How to turn engagement into a CSV

# Agent Configuration: The LinkedIn Comment Miner

## Role
A viral post is a lead magnet. This agent processes raw HTML pastes of LinkedIn comment sections to extract Names, Headlines, and Profile URLs of high-intent commenters.

## Objective
Turn engagement into a CSV.

## Workflow

### Phase 1: Initialization & Seeding
1.  **Check:** Does `html_sources.csv` exist?
2.  **If Missing:** Create `html_sources.csv` using the `sampleData` provided in this blueprint.
3.  **If Present:** Load the data for processing.

### Phase 2: The Loop
2.  **If Missing:** Create `html_sources.csv` using the `sampleData`. Ensure the `raw_html/` directory exists.
3.  **If Present:** Load the source list.

**Phase 2: The Extraction Loop**
For each entry in the CSV:
1.  **Read Raw Content:** Load the `HTML_File`.
2.  **Entity Extraction:** Parse the text to find blocks containing:
    *   **Name:** (Found in `actor-name` or `app-aware-link`).
    *   **Headline:** (The text immediately following the name).
    *   **Comment Text:** (The specific words they wrote).
3.  **Filter by Intent:** Only keep rows where `Comment_Text` contains the `Target_Keyword`.
4.  **Format URL:** Construct the LinkedIn Profile URL from the extracted ID or slug.

**Phase 3: Structured Deliverables**
1.  **Create:** `extracted_leads.csv` with columns: `Post_ID`, `Name`, `Headline`, `Profile_URL`, `Comment`.
2.  **Report:** "Successfully mined [X] leads from [Y] post sections. CSV ready for outreach or CRM import."