# Salesforce Leadgenius > Export LeadGenius leads and import them into Salesforce with custom AI fields, FLS deployment, and automated scripts. Covers Connected App setup, field creation, data mapping, and verification. - Skill: `thierryteisseire/salesforce-leadgenius` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add thierryteisseire/salesforce-leadgenius` - Raw SKILL.md: https://api.skillmd.com/api/skills/thierryteisseire/salesforce-leadgenius/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thierryteisseire (https://skillmd.com/u/thierryteisseire) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/thierryteisseire/salesforce-leadgenius --- # LeadGenius → Salesforce Import Guide > How to export leads from LeadGenius and import them into a Salesforce org, with custom fields for all AI insights and enrichment data. --- ## Table of Contents 0. [Overview](#0-overview) 1. [Prerequisites](#1-prerequisites) 2. [Creating the Salesforce Connected App](#2-creating-the-salesforce-connected-app) 3. [Exporting from LeadGenius](#3-exporting-from-leadgenius) 4. [LeadGenius CSV Field Reference](#4-leadgenius-csv-field-reference) 5. [Creating Custom Fields in Salesforce](#5-creating-custom-fields-in-salesforce) 6. [Deploying Field-Level Security](#6-deploying-field-level-security) 7. [Importing Leads](#7-importing-leads) 8. [Populating Custom Fields (Update Pass)](#8-populating-custom-fields-update-pass) 9. [Creating the List View](#9-creating-the-list-view) 10. [Verification](#10-verification) 11. [Complete Automated Script](#11-complete-automated-script) 12. [Troubleshooting](#12-troubleshooting) --- ## 0. Overview ### What This Does ``` LeadGenius CSV → Salesforce Leads 120 columns with custom AI fields + List View + AI Insights visible ``` ### Architecture LeadGenius exports a CSV with up to **120 columns** per lead. Most Salesforce orgs have a handful of standard Lead fields. This guide bridges the gap by: 1. **Creating 17 custom fields** on the Lead object to hold AI and enrichment data 2. **Granting field-level security** so the API can read/write them 3. **Importing leads** with standard field mapping (name, email, company, etc.) 4. **Updating leads** with AI data in dedicated custom fields 5. **Creating a List View** that surfaces AI Score, Qualification, and Seniority ### API Strategy Summary | Operation | API Used | Why | |-----------|----------|-----| | Authentication | OAuth2 Password Grant | Only method that works reliably | | Create custom fields | Tooling API | Creates fields in metadata instantly | | Grant FLS permissions | Metadata SOAP Deploy | Makes fields visible to REST API | | Insert/Update leads | REST Data API | Standard CRUD operations | | Create List View | ❌ Manual | API calls are silently ignored in some orgs | --- ## 1. Prerequisites ### Software ```bash pip3 install requests python-dotenv ``` ### Salesforce Org - An active Salesforce org (Developer, sandbox, or production) - Admin-level user account - A Connected App configured (see Section 2) ### LeadGenius - An active LeadGenius account (https://last.leadgenius.app) - Leads exported to CSV from the LeadGenius UI ### Environment File Create `.env` in your working directory: ```env # Salesforce Connected App credentials SALESFORCE_CONSUMER_KEY=your_consumer_key_here SALESFORCE_CONSUMER_SECRET=your_consumer_secret_here # Salesforce user credentials SALESFORCE_USERNAME=your-user@salesforce.com SALESFORCE_PASSWORD=YourPassword123 SALESFORCE_SECURITY_TOKEN=YourSecurityToken # Salesforce org URL SALESFORCE_INSTANCE_URL=https://your-org.develop.my.salesforce.com ``` --- ## 2. Creating the Salesforce Connected App A **Connected App** provides the Consumer Key / Consumer Secret needed for API authentication. This is a one-time setup per Salesforce org. ### 2.1 Navigate to App Manager 1. Log in to Salesforce 2. Click **⚙️ gear** (top right) → **Setup** 3. Quick Find → type **App Manager** 4. Click **App Manager** > 🇫🇷 **French UI:** Gear opens "Configuration". Search "Gestionnaire d'applications". ### 2.2 Create a New Connected App 1. Click **New Connected App** (top right) 2. Fill in Basic Information: | Field | Value | |-------|-------| | Connected App Name | `leadgenius` | | API Name | `leadgenius` | | Contact Email | your email | ### 2.3 Configure OAuth Settings 1. Check ✅ **Enable OAuth Settings** 2. Set **Callback URL**: `https://login.salesforce.com/services/oauth2/callback` 3. Add these **OAuth Scopes**: - `Full access (full)` - `Perform requests at any time (refresh_token, offline_access)` - `Access and manage your data (api)` > 🇫🇷 **French:** "Accès complet (full)", "Effectuer des requêtes à tout moment", "Accéder à vos données et les gérer" 4. Click **Save** → Click **Continue** (wait message) ### 2.4 Get Consumer Key and Consumer Secret 1. On the Connected App detail page, click **Manage Consumer Details** 2. You may need to verify your identity (email code) 3. Copy and save: - **Consumer Key** → `SALESFORCE_CONSUMER_KEY` - **Consumer Secret** → `SALESFORCE_CONSUMER_SECRET` > ⚠️ Save these immediately — Consumer Secret is only shown once. ### 2.5 Get Your Security Token 1. Click your **avatar** (top right) → **Settings** 2. Left sidebar → **My Personal Information** → **Reset My Security Token** 3. Click **Reset Security Token** 4. Check your email → copy the token → `SALESFORCE_SECURITY_TOKEN` > 🇫🇷 **French:** "Paramètres" → "Mes informations personnelles" → "Réinitialiser mon jeton de sécurité" > ⚠️ Every time you change your Salesforce password, the security token is also invalidated. ### 2.6 Enable Password Grant (If Needed) Some orgs block the OAuth Password Grant by default: 1. **Setup** → Quick Find: **OAuth and OpenID Connect Settings** 2. Enable **"Allow OAuth Username-Password Flows"** > 🇫🇷 "Paramètres OAuth et OpenID Connect" → "Autoriser les flux nom d'utilisateur-mot de passe OAuth" If disabled, you'll get: `{"error": "unsupported_grant_type"}` ### 2.7 Relax IP Restrictions (If Needed) If you get `INVALID_LOGIN` with correct credentials: 1. **Setup** → **App Manager** → Find `leadgenius` → **▼** → **Manage** 2. Click **Edit Policies** 3. Under **IP Relaxation**: select **"Relax IP restrictions"** 4. **Save** ### 2.8 Verify Connection ```python import requests, os from dotenv import load_dotenv load_dotenv() data = { 'grant_type': 'password', 'client_id': os.getenv('SALESFORCE_CONSUMER_KEY'), 'client_secret': os.getenv('SALESFORCE_CONSUMER_SECRET'), 'username': os.getenv('SALESFORCE_USERNAME'), 'password': os.getenv('SALESFORCE_PASSWORD') + os.getenv('SALESFORCE_SECURITY_TOKEN'), } r = requests.post('https://login.salesforce.com/services/oauth2/token', data=data) result = r.json() if 'access_token' in result: print(f"✅ Connected to {result['instance_url']}") else: print(f"❌ Auth failed: {result}") ``` ### Troubleshooting | Symptom | Cause | Fix | |---------|-------|-----| | `invalid_client_id` | Wrong Consumer Key | Re-copy from Connected App | | `invalid_client` | Wrong Consumer Secret | Reset in Manage Consumer Details | | `invalid_grant` | Wrong password+token | Password + Security Token concatenated, no separator | | `unsupported_grant_type` | Password grant disabled | Enable in OAuth settings (2.6) | | `INVALID_LOGIN` | IP restriction | Relax IP restrictions (2.7) | --- ## 3. Exporting from LeadGenius ### From the LeadGenius UI 1. Go to https://last.leadgenius.app 2. Navigate to your **Client** workspace 3. Select the leads you want to export (or select all) 4. Click **Export** → **CSV** 5. Save the CSV file to your working directory ### From the LeadGenius API ```bash # Authenticate python3 scripts/auth.py --email your@email.com # Export all leads for a client python3 scripts/lgp.py leads list --client-id your-client-slug --limit 5000 --format csv > leads.csv ``` ### CSV Filename Convention Name your file descriptively: ``` client-name-MMYYYY.csv # Example: prophix-demo-02026.csv ``` --- ## 4. LeadGenius CSV Field Reference The LeadGenius CSV contains up to 120 columns. Here are the ones we map to Salesforce: ### Standard Lead Fields (→ Salesforce Standard Fields) | LeadGenius CSV Column | Salesforce Field | Type | Notes | |-----------------------|------------------|------|-------| | `First Name` | `FirstName` | Text | | | `Last Name` | `LastName` | Text | Required — use "Unknown" if empty | | `Title` | `Title` | Text | | | `Email` | `Email` | Email | | | `Phone Number` | `Phone` | Phone | | | `Company Name` | `Company` | Text | Required — use "Unknown" if empty | | `Industry` | `Industry` | Picklist | Skip if "N/A" | | `Estimated Num Employees` | `NumberOfEmployees` | Number | | | `Country` | `Country` | Text | | | `City` | `City` | Text | | | `Lead Source` | `LeadSource` | Text | Override with your campaign name | ### ⚠️ Fields to SKIP | CSV Column | Why | |------------|-----| | `State` | French region names (e.g., "Île-de-France") cause Salesforce validation errors. **Always omit.