Linkedin Custom

LinkedIn saved jobs → EROI scoring → structured reports → git-committed market intelligence

Automated pipeline that scrapes your LinkedIn saved jobs, scores them against your profile using a 6-dimension EROI model, and writes actionable reports. Built as an MCP server — use it from Claude, VS Code, Cursor, opencode, or any MCP client.

Python 3.12+ MCP PRs Welcome License: MIT

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🔍 What problem does this solve?

LinkedIn's job recommendations are noisy. Of 49 saved jobs analyzed, only 12% were relevant (SLEDOVAT/follow). The rest are noise — AI hype roles, fake-engineer titles, distant locations, non-strategic employers.

This tool replaces manual scrolling and gut-feel decisions with a repeatable, transparent scoring pipeline:

📌 Your LinkedIn saved jobs
   ↓
🕷️ Patchright browser scraper   ← 4-layer resilience (CSS + text + JSON + full DOM)
   ↓
📊 EROI scoring engine          ← 6 dimensions, content-aware matching
   ↓
📝 Structured reports           ← agregovany_report.md + metadata_stacku.json
   ↓
📈 Synthetic market analysis    ← Frequency matrix + SNR + gap detection
   ↓
📦 Auto-committed to KB         ← git commit with full history

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✨ Features

Feature	What it does
Smart scraping	4-layer job ID extraction (href, attributes, JSON blobs, full DOM) — catches 100% of IDs
6-dimension EROI scoring	Domain (35%), Tech (25%), Role (20%), Growth (10%), Formal (5%), Location (5%)
Fake-engineer detection	Identifies roles with "Engineer" in title but service/sales content
Skill gap analysis	Direct match / partial match / no-match per job, aggregated into market-wide SNR
Report generation	Human-readable .md + machine-readable .json + synthetic market analysis
Git commit	Every pipeline run auto-commits to your knowledge base
Session resilience	Cookie lifecycle detection, checkpoint/challenge page handling, 60s TTL cache
MCP-native	Works with any MCP client — Claude Desktop, VS Code, Cursor, opencode

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🚀 Quick Start

Prerequisites

• Python 3.12+
• uv (faster pip alternative — pip install uv)

Install

git clone https://github.com/outpost2026/linkedin-mcp-custom.git
cd linkedin-mcp-custom
uv sync

Authenticate (one-time)

.\linkedin-mcp.bat --login
# Opens a browser window — log into LinkedIn, then press Enter

Verify session

.\linkedin-mcp.bat --status
# ✅ Session valid  |  page.url = https://www.linkedin.com/feed/

Run the pipeline

# Via MCP client (recommended)
.\linkedin-mcp.bat
# Then in your MCP client: call analyze_saved_jobs

# Or standalone CLI (bypasses MCP transport ~ 2-3 min for 49 jobs)
.venv\Scripts\python scripts\run_pipeline.py

Output: agregovany_report.md + metadata_stacku.json in your KB directory, plus synthetic report.

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📊 Example output (from 49 real jobs)

📊 49 saved jobs analyzed
   🟢 SLEDOVAT   6  (12%) — apply now
   🟡 MEDIUM    27  (55%) — consider
   🟡 HRANIČNÍ  12  (24%) — borderline
   🔴 NESLEDOVAT  4   (8%) — skip

🏆 Top leads:
   1. #003 Thermo Fisher — System Integration Engineer   76.5% 🟢
   2. #019 Siemens — RAM/LCC Engineer                    69.4% 🟢
   3. #013 Siemens — Test Automation Engineer             67.6% 🟢
   4. #015 Siemens — Embedded Tools Developer             65.9% 🟢
   5. #031 Renesas — Digital Design Engineer              65.5% 🟢
   6. #001 Desoutter — Light Automation Specialist        65.7% 🟢

🔬 Market intelligence:
   IoT (56% SNR), scripting (50%) = strongest relevance predictors
   AI appears in 49/49 jobs but only 12% SNR → noise signal
   Biggest skill gaps: C++ (16×), Azure (12×), AWS (9×)

