Git2LinkedIn

Overview

Workflow

1. Collect required inputs:

• role

  (required)

• company

  (required)

1. Collect optional inputs:

• repo

  (default

  .

  )

• since

  (

  YYYY-MM-DD

  )

• until

  (

  YYYY-MM-DD

  )

• author

  (default: current git user)

• out

  (optional markdown output path)

• max-commits

  (default

  400

  )

1. Run the extractor:

# All-time
python3 skills/git2linkedin/scripts/git_history_extract.py \
  --role "Senior Software Engineer" \
  --company "Acme"

# Date range
python3 skills/git2linkedin/scripts/git_history_extract.py \
  --role "Senior Software Engineer" \
  --company "Acme" \
  --since 2025-01-01 \
  --until 2025-12-31

1. Convert JSON output into final LinkedIn text:

• Write one short English summary sentence.
• Write 4-6 impact bullets.
• Focus on end-user value and business relevance.
• Avoid repository metrics, commit hashes, file paths, and internal identifiers.

1. Handle empty ranges explicitly:

• If commit list is empty, explain that no commits were found and suggest widening the date range or changing

  author

  .

Output Contract

• summary

  : baseline sentence for LinkedIn experience

• feature_highlights

  : feature-oriented bullet candidates

• end_user_outcomes

  : user-facing outcomes

• business_relevance

  : why this work matters for role/business goals

• source_mode

  : extraction mode metadata (

  direct-commit-messages

  )

• guidance

  : fallback suggestion when history is empty

Guardrails

• Keep output in English unless user asks otherwise.
• Keep bullets concise and outcome-focused.
• Keep sentence style mostly passive and neutral; avoid first-person phrasing (

  I improved

  ,

  I enhanced

  , etc.).
• Do not include repository statistics.
• Do not expose commit hashes, file paths, issue IDs, URLs, or raw commit messages.
• Do not invent confidential product details; keep language high-level and safe for public profiles.