** | | `Status` | Conflicts with Salesforce Lead Status picklist values. Set your own. | ### AI Insight Fields (→ Salesforce Custom Fields) | LeadGenius CSV Column | Salesforce Custom Field | Type | |-----------------------|------------------------|------| | `Ai Score Value` | `LG_AI_Score__c` | Number(5,0) | | `Ai Lead Score Score` | `LG_Lead_Score__c` | Number(5,0) | | `Ai Qualification` | `LG_Qualification__c` | Text(255) | | `Ai Score Justification` | `LG_Justification__c` | LongTextArea(32000) | | `Ai Next Action` | `LG_Next_Action__c` | LongTextArea(32000) | | `Ai Cold Email` | `LG_Cold_Email__c` | LongTextArea(32000) | | `Ai Linkedin Connect` | `LG_LinkedIn_Connect__c` | LongTextArea(32000) | | `Ai Decision Maker Role` | `LG_Decision_Role__c` | Text(255) | | `Is Likely To Engage` | `LG_Likely_Engage__c` | Text(10) | ### Enrichment Fields (→ Salesforce Custom Fields) | LeadGenius CSV Column | Salesforce Custom Field | Type | |-----------------------|------------------------|------| | `Linkedin Url` | `LG_LinkedIn_URL__c` | URL | | `Company Linkedin Url` | `LG_Company_LinkedIn__c` | URL | | `Seniority` | `LG_Seniority__c` | Text(100) | | `Departments` | `LG_Departments__c` | Text(255) | | `Enrichment5 Engagement Rate` | `LG_Engagement_Rate__c` | Number(10,1) | | `Enrichment5 Total Engagements` | `LG_Total_Engagements__c` | Number(10,0) | | `Lead Id` | `LG_Lead_ID__c` | Text(255) — External ID | | `Enrichment Source` | `LG_Enrichment_Source__c` | Text(255) | ### Fields We Put in Description (Too Large for Individual Fields) Some LeadGenius fields contain structured multi-paragraph content. We aggregate these into the standard `Description` field: | CSV Column | Content | |------------|---------| | `Ai Score Justification` | Full AI analysis (verdict, context, recommendations) | | `Ai Next Action` | Next steps with tech stack analysis | | `Ai Cold Email` | Personalized outreach draft | | `Company Analysis Result` | Company deep-dive analysis | | `Value Proposition` | Tailored value proposition | --- ## 5. Creating Custom Fields in Salesforce ### The 17 Custom Fields All field API names are prefixed with `LG_` (LeadGenius) to avoid conflicts with other integrations. ```python TOOLING = f'{instance_url}/services/data/v62.0/tooling' LEADGENIUS_FIELDS = [ # AI Scores {"FullName": "Lead.LG_AI_Score__c", "Metadata": { "label": "LG AI Score", "type": "Number", "precision": 5, "scale": 0}}, {"FullName": "Lead.LG_Lead_Score__c", "Metadata": { "label": "LG Lead Score", "type": "Number", "precision": 5, "scale": 0}}, # AI Text Analysis {"FullName": "Lead.LG_Qualification__c", "Metadata": { "label": "LG Qualification", "type": "Text", "length": 255}}, {"FullName": "Lead.LG_Justification__c", "Metadata": { "label": "LG Justification", "type": "LongTextArea", "length": 32000, "visibleLines": 5}}, {"FullName": "Lead.LG_Next_Action__c", "Metadata": { "label": "LG Next Action", "type": "LongTextArea", "length": 32000, "visibleLines": 3}}, {"FullName": "Lead.LG_Cold_Email__c", "Metadata": { "label": "LG Cold Email", "type": "LongTextArea", "length": 32000, "visibleLines": 5}}, {"FullName": "Lead.LG_LinkedIn_Connect__c", "Metadata": { "label": "LG LinkedIn Connect", "type": "LongTextArea", "length": 32000, "visibleLines": 3}}, {"FullName": "Lead.LG_Decision_Role__c", "Metadata": { "label": "LG Decision Maker Role", "type": "Text", "length": 255}}, {"FullName": "Lead.LG_Likely_Engage__c", "Metadata": { "label": "LG Likely to Engage", "type": "Text", "length": 10}}, # LinkedIn / Enrichment {"FullName": "Lead.LG_LinkedIn_URL__c", "Metadata": { "label": "LG LinkedIn URL", "type": "Url"}}, {"FullName": "Lead.LG_Company_LinkedIn__c", "Metadata": { "label": "LG Company LinkedIn", "type": "Url"}}, {"FullName": "Lead.LG_Seniority__c", "Metadata": { "label": "LG Seniority", "type": "Text", "length": 100}}, {"FullName": "Lead.LG_Departments__c", "Metadata": { "label": "LG Departments", "type": "Text", "length": 255}}, {"FullName": "Lead.LG_Engagement_Rate__c", "Metadata": { "label": "LG Engagement Rate", "type": "Number", "precision": 10, "scale": 1}}, {"FullName": "Lead.LG_Total_Engagements__c", "Metadata": { "label": "LG Total Engagements", "type": "Number", "precision": 10, "scale": 0}}, # Tracking {"FullName": "Lead.LG_Lead_ID__c", "Metadata": { "label": "LG Lead ID", "type": "Text", "length": 255, "unique": True, "externalId": True}}, {"FullName": "Lead.LG_Enrichment_Source__c", "Metadata": { "label": "LG Enrichment Source", "type": "Text", "length": 255}}, ] ``` ### How to Create Them ```python import requests, os, time from dotenv import load_dotenv load_dotenv() # Authenticate (see Section 2.8) token, url = authenticate() headers = {'Authorization': f'Bearer {token}', 'Content-Type': 'application/json'} TOOLING = f'{url}/services/data/v62.0/tooling' created = 0 skipped = 0 for fd in LEADGENIUS_FIELDS: fname = fd['FullName'].split('.')