post-engagers

name

post-engagers

description

Extract people who engage (comment, react, repost) on a LinkedIn post, enrich their profile / email / company data, and land them in a local SQLite CRM (content.db → crm.db) as the source of truth. Extruct people-table upload is optional. Triggers on: "post engagers", "linkedin engagers", "who commented on", "who liked", "who reacted", "linkedin post engagers", "scrape post", "extract engagers", "post commenters".

LinkedIn Post Engagers

Turn LinkedIn post engagement into a prospecting list. The canonical sink is content.db (raw interactions) → crm.db (unified CRM). Uploading to an Extruct people table is an optional side output, not the default.

Assumed repo layout

This skill expects the repo to follow the revops/ convention:

revops/
  content/
    fetch_interactions.py    # scrape post → content.db
    enrich_profiles.py       # member_id / profile_urn via AnySite
    enrich_emails.py         # Fullenrich v2 bulk
    enrich_companies.py      # Extruct firmographics
  etl/
    content_to_crm.py        # content.db → crm.db raw_content_*
    crm_to_attio_content.py  # (optional) crm.db → Attio
  lib/
    classify.py              # segment classifier (owns segment taxonomy)
    config.py                # CONTENT_DB_PATH, CRM_DB_PATH, load_env
    fullenrich.py, extruct.py, attio.py
  db/
    content.db
    crm.db

Segment classification lives in revops/lib/classify.py — do not reimplement or inline a regex table in this skill. fetch_interactions.py calls it automatically.

Related skills

post-engagers → (crm.db lands engagers) → email-generation → campaign-sending

Downstream skills read from crm.db / content.db, not from an intermediate CSV.

Inputs

Input	Source	Required
LinkedIn post URL(s)	User provides	yes
Engagement types	comments / reactions / reposts (default: all)	no
Enrichment stages to run	profiles / emails / companies (default: all)	no
Push to Attio?	yes/no (default: no)	no
Upload to Extruct people table?	yes/no (default: no)	no

Workflow

Step 1: Collect post URLs

Get the LinkedIn post URL(s) from the user. Accept one or multiple. Each URL will be passed directly to fetch_interactions.py, which extracts the URN.

Check whether any of the URNs already exist in content.db — if so, re-running fetch will upsert and pick up new engagers since last run.

SELECT urn, author_name, comment_count, reaction_count, share_count, fetched_at
FROM content_posts WHERE urn IN (...);

Step 2: Scrape engagers → content.db

Run the fetch script per post. It uses AnySite MCP (get_linkedin_post_comments, get_linkedin_post_reactions, get_linkedin_post_reposts), classifies segments via lib.classify, and writes to content_posts + content_interactions.

python3 revops/content/fetch_interactions.py "<post_url>"
# or scrape a single interaction type:
python3 revops/content/fetch_interactions.py --urn <URN> --type comments

If the user has a different scraping provider (Apify, RapidAPI, Phantombuster, self-hosted), adapt fetch_interactions.py to call that provider — do not bypass the script and write to content.db from a notebook. The schema, upsert logic, and segment classification all live there.

Step 3: Enrichment stages (in order)

Each stage is idempotent and only hits rows missing the target field. Run them sequentially — later stages depend on earlier fields (emails need profiles, companies need domain).

python3 revops/content/enrich_profiles.py    # AnySite user endpoint → member_id, profile_urn
python3 revops/content/enrich_emails.py      # Fullenrich → email, email_status, domain
python3 revops/content/enrich_companies.py   # Extruct → firmographics by domain

Useful flags:

• --dry-run on every stage to preview before paying for credits.
• --limit N to cap batch size.
• enrich_emails.py --segment "Founders / CEOs" --segment "Sales Leadership" to restrict email spend to decision makers.

Before running email enrichment, show the user the segment breakdown so they can decide which segments to include:

SELECT segment, COUNT(*) AS n
FROM content_interactions
WHERE post_id IN (SELECT id FROM content_posts WHERE urn IN (...))
GROUP BY segment ORDER BY n DESC;

Confirm selection before calling Fullenrich — email credits cost real money.

Step 4: Sync content.db → crm.db

python3 revops/etl/content_to_crm.py --post <URN>   # single post
python3 revops/etl/content_to_crm.py                # all

This populates raw_content_posts and raw_content_interactions in crm.db. The stg_people / mart_people views then merge engagers with LinkedIn connections, campaign contacts, and Attio records automatically — no further action needed for the unified view.

Step 5 (optional): Push to Attio

Only if the user explicitly asks to sync to Attio:

python3 revops/etl/crm_to_attio_content.py --dry-run
python3 revops/etl/crm_to_attio_content.py

Step 6 (optional): Upload to an Extruct people table

Only when the user wants engagers in an Extruct generic table — e.g. to feed a separate Extruct-driven campaign flow that doesn't read from crm.db. Delegate Extruct API calls to the extruct-api skill. Pull rows from mart_people (or content_interactions filtered by post URN) rather than re-deriving from scratch.

Suggested columns:

{
  "name": "{user-provided name or 'Post Engagers — {post_author} — {date}'}",
  "kind": "generic",
  "column_configs": [
    {"kind": "input", "name": "Full Name", "key": "full_name"},
    {"kind": "input", "name": "LinkedIn URL", "key": "linkedin_url"},
    {"kind": "input", "name": "Job Title", "key": "job_title"},
    {"kind": "input", "name": "Segment", "key": "segment"},
    {"kind": "input", "name": "Engagement Type", "key": "engagement_type"},
    {"kind": "input", "name": "Source Post", "key": "source_post"},
    {"kind": "input", "name": "Company", "key": "company"},
    {"kind": "input", "name": "Domain", "key": "domain"},
    {"kind": "input", "name": "Email", "key": "email"}
  ]
}

Deduplicate by linkedin_url against the target table before uploading.

Verification checklist

After the chain completes, sanity-check before declaring done:

-- in content.db
SELECT interaction_type, COUNT(*) FROM content_interactions
  WHERE post_id = (SELECT id FROM content_posts WHERE urn = ?) GROUP BY 1;
SELECT COUNT(*) filter (WHERE email IS NOT NULL AND email != '') AS with_email,
       COUNT(*) AS total
  FROM content_interactions WHERE post_id = ...;

-- in crm.db
SELECT COUNT(*) FROM raw_content_interactions WHERE post_id = ...;
SELECT COUNT(*) FROM mart_people WHERE interaction_count > 0;

Tips

• Multiple posts = richer list. Scrape 3–5 recent posts from the same author; engagers who hit on 2+ posts are the warmest leads. stg_engagement already aggregates per person.
• Filter before enriching emails. Emails are the expensive stage — restrict by segment to avoid paying for students, recruiters, bots.
• Re-run safely. Every script is idempotent; fetch upserts, enrichment only touches null fields, sync uses ON CONFLICT DO UPDATE.
• Don't hand-edit content.db or crm.db raw tables. Go through the scripts so the schema, classifiers, and upsert rules stay consistent.

Output

Output	Location
Raw post + engagers	revops/db/content.db (content_posts, content_interactions)
Unified CRM view	revops/db/crm.db (raw_content_*, stg_people, mart_people)
Attio records (optional)	Pushed via crm_to_attio_content.py
Extruct people table (optional)	https://app.extruct.ai/tables/{table_id}