output columns

Timeline posts include: rank, author, author_url, headline, text, posted_at, reactions, comments, url.

Job search results include: rank, title, company, location, listed, salary, url. With --details: also description, apply_url.


Step 4: Present the Results

After fetching data, present it clearly for financial research:

  1. Summarize key content — highlight the most relevant posts or jobs for the user's research
  2. Include attribution — show author name, headline, post text, and engagement (reactions, comments)
  3. Provide URLs when the user might want to read the full post or job listing
  4. For feed posts, highlight market commentary, analyst takes, earnings reactions, and professional sentiment
  5. For job search results, present title, company, location, salary (when available), and posting date
  6. Flag sentiment — note bullish/bearish professional sentiment, consensus vs contrarian views
  7. Treat sessions as private — never expose browser session details

Step 5: Diagnostics

If something isn't working, run:

bash
opencli doctor

This checks daemon status, extension connectivity, and browser session health.


Error Reference

ErrorCauseFix
Extension not connectedBrowser Bridge not installed/enabledInstall extension and enable it in Chrome
No sessionNot logged into linkedin.comLogin to linkedin.com in Chrome
AuthRequiredErrorLinkedIn login wall detectedRefresh linkedin.com and log in again
EmptyResultErrorNo results found for queryBroaden search terms or check feed has content
Rate limitedToo many requestsWait a few minutes, then retry

Reference Files

  • references/commands.md — Complete read command reference with all flags, research workflows, and usage examples

Read the reference file when you need exact command syntax, research workflow patterns, or output details.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Linkedin Reader AI skill do?

Read LinkedIn for financial research using opencli (read-only). Use this skill whenever the user wants to read their LinkedIn feed, search for jobs in the finance/trading industry, view professional posts about markets or earnings, or gather professional sentiment from LinkedIn. Triggers include: "check my LinkedIn feed", "search LinkedIn for", "LinkedIn posts about", "what's on LinkedIn about AAPL", "finance jobs on LinkedIn", "LinkedIn market sentiment", "who's posting about earnings on LinkedIn", "LinkedIn feed", "professional network buzz", "what are analysts saying on LinkedIn", any me...

Why use Linkedin Reader on TypingMind?

Because you install it once and use it with any model. Linkedin Reader is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Linkedin Reader in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/himself65/finance-skills/tree/main/plugins/social-readers/skills/linkedin-reader. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Linkedin Reader?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Linkedin Reader?

As many as you like. As long as a model supports skills, you can use Linkedin Reader with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Linkedin Reader AI skill free?

Yes. It is published on GitHub by himself65 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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