[1] r = requests.post(f'{TOOLING}/sobjects/CustomField', headers=headers, json=fd) if r.status_code == 201: created += 1 print(f" ✅ {fname} created") elif r.status_code == 400 and 'DUPLICATE' in r.text: skipped += 1 print(f" ⏭️ {fname} already exists") else: print(f" ❌ {fname}: {r.text[:100]}") time.sleep(0.5) # Avoid rate limits print(f"\n✅ Created: {created} | ⏭️ Skipped: {skipped}") ``` ### ⚠️ Critical: Fields Are NOT Usable Yet After creation via Tooling API, the fields exist in Salesforce metadata but are **invisible to the REST Data API**. You MUST deploy Field-Level Security (Section 6) before importing data. **How to verify:** ```python r = requests.get(f'{url}/services/data/v62.0/sobjects/Lead/describe', headers=headers) visible = [f['name'] for f in r.json()['fields'] if f['name'].startswith('LG_')] print(f"Visible LG fields: {len(visible)}") # If 0 → FLS not deployed yet (expected at this stage) ``` --- ## 6. Deploying Field-Level Security This step grants the Admin profile read/write access to all LeadGenius custom fields. ### Why This Is Needed The Tooling API creates fields in metadata, but they're **gated by Field-Level Security (FLS)**. Without FLS, the REST API returns `No such column` when you try to use them. ### Deploy FLS via Metadata SOAP API ```python import requests, os, base64, zipfile, io, time import xml.etree.ElementTree as ET from dotenv import load_dotenv load_dotenv() token, url = authenticate() metadata_url = f'{url}/services/Soap/m/62.0' # All LG_ field names lg_fields = [ 'LG_AI_Score__c', 'LG_Lead_Score__c', 'LG_Qualification__c', 'LG_Justification__c', 'LG_Next_Action__c', 'LG_Cold_Email__c', 'LG_LinkedIn_Connect__c', 'LG_Decision_Role__c', 'LG_Likely_Engage__c', 'LG_LinkedIn_URL__c', 'LG_Company_LinkedIn__c', 'LG_Seniority__c', 'LG_Departments__c', 'LG_Engagement_Rate__c', 'LG_Total_Engagements__c', 'LG_Lead_ID__c', 'LG_Enrichment_Source__c', ] # Build Profile XML perms = ''.join([f''' Lead.{f} true true ''' for f in lg_fields]) profile_xml = f''' {perms} ''' package_xml = ''' AdminProfile 62.0 ''' # ZIP it buf = io.BytesIO() with zipfile.ZipFile(buf, 'w', zipfile.ZIP_DEFLATED) as zf: zf.writestr('package.xml', package_xml) zf.writestr('profiles/Admin.profile', profile_xml) zip_data = base64.b64encode(buf.getvalue()).decode() # Deploy via SOAP soap = f''' {token} {zip_data} false true false true ''' r = requests.post(metadata_url, data=soap.encode(), headers={'Content-Type': 'text/xml; charset=utf-8', 'SOAPAction': 'deploy'}) # Extract deploy ID and poll (see helper function below) deploy_id = extract_deploy_id(r.text) success = poll_deploy_status(deploy_id, token, metadata_url) ``` ### Helper Functions ```python def extract_deploy_id(response_text): """Extract the 18-char deploy ID from SOAP response""" for elem in ET.fromstring(response_text).iter(): if 'id' in elem.tag.lower() and elem.text and len(elem.text) == 18: return elem.text return None def poll_deploy_status(deploy_id, token, metadata_url, max_attempts=20): """Poll until deploy completes. Returns True on success.""" for attempt in range(max_attempts): time.sleep(3) check = f''' {token} {deploy_id} true ''' r = requests.post(metadata_url, data=check.encode(), headers={'Content-Type': 'text/xml; charset=utf-8', 'SOAPAction': 'checkDeployStatus'}) done = ok = False for elem in ET.fromstring(r.text).iter(): tag = elem.tag.split('}')[-1] if '}' in elem.tag else elem.tag if tag == 'done' and elem.text == 'true': done = True if tag == 'success' and elem.text == 'true': ok = True if done: print(f" {'✅ FLS deployed!' if ok else '❌ FLS deploy failed'}") return ok print(f" ⏳ Attempt {attempt+1}...") return False ``` ### Verify FLS Is Working After deployment, wait 3-5 seconds, then