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