Borrowing it
Nothing to install: this file belongs to Maheidem/linkedin-optimizer-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Maheidem/linkedin-optimizer-mcp/feature/npm-package-setup/CLAUDE.mdgit clone --depth 1 https://github.com/Maheidem/linkedin-optimizer-mcpWrote 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.
[](https://agentmods.dev/instructions/maheidem/linkedin-optimizer-mcp/claude-md)<a href="https://agentmods.dev/instructions/maheidem/linkedin-optimizer-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/maheidem/linkedin-optimizer-mcp/claude-md.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01639 | $0.01639 |
| Opus 5 | $0.00820 | $0.00820 |
| Sonnet 5 | $0.00328 | $0.00328 |
| Haiku 4.5 | $0.00164 | $0.00164 |
Grade A, and why
linkedin-optimizer-mcp CLAUDE.md 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post Creation Workflow
This document outlines the exact workflow for creating high-quality LinkedIn posts based on Marcos Heidemann's preferences and successful posting patterns.
📋 Complete Workflow Steps
1. Date/Time Check (MANDATORY FIRST STEP)
date
Purpose: Get accurate current date/time for research context Why: Ensures research is current and relevant
2. Research Phase
Use the research-documentation-specialist agent:
Task: Research latest [TOPIC] news from [CURRENT_DATE]. Focus on:
- Recent developments and breakthroughs
- Technical advances with specific metrics
- Business/industry impact with statistics
- Emerging trends relevant to ML/DS engineers
3. Source Verification (CRITICAL)
After initial research, ALWAYS verify sources:
Task: Find and verify actual URLs for these specific claims:
[LIST EACH STATISTIC/CLAIM]
For each claim, I need:
- Exact, accessible URL (not paywalled)
- Verification the link works and contains specific data
- If paywalled, find alternative accessible sources
- Confirm publication date
Provide only verified, clickable URLs. If cannot verify, clearly state which claims lack sources.
4. Content Creation
Follow these rules:
❌ NEVER DO:
- Start with "As a Principal..." or any role-based opening
- Include statistics without verified source URLs
- Use generic professional introductions
✅ ALWAYS DO:
- Start with direct insight, observation, or intriguing statement
- Include only verified statistics with working URLs
- Provide sources section with numbered list
- Ask specific questions targeting ML/DS professionals
Post Structure:
Hook (1 line)
↓
Context/Development (2-3 sentences)
↓
Analysis/Perspective (2-4 bullet points)
↓
Engagement Question (1-2 sentences)
↓
Sources (mandatory)
↓
Hashtags (5-7 maximum)
5. Source Attribution Format
Sources:
• [Description]: [URL]
• [Description]: [URL]
• [Description]: [URL]
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.
- 5d ago First seen · 211 lines · 1,639 tokens per session scan A 831ba02023c8
linkedin-optimizer-mcp CLAUDE.md is an instructions file published in the GitHub repository Maheidem/linkedin-optimizer-mcp (1 stars, last pushed 9mo ago), licensed MIT. It adds 1,639 tokens to every session, about $0.0082 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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