verify: ```python time.sleep(5) r = requests.get(f'{url}/services/data/v62.0/sobjects/Lead/describe', headers=headers) visible = [f['name'] for f in r.json()['fields'] if f['name'].startswith('LG_')] print(f"✅ Visible LG fields: {len(visible)}/17") for f in sorted(visible): print(f" ✅ {f}") ``` **Expected:** 17/17 fields visible. If 0/17, the deploy failed or hasn't propagated yet — wait and retry. --- ## 7. Importing Leads ### Field Mapping ```python import csv def safe_str(val, mx=255): """Clean string for Salesforce""" if not val or str(val).strip() in ('N/A', '(empty)', 'None', ''): return None return str(val).strip()[:mx] or None def safe_long(val, mx=32000): """Clean long text for Salesforce""" if not val or str(val).strip() in ('N/A', '(empty)', 'None', ''): return None return str(val).strip()[:mx] or None def safe_num(val): """Clean number for Salesforce""" if not val or str(val).strip() in ('N/A', '(empty)', 'None', ''): return None try: return float(str(val).strip()) except: return None def build_lead(row, campaign_name, available_fields): """Build a Salesforce Lead record from a LeadGenius CSV row""" lead = { # Standard fields 'FirstName': safe_str(row.get('First Name'), 40), 'LastName': safe_str(row.get('Last Name'), 80) or 'Unknown', 'Title': safe_str(row.get('Title')), 'Email': safe_str(row.get('Email')), 'Phone': safe_str(row.get('Phone Number')), 'Company': safe_str(row.get('Company Name')) or 'Unknown', 'Industry': safe_str(row.get('Industry')), 'NumberOfEmployees': int(safe_num(row.get('Estimated Num Employees')) or 0) or None, 'Country': safe_str(row.get('Country')), 'City': safe_str(row.get('City')), 'LeadSource': campaign_name, 'Status': 'Open - Not Contacted', # ⚠️ NO State field — French regions cause validation errors } # Custom LG_ fields (only if FLS is deployed) lg_mapping = { 'LG_AI_Score__c': safe_num(row.get('Ai Score Value')), 'LG_Lead_Score__c': safe_num(row.get('Ai Lead Score Score')), 'LG_Qualification__c': safe_str(row.get('Ai Qualification')), 'LG_Justification__c': safe_long(row.get('Ai Score Justification')), 'LG_Next_Action__c': safe_long(row.get('Ai Next Action')), 'LG_Cold_Email__c': safe_long(row.get('Ai Cold Email')), 'LG_LinkedIn_Connect__c': safe_long(row.get('Ai Linkedin Connect')), 'LG_Decision_Role__c': safe_str(row.get('Ai Decision Maker Role')), 'LG_Likely_Engage__c': safe_str(row.get('Is Likely To Engage'), 10), 'LG_LinkedIn_URL__c': safe_str(row.get('Linkedin Url')), 'LG_Company_LinkedIn__c': safe_str(row.get('Company Linkedin Url')), 'LG_Seniority__c': safe_str(row.get('Seniority'), 100), 'LG_Departments__c': safe_str(row.get('Departments')), 'LG_Engagement_Rate__c': safe_num(row.get('Enrichment5 Engagement Rate')), 'LG_Total_Engagements__c': safe_num(row.get('Enrichment5 Total Engagements')), 'LG_Lead_ID__c': safe_str(row.get('Lead Id')), 'LG_Enrichment_Source__c': safe_str(row.get('Enrichment Source')), } for fname, val in lg_mapping.items(): if val is not None and fname in available_fields: lead[fname] = val # Build Description from AI analysis (always works, no custom fields needed) desc_parts = [] justif = safe_long(row.get('Ai Score Justification')) if justif: desc_parts.append(f"=== AI ANALYSIS ===\n{justif}") next_act = safe_long(row.get('Ai Next Action')) if next_act: desc_parts.append(f"=== NEXT ACTION ===\n{next_act}") cold_email = safe_long(row.get('Ai Cold Email')) if cold_email: desc_parts.append(f"=== COLD EMAIL DRAFT ===\n{cold_email}") company_analysis = safe_long(row.get('Company Analysis Result')) if company_analysis: desc_parts.append(f"=== COMPANY ANALYSIS ===\n{company_analysis}") value_prop = safe_long(row.get('Value Proposition')) if value_prop: desc_parts.append(f"=== VALUE PROPOSITION ===\n{value_prop}") if desc_parts: lead['Description'] = '\n\n'.join(desc_parts)[:32000] # Remove None values return {k: v for k, v in lead.items() if v is not None} ``` ### Import Loop ```python # Get available fields (respects FLS) r = requests.get(f'{API}/sobjects/Lead/describe', headers=headers) available_fields = [f['name'] for f in r.json()['fields']] # Read CSV with open('your-leads.csv', 'r', encoding='utf-8-sig') as f: rows = list(csv.DictReader(f)) campaign_name = "LeadGenius Import" # Your campaign identifier success = 0 errors = 0 for i, row in enumerate(rows): lead = build_lead(row, campaign_name, available_fields) r = requests.post(f'{API}/sobjects/Lead', headers=headers, json=lead) name = f"{row.get('First Name','')} {row.get('Last Name','')}".strip() if r.status_code in [200, 201]: success += 1 if success % 25 == 0 or success <= 2: score = row.get('Ai Score Value', '-') print(f" ✅ {success:3d}/{len(rows)} | {name:30s} | Score: {score}") else: errors += 1 msg = r.json()[0]['message'][:80] if isinstance(r.json(), list) else r.text[:80] if errors <= 5: print(f" ❌ {name}: {msg}") print(f"\n✅ Imported: {success} | ❌ Errors: {errors}") ``` --- ## 8. Populating Custom Fields (Update Pass) If the custom fields weren't ready during initial import (FLS deployed after first import), run a second pass to populate them: ```python # Get all SF leads by email r = requests.get(f'{API}/query', params={'q': 'SELECT Id, Email FROM Lead'}, headers=headers) sf_leads = {} for rec in r.json().get('records', []): if rec.get('Email'): sf_leads[rec['Email'].lower()] = rec['Id'] # Read CSV and update updated = 0 for row in rows: email = safe_str(row.get('Email')) if not email or email.lower() not in sf_leads: continue lead_id = sf_leads[email.lower()] update = {} lg_mapping = { 'LG_AI_Score__c': safe_num(row.get('Ai Score Value')), 'LG_Lead_Score__c': safe_num(row.get('Ai Lead Score Score')), 'LG_Qualification__c': safe_str(row.get('Ai Qualification')), 'LG_Justification__c': safe_long(row.get('Ai Score Justification')), 'LG_Next_Action__c': safe_long(row.get('Ai Next Action')), 'LG_Cold_Email__c': safe_long(row.get('Ai Cold Email')), # ... all LG_ fields ... } for fname, val in lg_mapping.items(): if val is not None and fname in available_fields: update[fname] = val if update: r = requests.patch(f'{API}/sobjects/Lead/{lead_id}', headers=headers, json=update) if r.status_code == 204: updated += 1 print(f"✅ Updated: {updated} leads with AI data") ``` --- ## 9. Creating the List View ### ❌ API Limitation List Views **cannot be created programmatically** in some Salesforce orgs (OrgFarm, scratch orgs, some sandboxes). The Metadata API reports success but the view is never created. ### ✅ Manual Steps (2 Minutes) 1. Go to the **Leads** tab in Salesforce 2. Click the **list view dropdown** → **New** 3. Name: **LeadGenius Leads** 4. Visibility: **All users can see this list view** 5. Click **Save** 6. Add **Filter**: `Lead Source` equals `LeadGenius Import` (or your campaign name) 7. Click the **⚙️ gear** → **Select Fields to Display** 8. Add these columns: | # | Column | API Name | |---|--------|----------| | 1 | Name | `FULL_NAME` | | 2 | Company | `LEAD_COMPANY` | | 3 | Title | `LEAD_TITLE` | | 4 | **LG AI Score** | `LG_AI_Score__c` | | 5 | **LG Qualification** | `LG_Qualification__c` | | 6 | **LG Seniority** | `LG_Seniority__c` | | 7 | Email | `LEAD_EMAIL` | | 8 | Phone | `LEAD_PHONE` | | 9 | City | `LEAD_CITY` | | 10 | Industry | `LEAD_INDUSTRY` | | 11 | **LG Likely to Engage** | `LG_Likely_Engage__c` | | 12 | **LG LinkedIn URL** | `LG_LinkedIn_URL__c` | 9. Click **Save** 10. Optionally **sort by LG AI Score** (descending) to see top leads first --- ## 10. Verification ### Quick Checks ```python # Total leads r = requests.get(f'{API}/query', params={'q': 'SELECT COUNT() FROM Lead'}, headers=headers) print(f"Total leads: {r.json()['totalSize']}") # Leads with AI Score r = requests.get(f'{API}/query', params={'q': 'SELECT COUNT() FROM Lead WHERE LG_AI_Score__c > 0'}, headers=headers) print(f"Leads with AI Score: {r.json()['totalSize']}") # Top 10 by AI Score r = requests.get(f'{API}/query', params={ 'q': '''SELECT FirstName, LastName, Company, LG_AI_Score__c, LG_Seniority__c, LG_Likely_Engage__c FROM Lead WHERE LG_AI_Score__c > 0 ORDER BY LG_AI_Score__c DESC LIMIT 10''' }, headers=headers) print("\n🏆 Top 10:") for rec in r.json()['records']: print(f" {rec.get('LG_AI_Score__c',0):5.0f} | " f"{rec.get('FirstName','')} {rec.get('LastName','')} | " f"{rec.get('Company','')} | " f"{rec.get('LG_Seniority__c','')}") # Verify custom fields are visible r = requests.get(f'{API}/sobjects/Lead/describe', headers=headers) lg = [f['name'] for f in r.json()['fields'] if f['name'].startswith('LG_')] print(f"\n📐 LG custom fields visible: {len(lg)}/17") ``` --- ## 11. Complete Automated Script Here's a ready-to-run script that does everything in one go: ```python """ LeadGenius → Salesforce Import Usage: python3 lg_to_sf.py --csv leads.csv --campaign "My Campaign" """ import requests, os, csv, time, base64, zipfile, io, argparse