Exa Websets MCP Server

/** * Hiring Orchestration: Recruiter and Job Seeker Workflows * Helps recruiters source candidates and job seekers find opportunities */ export async function hiringOrchestration( mode: "recruiter" | "job_seeker", role: string, location?: string, seniority?: string, keywords?: string ): Promise<string> { const geo = location || "remote/global"; const level = seniority || "mid-level to senior"; const tags = keywords || "leadership, technical mentoring, innovation"; if (mode === "recruiter") { return recruiterWorkflow(role, geo, level, tags); } else { return jobSeekerWorkflow(role, geo, level, tags); } } function recruiterWorkflow(role: string, location: string, seniority: string, keywords: string): string { return `# 🎯 Hiring Orchestration: Recruiter - Passive Candidate Sourcing ## What This Solves Find passive candidates who fit your role requirements by analyzing their online presence, technical skills, and career trajectory. This orchestration helps recruiters: - **Passive Candidate Sourcing**: Discover engineers, PMs, sales professionals in your target market - **Skill Verification**: Confirm technical expertise through projects, open-source contributions, publications - **Career Trajectory**: Identify who's ready for the next level - **Cultural Fit Assessment**: Find candidates with values alignment - **Contact Discovery**: Extract email patterns and LinkedIn profiles - **Outreach Personalization**: Build targeted messaging based on their interests ## When to Use ✅ Recruiting for hard-to-fill technical roles ✅ Building talent pipelines for upcoming openings ✅ Competitive poaching and counterintelligence ✅ Referral program sourcing ✅ Senior leadership recruitment ## 🎯 TL;DR Quick Steps 1. 🔍 **Search**: Find passive candidates with your target skills/background 2. 🧰 **Create Webset**: Organize candidate profiles and mentions 3. ✨ **Enrich**: Extract skills, experience, and contact info 4. 📊 **Score**: Rank by fit and seniority 5. 📧 **Outreach**: Build personalized recruiting sequences --- ## 📋 Step-by-Step Workflow ### Step 1: Search for Passive Candidates Search for people with your target role expertise, skills, and career signals. **Tool**: \`web_search_exa\` **Parameters**: \`\`\`json { "query": "${role} engineer (${keywords}) location:${location} site:github.com OR site:linkedin.com OR site:twitter.com OR site:medium.com", "numResults": 75, "timeRange": "365d", "waitForResults": true } \`\`\` **Expected Output**: 30-75 results with GitHub profiles, LinkedIn updates, blog posts, and social mentions of candidates matching your criteria. --- ### Step 2: Create a Candidate Webset Organize candidate profiles into a persistent collection for tracking and enrichment. **Tool**: \`websets_manager\` **Parameters**: \`\`\`json { "operation": "create_webset_from_search", "name": "${role} Candidates - Sourcing Pool (${seniority})", "description": "Passive candidates for ${role} role. Seniority: ${seniority}. Location: ${location}. Key skills: ${keywords}. Profiles from GitHub, LinkedIn, blogs, and technical communities.", "searchQuery": "${role} engineer (${keywords}) location:${location} site:github.com OR site:linkedin.com OR site:twitter.com OR site:medium.com", "timeRange": "365d", "maxResults": 200 } \`\`\` **Expected Output**: \`\`\`json { "websetId": "ws_candidates_789", "status": "processing", "createdAt": "2024-01-10T09:00:00Z", "estimatedCompletion": "2024-01-10T09:40:00Z" } \`\`\` --- ### Step 3: Check Webset Status Monitor until processing completes (usually 20-40 minutes). **Tool**: \`websets_manager\` **Parameters**: \`\`\`json { "operation": "get_webset_status", "websetId": "ws_candidates_789" } \`\`\` **Expected Output**: \`\`\`json { "websetId": "ws_candidates_789", "status": "completed", "itemCount": 156, "completedAt": "2024-01-10T09:38:00Z" } \`\`\` --- ### Step 4: Extract Skills and Experience Data Retrieve candidate profiles and extract skills, experience, and seniority indicators. **Tool**: \`websets_manager\` **Parameters** (Get items): \`\`\`json { "operation": "list_items", "websetId": "ws_candidates_789", "limit": 150, "offset": 0 } \`\`\` **Expected Output**: Array of 150 candidate items with \`url\`, \`title\`, \`text\`, \`source\` (GitHub/LinkedIn/blog). **Tool**: \`websets_manager\` **Parameters** (Enrich with skill extraction): \`\`\`json { "operation": "enhance_content", "websetId": "ws_candidates_789", "enhancements": [ "extract_entities", "extract_skills", "summarize", "classify_experience_level" ], "waitForResults": true, "advanced": { "outputFormat": "json", "entityTypes": ["person", "skill", "company", "project"] } } \`\`\` **Expected Output**: Each candidate enriched with: - \`entities.person\`: ["John Smith", ...] - \`entities.skills\`: ["Python", "Kubernetes", "System Design", ...] - \`entities.company\`: ["Google", "Meta", ...] - \`entities.projects\`: ["Open-source ML library", ...] - \`experience_level\`: "junior" | "mid" | "senior" | "staff" - \`summary\`: Career highlight and current focus --- ### Step 5: Score and Tag Candidates Use knowledge_graph to score candidates and prepare outreach messaging. **Tool**: \`knowledge_graph\` **Parameters** (Create candidate entities): \`\`\`json { "operation": "create_entities", "entities": [ { "name": "John Smith", "entityType": "person", "observations": [ "Experience Level: Senior", "Top Skills: Go, Distributed Systems, Kubernetes", "Current/Recent Company: Google", "GitHub Activity: High (50+ contributions last 3 months)", "Recent Blog Posts: 'Scaling to 1M QPS', 'Kubernetes Best Practices'", "Contact: john.smith@email.com, github.com/johnsmith", "Fit Score: 9/10 (expertise matches 90% of JD)", "Outreach Angle: Mention leadership opportunity in your recent blog post on distributed systems" ] } ] \`\`\` --- ### Step 6: Export and Prepare Outreach List Generate a prioritized list for recruiting outreach. **Tool**: \`websets_manager\` **Parameters**: \`\`\`json { "operation": "export_webset", "websetId": "ws_candidates_789", "format": "csv", "fields": ["name", "experience_level", "top_skills", "current_company", "github_profile", "blog_url", "contact_email", "fit_score"] } \`\`\` **Expected Output**: CSV with: - name, experience_level, top_skills, current_company, github_profile, blog_url, contact_email, fit_score Then import into your ATS/recruiting platform or set up outreach sequences. --- ## 💡 Real-World Examples ### Example 1: Senior Backend Engineers at Scale-ups **Goal**: Find 50+ senior engineers with distributed systems experience at growing tech companies. **Search Query**: \`\`\` "backend engineer" OR "systems engineer" (golang OR rust OR distributed systems OR "high scale") location:Bay Area OR remote site:github.com OR site:linkedin.com seniority:senior \`\`\` **Enrichment Focus**: Extract tech stack, company, GitHub activity, recent projects **Expected Result**: 40-80 candidates with verified skills, companies, and contact patterns --- ### Example 2: Product Managers Transitioning to Leadership **Goal**: Find PMs ready to become Directors/VPs. **Search Query**: \`\`\` "product manager" OR "senior PM" ("leading team" OR "mentoring" OR "strategic" OR "director" OR "VP product") location:${location} site:linkedin.com OR site:medium.com \`\`\` **Enrichment Focus**: Classify experience level, extract leadership signals, summarize impact **Expected Result**: 20-40 PM candidates showing leadership readiness with mentoring/team-building experience --- ### Example 3: Startup Founders as Technical Co-Founders **Goal**: Identify successful startup founders who might partner on a new venture. **Search Query**: \`\`\` founder OR "co-founder" (exit OR acquisition OR "Series B" OR "Series C") (technical OR engineering OR CTO) site:github.com OR site:techcrunch.com OR site:producthunt.com \`\`\` **Enrichment Focus**: Extract exit details, technical skills, co-founder network **Expected Result**: 15-30 technical founders with successful exit history --- ## 🎓 Best Practices ### Candidate Search & Sourcing - Use platform-specific searches: GitHub for engineers, LinkedIn for all roles, Medium/blogs for thought leaders - Include intent signals: "hiring", "open to", "looking", "available" - Add seniority filters: "senior", "staff", "principal", "director" - Combine hard skills (languages, frameworks) with soft skills (leadership, mentoring) ### Enrichment Strategy - Prioritize \`extract_skills\` and \`extract_entities\` to build candidate profiles - Use \`classify_experience_level\` to identify senior vs. junior candidates - Run \`summarize\` to create talking points for outreach - Use knowledge_graph to store custom scoring and outreach angles ### Candidate Scoring & Filtering - Technical fit (skills match JD): 0-50 points - Seniority fit (role matches level): 0-30 points - Cultural fit (company background, values): 0-20 points - Recency (active in last 3 months): +10 points if yes - Total target: 80+ for outreach, 60-79 for nurture list ### Outreach Personalization - Mention specific projects from GitHub (shows you researched them) - Reference their blog posts or talks (high credibility signal) - Lead with mission fit before role description - Avoid generic recruiter messages; be specific about why they're a fit ### Passive Candidate Nurturing - For candidates below 80 score, nurture with your company's technical blog posts - Share articles on their interests (if they blog about Kubernetes, send relevant Kubernetes content) - Engage on GitHub/social media first (stars, thoughtful comments) before direct outreach - Build relationships over 2-3 months before pitching open role --- ## ⚠️ Troubleshooting ### Issue: Too many low-quality candidates **Cause**: Search too broad or not filtering by seniority/skills correctly. **Solution**: - Add specific technology stacks: \`(golang AND kubernetes AND "distributed systems")\` - Filter by company prestige: \`site:github.com/google OR site:github.com/meta\` - Increase activity requirements: \`"100+ contributions last 90 days"\` ### Issue: Can't find email addresses **Cause**: Most candidates don't publish email directly on GitHub/LinkedIn. **Solution**: - Use \`extract_entities\` with \`includeEmails: true\` to find patterns - Check their personal website or blog (usually in GitHub bio) - Build email pattern from company domain (firstname@company.com) - Use email finder tool (Hunter, RocketReach) as secondary lookup ### Issue: Enrichment times out **Cause**: Webset too large (200+ items) or API rate limiting. **Solution**: - Split into smaller websets (100 items max per enrichment) - Run enrichment at off-peak hours - Prioritize top-scored candidates first - Use \`waitForResults: false\` and check status later ### Issue: API rate limit or service error **Cause**: Technical error on Exa API side. **Solution**: - Check API key is set: \`echo $EXA_API_KEY\` - Verify network connectivity - Wait 5-10 minutes and retry - Contact support with webset ID and timestamp ### Issue: Candidates don't match role after enrichment **Cause**: Search query was too loose or matched adjacent titles. **Solution**: - Refine search with specific negative filters: \`-"hiring for" -"currently hiring"\` - Add required skills explicitly: \`(golang AND kubernetes)\` not just \`(golang OR kubernetes)\` - Increase minimum GitHub activity threshold - Manually review 10-20 results to refine query further --- ## ❓ FAQ **Q: How often should I run candidate sourcing websets?** A: Monthly for ongoing talent pipeline building. Weekly during active hiring push. Quarterly for evergreen "bench" candidates. **Q: Can I combine multiple role searches in one webset?** A: Yes! Use OR logic: \`("backend engineer" OR "systems engineer") AND (golang OR rust)\` **Q: What's the best way to approach a passive candidate?** A: Research first (GitHub/blog), personalize second (mention specific project), lead with culture/mission/growth opportunity. **Q: How accurate is the "experience level" classification?** A: Pretty reliable for "senior" (8+ years, staff-level titles) and "junior" (0-2 years). "Mid-level" is broader. Manually verify. **Q: How do I track which candidates I've already contacted?** A: Tag in knowledge_graph: \`"outreach_sent: 2024-01-10", "status: waiting_response"\` **Q: Can I find candidates by non-technical attributes (e.g., geographic location, university)?** A: Yes! Add those to search: \`"Stanford graduate" OR "University of Washington"\`, \`location:Seattle\` **Q: What if I want ex-employees or competitors' employees?** A: Search for company mentions: \`site:linkedin.com "former Google engineer" OR "ex-Google"\` --- ## 🚀 Next Steps 1. Define your role requirements and must-have skills 2. Run your first candidate sourcing webset (aim for 100-200 candidates) 3. Enrich with skills extraction and experience level classification 4. Score candidates and export top 20-30 for immediate outreach 5. Set up weekly sourcing cadence for ongoing pipeline 6. Build outreach sequences with personalized messages Ready to discover your next hire?