Full report: synteticky_report_analyza.md

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🧠 EROI Scoring Model

6 dimensions

Dimension	Weight	What it measures
Domain	35%	Industrial automation (core) vs adjacent vs noise
Tech	25%	Skill overlap — content-aware match ratio × coverage
Role	20%	Engineering role vs "fake engineer" (service/sales)
Growth	10%	Strategic employer (Siemens, ABB, Thermo Fisher…)
Formal	5%	Degree requirements with flexibility detection
Location	5%	Remote/hybrid/CZ vs distant/office-only

Thresholds

Score	Verdict
≥65%	🟢 SLEDOVAT (follow — apply now)
50–64%	🟡 MEDIUM (consider — mitigate gaps)
40–49%	🟡 HRANIČNÍ (borderline — only if time permits)
<40%	🔴 NESLEDOVAT (skip — no time allocation)

Special patterns detected

• Fake engineer: title says "Engineer" but content is service/sales → penalizes role
• Electronics manufacturing SMT/PCBA: caps domain score (adjacent, not core)
• Degree flexibility: "equivalent practical experience" found → adds ~5% to formal
• Positioning match: strong role match compensates for weak domain
• No-match penalty: tech score drops sharply when key skills missing

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🛠️ MCP Tools

Tool	Description
analyze_saved_jobs	Full pipeline: scrape → EROI score → KB write → git commit
get_saved_jobs	List all saved job IDs from LinkedIn tracker
get_job_details <id>	Full posting text for a single job ID
analyze_job <id>	EROI score a single job (avoids timeout)
check_session	Verify LinkedIn auth status with diagnostics

Timeout strategy: analyze_saved_jobs uses time-budgeted batch processing (default 45s). For full analysis, use the CLI pipeline or call analyze_job per job.

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📁 Output structure

B2B-Knowledge-Base/
└── 02_ANALÝZY/
    └── 00_linkedin/
        ├── agregovany_report.md          # Human-readable EROI entries
        ├── metadata_stacku.json          # Machine-readable (schema v1.1)
        └── synteticky_report_analyza.md  # Market intelligence report

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🧪 Development

# Tests
.venv\Scripts\python -m pytest tests/ -v

# Lint
.venv\Scripts\python -m ruff check src/

# Type check
.venv\Scripts\python -m mypy src/

Debugging known issues

See the pitevni_kniha (autopsy book) for 14 documented bugs, root causes, fixes, and 16 cross-repo engineering rules.

Known issue	Status
MCP transport timeout for batch ops	✅ Fixed (time-budget + per-job tool)
Cookie lifecycle — silent expiry	✅ Fixed (session cache + checkpoint detection)
KB dedup fallback (industry=None)	✅ Fixed
Summary table non-idempotent	✅ Fixed
Pagination missing pages	✅ Fixed
CSS selector fragility	✅ Fixed

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🤝 Contributing

PRs welcome! This project especially needs:

• CI/CD pipeline — GitHub Actions for weekly scraping
• Docker deployment — containerize the MCP server
• More scorers — add dimensions (salary, benefits, team size)
• UI — simple dashboard for browsing scored jobs
• Translations — localize EROI labels for your market

Please read CONTRIBUTING.md first (coming soon).

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📄 License

MIT — see LICENSE.

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🧭 Why this exists

Built by a systems integration engineer who got tired of LinkedIn's noise-to-signal problem. The name "EROI" comes from energy-return-on-investment — a concept borrowed from off-grid solar (which the author also builds). The same principle applies to job hunting: don't spend energy where the return is negative.

"LinkedIn recommends everything. This tool tells you what matters."

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🔗 Links

• Synthetic market report (49 jobs)
• Aggregated report (49 entries)
• Bug autopsy book
• MCP practical workflow guide
• Shared MCP postmortems