import xml.etree.ElementTree as ET from dotenv import load_dotenv load_dotenv() # ============= CONFIG ============= parser = argparse.ArgumentParser() parser.add_argument('--csv', required=True, help='Path to LeadGenius CSV') parser.add_argument('--campaign', default='LeadGenius Import', help='Lead Source / campaign name') parser.add_argument('--clear', action='store_true', help='Delete existing leads first') args = parser.parse_args() # ============= AUTH ============= def authenticate(): data = { 'grant_type': 'password', 'client_id': os.getenv('SALESFORCE_CONSUMER_KEY'), 'client_secret': os.getenv('SALESFORCE_CONSUMER_SECRET'), 'username': os.getenv('SALESFORCE_USERNAME'), 'password': os.getenv('SALESFORCE_PASSWORD') + os.getenv('SALESFORCE_SECURITY_TOKEN'), } r = requests.post('https://login.salesforce.com/services/oauth2/token', data=data) result = r.json() if 'access_token' not in result: raise Exception(f"Auth failed: {result}") return result['access_token'], result['instance_url'] token, url = authenticate() h = {'Authorization': f'Bearer {token}', 'Content-Type': 'application/json'} API = f'{url}/services/data/v62.0' TOOLING = f'{url}/services/data/v62.0/tooling' META = f'{url}/services/Soap/m/62.0' print(f"✅ Connected to {url}\n") # ============= HELPERS ============= def s(val, mx=255): if not val or str(val).strip() in ('N/A','(empty)','None',''): return None return str(val).strip()[:mx] or None def sl(val, mx=32000): if not val or str(val).strip() in ('N/A','(empty)','None',''): return None return str(val).strip()[:mx] or None def n(val): if not val or str(val).strip() in ('N/A','(empty)','None',''): return None try: return float(str(val).strip()) except: return None def extract_id(text): for elem in ET.fromstring(text).iter(): if 'id' in elem.tag.lower() and elem.text and len(elem.text) == 18: return elem.text return None def poll(deploy_id): for att in range(20): time.sleep(3) soap = f''' {token} {deploy_id}true ''' r = requests.post(META, data=soap.encode(), headers={'Content-Type':'text/xml; charset=utf-8','SOAPAction':'checkDeployStatus'}) done = ok = False for elem in ET.fromstring(r.text).iter(): tag = elem.tag.split('}')[-1] if '}' in elem.tag else elem.tag if tag == 'done' and elem.text == 'true': done = True if tag == 'success' and elem.text == 'true': ok = True if done: return ok return False # ============= STEP 1: OPTIONAL CLEAR ============= if args.clear: print("STEP 1: CLEARING LEADS") while True: r = requests.get(f'{API}/query', params={'q': 'SELECT Id FROM Lead LIMIT 200'}, headers=h) recs = r.json().get('records', []) if not recs: break ids = ','.join([x['Id'] for x in recs]) requests.delete(f'{API}/composite/sobjects?ids={ids}&allOrNone=false', headers=h) print(" ✅ Done\n") # ============= STEP 2: CREATE FIELDS ============= print("STEP 2: CREATING CUSTOM FIELDS") FIELDS = [ {"FullName":"Lead.LG_AI_Score__c","Metadata":{"label":"LG AI Score","type":"Number","precision":5,"scale":0}}, {"FullName":"Lead.LG_Lead_Score__c","Metadata":{"label":"LG Lead Score","type":"Number","precision":5,"scale":0}}, {"FullName":"Lead.LG_Qualification__c","Metadata":{"label":"LG Qualification","type":"Text","length":255}}, {"FullName":"Lead.LG_Justification__c","Metadata":{"label":"LG Justification","type":"LongTextArea","length":32000,"visibleLines":5}}, {"FullName":"Lead.LG_Next_Action__c","Metadata":{"label":"LG Next Action","type":"LongTextArea","length":32000,"visibleLines":3}}, {"FullName":"Lead.LG_Cold_Email__c","Metadata":{"label":"LG Cold Email","type":"LongTextArea","length":32000,"visibleLines":5}}, {"FullName":"Lead.LG_LinkedIn_Connect__c","Metadata":{"label":"LG LinkedIn Connect","type":"LongTextArea","length":32000,"visibleLines":3}}, {"FullName":"Lead.LG_Decision_Role__c","Metadata":{"label":"LG Decision Maker Role","type":"Text","length":255}}, {"FullName":"Lead.LG_Likely_Engage__c","Metadata":{"label":"LG Likely to Engage","type":"Text","length":10}}, {"FullName":"Lead.LG_LinkedIn_URL__c","Metadata":{"label":"LG LinkedIn URL","type":"Url"}}, {"FullName":"Lead.LG_Company_LinkedIn__c","Metadata":{"label":"LG Company LinkedIn","type":"Url"}}, {"FullName":"Lead.LG_Seniority__c","Metadata":{"label":"LG Seniority","type":"Text","length":100}}, {"FullName":"Lead.LG_Departments__c","Metadata":{"label":"LG Departments","type":"Text","length":255}}, {"FullName":"Lead.LG_Engagement_Rate__c","Metadata":{"label":"LG Engagement Rate","type":"Number","precision":10,"scale":1}}, {"FullName":"Lead.LG_Total_Engagements__c","Metadata":{"label":"LG Total Engagements","type":"Number","precision":10,"scale":0}}, {"FullName":"Lead.LG_Lead_ID__c","Metadata":{"label":"LG Lead ID","type":"Text","length":255,"unique":True,"externalId":True}}, {"FullName":"Lead.LG_Enrichment_Source__c","Metadata":{"label":"LG Enrichment Source","type":"Text","length":255}}, ] for fd in FIELDS: fname = fd['FullName'].split('.')