`; } function jobSeekerWorkflow(role: string, location: string, seniority: string, keywords: string): string { return `# 🚀 Hiring Orchestration: Job Seeker - Target Company and Role Tracking ## What This Solves Discover companies that are hiring, track their growth and product momentum, and identify the right people to connect with. This orchestration helps job seekers: - **Target Company Discovery**: Find companies in your target market with your ideal size/stage - **Hiring Signal Tracking**: Identify when companies are actively hiring - **Role Fit Analysis**: Understand what skills companies are currently seeking - **Recruiter Mapping**: Find recruiters and hiring managers in your target companies - **Opportunity Alerts**: Get notified when your target companies post relevant roles - **Relationship Building**: Connect authentically with decision-makers before applying ## When to Use ✅ Active job search in specific vertical ✅ Career transition planning ✅ Building relationships with future employers ✅ Startup founder prospecting/angel investing discovery ✅ Consulting/contract work lead generation ## 🎯 TL;DR Quick Steps 1. 🔍 **Search**: Find target companies and their hiring activity 2. 🧰 **Create Webset**: Organize company/hiring signals 3. ✨ **Enrich**: Extract hiring needs, recruiter contacts, growth signals 4. 📊 **Analyze**: Rank by company fit and opportunity alignment 5. 🤝 **Outreach**: Build authentic connections with decision-makers --- ## 📋 Step-by-Step Workflow ### Step 1: Discover Target Companies Search for companies hiring for your target role that match your career goals. **Tool**: \`web_search_exa\` **Parameters**: \`\`\`json { "query": "now hiring ${role} (${keywords}) location:${location} site:jobs.lever.co OR site:greenhouse.io OR site:linkedin.com/jobs", "numResults": 100, "timeRange": "30d", "waitForResults": true } \`\`\` **Expected Output**: 50-100 results with active job postings, hiring announcements, and company growth signals. --- ### Step 2: Create Target Companies Webset Organize hiring companies and opportunities into a tracked collection. **Tool**: \`websets_manager\` **Parameters**: \`\`\`json { "operation": "create_webset_from_search", "name": "${role} Jobs & Target Companies (${location})", "description": "Target companies hiring for ${role}. Seniority: ${seniority}. Location: ${location}. Interests: ${keywords}. Tracks active postings, hiring volume, and growth signals.", "searchQuery": "now hiring ${role} (${keywords}) location:${location} site:jobs.lever.co OR site:greenhouse.io OR site:linkedin.com/jobs", "timeRange": "30d", "maxResults": 250 } \`\`\` **Expected Output**: \`\`\`json { "websetId": "ws_target_jobs_101", "status": "processing", "createdAt": "2024-01-10T11:00:00Z", "estimatedCompletion": "2024-01-10T11:45:00Z" } \`\`\` --- ### Step 3: Check Webset Status Monitor until processing completes (20-45 minutes). **Tool**: \`websets_manager\` **Parameters**: \`\`\`json { "operation": "get_webset_status", "websetId": "ws_target_jobs_101" } \`\`\` **Expected Output**: \`\`\`json { "websetId": "ws_target_jobs_101", "status": "completed", "itemCount": 234, "completedAt": "2024-01-10T11:42:00Z" } \`\`\` --- ### Step 4: Extract Hiring Signals and Company Data Retrieve and enrich job postings to understand hiring needs and company growth. **Tool**: \`websets_manager\` **Parameters** (Get items): \`\`\`json { "operation": "list_items", "websetId": "ws_target_jobs_101", "limit": 200, "offset": 0 } \`\`\` **Expected Output**: Array of 200 job postings and company mentions with \`url\`, \`title\`, \`text\`, \`publishedDate\`. **Tool**: \`websets_manager\` **Parameters** (Enrich with hiring signal extraction): \`\`\`json { "operation": "enhance_content", "websetId": "ws_target_jobs_101", "enhancements": [ "extract_entities", "extract_job_requirements", "summarize", "classify_company_stage" ], "waitForResults": true, "advanced": { "outputFormat": "json", "entityTypes": ["company", "person", "skill", "role"] } } \`\`\` **Expected Output**: Each job posting enriched with: - \`entities.company\`: ["Acme Corp", ...] - \`entities.role\`: ["Senior Engineer", ...] - \`entities.skills\`: ["Python", "React", "AWS", ...] - \`entities.recruiter\`: ["Jane Doe (Recruiter)", ...] - \`job_requirements\`: ["5+ years experience", "Kubernetes knowledge", ...] - \`company_stage\`: "startup" | "scale-up" | "enterprise" - \`growth_signals\`: "high" | "medium" | "low" - \`summary\`: 1-2 sentence job description --- ### Step 5: Identify Recruiters and Decision-Makers Extract recruiter contacts and hiring manager information for relationship-building. **Tool**: \`knowledge_graph\` **Parameters** (Create company entities with recruiter/hiring info): \`\`\`json { "operation": "create_entities", "entities": [ { "name": "Acme Corp", "entityType": "company", "observations": [ "Company Stage: Scale-up (Series B, 100-250 employees)", "Growth Signals: 5 new job postings this month (expansion)", "Hiring for: Senior Engineer, Product Manager, Data Engineer", "Hiring Manager: Sarah Chen (VP Engineering, sarah@acme.com)", "Recruiter: Mike Johnson (Technical Recruiter, mike@acme.com)", "Website: acme.com | LinkedIn: /company/acme-corp", "Mission Fit: High (AI/ML company, matches your interests)", "Outreach Strategy: Connect with Sarah on LinkedIn about their ML infrastructure" ] } ] } \`\`\` --- ### Step 6: Score Companies and Create Outreach Plan Prioritize companies and roles by fit, and plan authentic outreach. **Tool**: \`knowledge_graph\` **Parameters** (Add company-specific outreach guidance): \`\`\`json { "operation": "add_observations", "entityName": "Acme Corp", "contents": [ "Overall Fit Score: 8.5/10", "Company Culture Score: 8/10 (based on website, reviews)", "Role Match Score: 9/10 (skills: 95% overlap)", "Stage Match: Perfect (you're seeking scale-up growth opportunity)", "Location: Remote OK, HQ in San Francisco", "Next Steps: ", "1. Follow Sarah Chen on LinkedIn", "2. Engage thoughtfully on 2-3 of her posts", "3. Send personalized DM referencing her recent tech post", "4. If connection accepted, ask for 15-min call about ML infrastructure", "5. After 1-2 weeks, mention you saw the open Senior Engineer role" ] } \`\`\` --- ### Step 7: Export and Execute Outreach Plan Generate a prioritized list of target companies and apply strategy. **Tool**: \`websets_manager\` **Parameters**: \`\`\`json { "operation": "export_webset", "websetId": "ws_target_jobs_101", "format": "csv", "fields": ["company_name", "open_roles", "hiring_manager", "recruiter_contact", "company_stage", "fit_score", "job_posting_url"] } \`\`\` **Expected Output**: CSV with: - company_name, open_roles, hiring_manager, recruiter_contact, company_stage, fit_score, job_posting_url Import into a spreadsheet and build 3-week relationship-building plan before formal application. --- ## 💡 Real-World Examples ### Example 1: Early-Stage AI Startups in SF Bay Area **Goal**: Find 10-20 Series A/B AI startups hiring engineers in SF Bay Area. **Search Query**: \`\`\` "Series A" OR "Series B" "now hiring" engineer (AI OR "machine learning" OR LLM) location:"Bay Area" site:jobs.lever.co OR site:linkedin.com/jobs \`\`\` **Enrichment Focus**: Extract company stage, hiring volume, technical requirements, recruiter contacts **Expected Result**: 15-25 hot startups with founder names, funding details, and active hiring --- ### Example 2: Remote Product Manager Roles at Scale-ups **Goal**: Find product manager opportunities at 50-500 person companies offering remote work. **Search Query**: \`\`\` "product manager" OR "senior PM" hiring remote (scale-up OR "Series B" OR "Series C") site:jobs.lever.co OR site:greenhouse.io \`\`\` **Enrichment Focus**: Extract product focus area, company vision, hiring manager info **Expected Result**: 20-40 PM roles with clear growth/mission fit --- ### Example 3: Technical Leadership Opportunities **Goal**: Find companies seeking engineering managers or technical leads (career growth signal). **Search Query**: \`\`\` "engineering manager" OR "technical lead" OR "staff engineer" hiring (leadership OR mentoring OR team-building) site:linkedin.com/jobs \`\`\` **Enrichment Focus**: Extract scope (team size, company size), leadership style signals, compensation signals **Expected Result**: 15-30 leadership-track roles with growth potential --- ## 🎓 Best Practices ### Target Company Research - Focus on 10-20 target companies you'd love to work for - Track their hiring volume and frequency (growing = good signal) - Study their engineering blog and product announcements - Understand their mission and technical challenges - Follow their engineers and executives on social media ### Hiring Signal