post-engagers

# 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. ```sql 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`. ```bash 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). ```bash 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: ```sql 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 ```bash 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: ```bash 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: ```json { "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: ```sql -- 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}` |