# Linkedin Lead Gen Outreach > Lightweight LinkedIn prospecting and outreach workflow for researching qualified leads, applying simple prioritization, drafting concise personalized messages, exporting clean CSV or Google Sheets-ready lead lists, and summarizing campaign activity. Use when preparing a compliant LinkedIn lead generation process, refining ICP-based targeting, building review-ready lead sheets, or generating simple outreach dashboards. - Skill: `knownasnaffy/linkedin-lead-gen-outreach-2` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add knownasnaffy/linkedin-lead-gen-outreach-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/knownasnaffy/linkedin-lead-gen-outreach-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: knownasnaffy (https://skillmd.com/u/knownasnaffy) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/knownasnaffy/linkedin-lead-gen-outreach-2 --- ### Cleanup Review `python scripts/_reset_state.py` is an optional cleanup helper for the `LinkedIn Lead Gen Outreach` skill. It may recursively remove /tmp/.skill_state. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms. # LinkedIn Lead Gen Outreach Run a clean, review-first LinkedIn prospecting workflow focused on lead quality, concise messaging, and simple export-ready sales operations. Keep every output structured, evidence-based, and easy to review before outreach. ## Workflow Use this sequence for complete requests: 1. define targeting 2. collect prospect data 3. apply simple lead scoring 4. draft short personalized outreach 5. export structured lead data 6. summarize campaign metrics ## 1. Define targeting Capture the search brief before producing leads. Minimum inputs: - keywords - target job titles - seniority - industry or company type - location - exclusions - business objective If the request is underspecified, convert it into a concise ICP before generating leads. ## 2. Collect prospect data Use visible LinkedIn information, user-provided data, or manually reviewed search results. Capture these fields whenever possible: - full name - LinkedIn URL - title - company - location - search match - business potential note - personalization signal - source list or query Useful personalization signals include: - recent post theme - recent promotion or job change - hiring activity - company growth signal Do not invent facts. If evidence is weak, mark it clearly and keep the message more general. ## 3. Apply simple lead scoring Use a lightweight and explainable scoring model. Default scoring dimensions: - role relevance: 0-5 - company fit: 0-5 - likely need: 0-5 - timing signal: 0-5 - personalization depth: 0-5 Total score bands: - 20-25: high priority - 12-19: medium priority - 0-11: low priority Always include a one-line explanation. ## 4. Draft personalized messages Write opening messages that are: - professional - concise - 2-3 lines max - easy to review and edit - grounded in real signals Recommended structure: 1. relevant opener 2. business relevance 3. soft CTA Rules: - keep messages short and polished - avoid hype, pressure, or artificial urgency - avoid unsupported claims - if personalization is weak, prefer a role-based message over forced specificity ## 5. Use message templates Adapt one of the templates in `references/templates.md`. Prefer: - signal-based messages when evidence is strong - role-based messages when evidence is moderate - executive-tone messages for senior stakeholders ## 6. Export format Prefer a flat CSV structure that also imports cleanly into Google Sheets. Recommended columns: - first_name - last_name - full_name - linkedin_url - title - company - location - keyword_match - business_potential_note - personalization_note - score_total - priority - score_reason - message_v1 - campaign_name - owner - source - status - next_action Suggested status values: - to_review - approved - ready_for_outreach - contacted - replied - disqualified ## 7. Dashboard and statistics When the user asks for a dashboard, produce a lightweight summary that can live in Markdown, CSV-derived calculations, or Google Sheets. Include these default metrics: - total leads - high / medium / low priority counts - leads by title - leads by geography - personalization coverage - leads ready for outreach Keep it simple and executive-friendly. ## Google Sheets guidance When preparing a sheet: - freeze the top row - apply filters to all headers - use data validation for `priority`, `status`, and `next_action` - add a summary section above or in a second tab - preserve the original raw data columns ## Compliance standard Operate in a LinkedIn-compliant, review-first manner. Use this skill to support: - profile research - qualification - message drafting - structured exports - reporting Do not rely on deceptive automation, hidden sending loops, or behavior intended to bypass platform safeguards. ## Deliverable order For a complete request, produce outputs in this order: 1. targeting summary 2. scoring rubric 3. lead table or CSV-ready rows 4. message variants 5. dashboard summary 6. Google Sheets notes ## Quality bar A strong result is: - clean and business-ready - grounded in visible evidence - concise enough for sales execution - easy to export or review - compliant and professional ## Community edition note This edition focuses on lightweight prospect research, simple prioritization, concise outreach drafting, and clean CSV or Sheets-ready exports. ## Resources Use bundled resources when useful: - `references/templates.md` for ICP, scoring, and message templates - `scripts/csv_builder.py` to convert JSON leads into CSV - `scripts/sheets_prep.py` to normalize CSV fields for Google Sheets workflows - `scripts/dashboard_stats.py` to compute simple campaign metrics from a CSV file