Interpretation - Multiple postings for same role = high urgency or high turnover - New categories of hires (e.g., "first ML engineer") = expansion signal - Rapid hiring growth = funding, product expansion, or potential churn - Quiet hiring = bootstrap or selective growth ### Relationship Building Before Applying - Don't apply cold; build relationship with hiring manager first - Engage authentically with their content (1-2 thoughtful comments) - Send brief, personalized message referencing their work - Ask for informational 15-min call, not a job pitch - After 1-2 conversations, express interest in open role ### Application Strategy - After relationship built, apply with personal intro from connection - Reference specific conversation or insight shared - Tailor resume/cover letter to company's stated challenges - Apply quickly after role posting (first week = 2x callback rate) - Follow up after 1 week if no response ### Offer Negotiation Prep - Research salary ranges in advance (Levels.fyi, Blind, Comparably) - Understand equity/stock option standards for company stage - Know your walk-away number before negotiating - Get offer in writing before accepting - Negotiate on: base salary, equity, signing bonus, start date, remote days --- ## ⚠️ Troubleshooting ### Issue: Too many irrelevant job postings **Cause**: Search query includes adjacent roles or industries. **Solution**: - Add specific required skills: \`(python AND system design)\` not just \`engineer\` - Exclude irrelevant titles: \`-sales -support -marketing\` - Filter by company size/stage to match your target - Increase location specificity (city/region not just country) ### Issue: Can't find recruiter contact information **Cause**: Recruiters often don't publish direct emails in job postings. **Solution**: - Extract company domain from job URL - Try recruiter@company.com or talent@company.com - Check company LinkedIn careers page for recruiter profile - Find recruiter name from Greenhouse/Lever posting URL sometimes shows it - Message hiring manager on LinkedIn if recruiter contact fails ### Issue: Enrichment times out **Cause**: Webset too large (250+ items). **Solution**: - Run on off-peak hours (early morning, late evening) - Split into smaller websets if needed - Focus on top 100 companies first - Use \`waitForResults: false\` and check status later ### Issue: API rate limit or service error **Cause**: Technical error on Exa API side. **Solution**: - Verify API key is set: \`echo $EXA_API_KEY\` - Check network connectivity - Wait 5-10 minutes and retry - Contact support with webset ID ### Issue: Companies don't match my career goals after enrichment **Cause**: Search was too broad or didn't filter correctly. **Solution**: - Refine stage filter: only \`"Series B" OR "Series C"\` if you want scale-ups - Add mission/industry filters: \`(fintech OR healthtech OR climate)\` - Increase technical skill requirements to match - Manually review top 20 results and refine further --- ## ❓ FAQ **Q: How often should I update my target company list?** A: Monthly is good. Weekly if actively job hunting. Update whenever you find new companies or when hiring signals change. **Q: Should I apply to multiple roles at the same company?** A: 1-2 roles max. Multiple applications can look unfocused. Pick the best fit and nail it. **Q: How long should I build relationships before applying?** A: 2-4 weeks of genuine engagement. Too fast = looks opportunistic. Too slow = they hire someone else. **Q: What if I see the same company multiple times in my search?** A: That's a strong signal—they're actively hiring and growing. Prioritize them. **Q: How do I know if a company is financially stable?** A: Check Crunchbase, PitchBook for funding history. Look for recent funding rounds. Check Glassdoor for "company health" signals. **Q: Can I use this to find startup cofounders?** A: Yes! Search for \`founder OR "co-founder" (technical OR engineering)\` in your industry of interest. --- ## 🚀 Next Steps 1. List 10-20 target companies where you'd love to work 2. Create a webset around hiring signals from those companies 3. Enrich to identify key recruiters and hiring managers 4. Build 3-week relationship plan for top 5 companies 5. Execute outreach: engage → connect → learn → apply 6. Track outcomes and refine strategy monthly Ready to land your dream role?`; }