[1] r = requests.post(f'{TOOLING}/sobjects/CustomField', headers=h, json=fd) if r.status_code == 201: print(f" ✅ {fname}") elif 'DUPLICATE' in r.text: print(f" ⏭️ {fname} (exists)") else: print(f" ❌ {fname}: {r.text[:80]}") time.sleep(0.5) # ============= STEP 3: DEPLOY FLS ============= print("\nSTEP 3: DEPLOYING FIELD-LEVEL SECURITY") lg_names = [fd['FullName'].split('.')[1] for fd in FIELDS] perms = ''.join([f''' Lead.{f}truetrue''' for f in lg_names]) prof = f'{perms}' pkg = 'AdminProfile62.0' buf = io.BytesIO() with zipfile.ZipFile(buf,'w',zipfile.ZIP_DEFLATED) as zf: zf.writestr('package.xml', pkg); zf.writestr('profiles/Admin.profile', prof) zd = base64.b64encode(buf.getvalue()).decode() soap = f'''{token}{zd}falsetruefalsetrue''' r = requests.post(META, data=soap.encode(), headers={'Content-Type':'text/xml; charset=utf-8','SOAPAction':'deploy'}) did = extract_id(r.text) ok = poll(did) print(f" {'✅ FLS deployed' if ok else '❌ FLS failed'}") # ============= STEP 4: VERIFY FIELDS ============= print("\nSTEP 4: VERIFYING FIELDS") time.sleep(5) r = requests.get(f'{API}/sobjects/Lead/describe', headers=h) avail = [f['name'] for f in r.json()['fields']] lg_visible = [f for f in avail if f.startswith('LG_')] print(f" LG fields visible: {len(lg_visible)}/17") # ============= STEP 5: IMPORT LEADS ============= print(f"\nSTEP 5: IMPORTING LEADS FROM {args.csv}") with open(args.csv, 'r', encoding='utf-8-sig') as f: rows = list(csv.DictReader(f)) print(f" {len(rows)} leads to import\n") ok_count = err_count = 0 for i, row in enumerate(rows): lead = { 'FirstName': s(row.get('First Name'),40), 'LastName': s(row.get('Last Name'),80) or 'Unknown', 'Title': s(row.get('Title')), 'Email': s(row.get('Email')), 'Phone': s(row.get('Phone Number')), 'Company': s(row.get('Company Name')) or 'Unknown', 'Industry': s(row.get('Industry')), 'NumberOfEmployees': int(n(row.get('Estimated Num Employees')) or 0) or None, 'Country': s(row.get('Country')), 'City': s(row.get('City')), 'LeadSource': args.campaign, 'Status': 'Open - Not Contacted', } # Custom fields lg_map = { 'LG_AI_Score__c': n(row.get('Ai Score Value')), 'LG_Lead_Score__c': n(row.get('Ai Lead Score Score')), 'LG_Qualification__c': s(row.get('Ai Qualification')), 'LG_Justification__c': sl(row.get('Ai Score Justification')), 'LG_Next_Action__c': sl(row.get('Ai Next Action')), 'LG_Cold_Email__c': sl(row.get('Ai Cold Email')), 'LG_LinkedIn_Connect__c': sl(row.get('Ai Linkedin Connect')), 'LG_Decision_Role__c': s(row.get('Ai Decision Maker Role')), 'LG_Likely_Engage__c': s(row.get('Is Likely To Engage'),10), 'LG_LinkedIn_URL__c': s(row.get('Linkedin Url')), 'LG_Company_LinkedIn__c': s(row.get('Company Linkedin Url')), 'LG_Seniority__c': s(row.get('Seniority'),100), 'LG_Departments__c': s(row.get('Departments')), 'LG_Engagement_Rate__c': n(row.get('Enrichment5 Engagement Rate')), 'LG_Total_Engagements__c': n(row.get('Enrichment5 Total Engagements')), 'LG_Lead_ID__c': s(row.get('Lead Id')), 'LG_Enrichment_Source__c': s(row.get('Enrichment Source')), } for fname, val in lg_map.items(): if val is not None and fname in avail: lead[fname] = val # Description parts = [] j = sl(row.get('Ai Score Justification')) if j: parts.append(f"=== AI ANALYSIS ===\n{j}") na = sl(row.get('Ai Next Action')) if na: parts.append(f"=== NEXT ACTION ===\n{na}") ce = sl(row.get('Ai Cold Email')) if ce: parts.append(f"=== COLD EMAIL ===\n{ce}") if parts: lead['Description'] = '\n\n'.join(parts)[:32000] lead = {k:v for k,v in lead.items() if v is not None} r = requests.post(f'{API}/sobjects/Lead', headers=h, json=lead) nm = f"{row.get('First Name','')} {row.get('Last Name','')}".strip() if r.status_code in [200,201]: ok_count += 1 if ok_count % 25 == 0 or ok_count <= 2: sc = row.get('Ai Score Value','-') print(f" ✅ {ok_count:3d}/{len(rows)} | {nm:30s} | Score: {sc}") else: err_count += 1 …(truncated)