Built With Skene

Built With Skene

Real-world AI agents built using the Skene Skills Directory

This showcase features agent deployments that demonstrate the power of skill chain composition. Each example includes the problem, solution, skills used, and measurable impact.

1. LeadFlow - Intelligent Lead Qualification Agent

Problem

B2B SaaS company with 50-person sales team was manually qualifying 200+ leads/month. Each lead took 2-3 hours of research, scoring, and routing. Only 15% of leads qualified, wasting significant rep time.

Solution

5-skill chain automating the entire qualification pipeline:

lead_qualification → opportunity_scoring → deal_inspection →
next_best_action → content_recommender

How It Works

1. lead_qualification — Applies MEDDIC framework, pulls enrichment data
2. opportunity_scoring — Scores on fit (40%), urgency (30%), budget (30%)
3. deal_inspection — Analyzes deal health, identifies risks early
4. next_best_action — Recommends specific actions for rep
5. content_recommender — Suggests relevant case studies and decks

Skills Used

• lead_qualification (Sales)
• opportunity_scoring (RevOps)
• deal_inspection (Sales)
• next_best_action (Sales)
• content_recommender (Marketing)

Impact

• ⚡ Qualification time: 2-3 hours → 5 minutes per lead (97% reduction)
• 📈 Leads processed: 200/month → 500/month (2.5x increase)
• 💰 Cost savings: $40K/month in rep time
• 🎯 Pipeline quality: 3x more qualified opportunities
• 📊 Win rate: 18% → 25% (39% improvement)

Payback: Week 1 Status: Production (18 months)

2. ChurnGuard - Proactive Churn Prevention Agent

Problem

SaaS startup with $12M ARR facing 20% annual churn. Customer success team was reactive, only intervening after usage dropped. No early warning system for at-risk accounts.

Solution

4-skill chain for predictive churn prevention:

health_scoring → churn_prediction → risk_mitigation_playbook →
escalation_manager

How It Works

1. health_scoring — Tracks product usage, support tickets, NPS
2. churn_prediction — ML model predicts churn 60-90 days early
3. risk_mitigation_playbook — Triggers intervention based on risk level
4. escalation_manager — Auto-escalates high-risk accounts to CSM

Skills Used

• health_scoring (Customer Success)
• churn_prediction (Customer Success)
• risk_mitigation_playbook (Customer Success)
• escalation_manager (Customer Success)

Impact

• ⚡ Early detection: 60-90 days advance warning (was 0)
• 📈 Churn reduction: 20% → 10% annual (50% improvement)
• 💰 ARR saved: $600K/year
• 🎯 CS efficiency: Proactive vs reactive interventions
• 📊 NPS improvement: +12 points

Payback: Month 1 Status: Production (12 months)

3. GrowthEngine - PLG Activation Optimizer

Problem

Freemium product with 10K monthly signups but only 8% activation rate and 2% free-to-paid conversion. No data-driven optimization of onboarding flow.

Solution

5-skill chain for complete PLG funnel optimization:

activation_analysis → engagement_scoring → monetization_trigger →
viral_loop_optimizer → retention_predictor

How It Works

1. activation_analysis — Identifies friction in aha moment journey
2. engagement_scoring — Real-time engagement tracking per user
3. monetization_trigger — Optimal timing for upgrade prompts
4. viral_loop_optimizer — Maximizes viral coefficient
5. retention_predictor — Predicts churners, triggers win-back

Skills Used

• activation_analysis (PLG)
• engagement_scoring (PLG)
• monetization_trigger (PLG)
• viral_loop_optimizer (PLG)
• retention_predictor (PLG)

Impact

• ⚡ Activation rate: 8% → 18% (125% increase)
• 📈 Time to activation: 7 days → 2 days
• 💰 Free-to-paid conversion: 2% → 4.5% (125% increase)
• 🎯 Viral coefficient: 0.3 → 0.6 (2x)
• 📊 MRR growth: 150% in 3 months

Payback: Week 2 Status: Production (9 months)

4. BoardReady - CFO Intelligence Dashboard

Problem

CFO of $30M ARR SaaS company spending 8 hours/week preparing board decks. Forecasts were static, variance analysis was manual, and financial insights came too late for decision-making.

Solution

5-skill chain for real-time financial intelligence:

financial_metrics_calculator → variance_analyzer → forecast_builder →
cash_flow_projector → board_reporting_generator

How It Works

1. financial_metrics_calculator — Auto-calculates ARR, burn, runway, etc.
2. variance_analyzer — Budget vs. actual with root cause analysis
3. forecast_builder — Multi-scenario forecasting (best/worst/likely)
4. cash_flow_projector — 13-week cash flow projections
5. board_reporting_generator — One-click board deck generation

Skills Used

• financial_metrics_calculator (FinOps)
• variance_analyzer (FinOps)
• forecast_builder (FinOps)
• cash_flow_projector (FinOps)
• board_reporting_generator (FinOps)

Impact

• ⚡ Board prep time: 8 hours → 15 minutes (95% reduction)
• 📈 Forecast updates: Weekly → daily (real-time)
• 💰 CFO time savings: $50K+/month value
• 🎯 Decision speed: 10x faster financial insights
• 📊 Forecast accuracy: 60% → 85%

Payback: Week 1 Status: Production (14 months)

5. ResearchPilot - Automated Literature Review Agent

Problem

Cancer biology lab spending 20+ hours/week per researcher manually reviewing literature. Slow hypothesis generation, difficulty keeping up with new publications.

Solution

4-skill chain for automated research synthesis:

pubmed_search → paper_summarizer → citation_mapper →
hypothesis_generator

How It Works

1. pubmed_search — Automated PubMed queries with custom filters
2. paper_summarizer — Extracts key findings, methods, and conclusions
3. citation_mapper — Builds citation networks, identifies key papers
4. hypothesis_generator — AI-powered hypothesis generation from literature

Skills Used

• pubmed_search (Scientific)
• paper_summarizer (Scientific)
• citation_mapper (Scientific)
• hypothesis_generator (Scientific)

Impact

• ⚡ Literature review time: 3 weeks → 6 hours (95% reduction)
• 📈 Papers synthesized: 50/week → 200/week (4x)
• 💡 Novel hypotheses: 2x increase in ideas generated
• 🎯 Grant success: Faster background research
• 📊 Publication rate: 3/year → 5/year

Payback: Immediate Status: Production (6 months)

6. PipelineIntel - Sales Forecasting Agent

Problem

Sales ops team manually building weekly forecasts from CRM data. Inconsistent methodology, low forecast accuracy (65%), and reps gaming the system.

Solution

3-skill chain for data-driven forecasting:

pipeline_analysis → forecast_builder → win_loss_analyzer

How It Works

1. pipeline_analysis — Real-time pipeline health and velocity metrics
2. forecast_builder — Statistical forecasting with confidence intervals
3. win_loss_analyzer — Identifies patterns in won/lost deals

Skills Used

• pipeline_analysis (RevOps)
• forecast_builder (RevOps)
• win_loss_analyzer (Sales)

Impact

• ⚡ Forecast prep time: 4 hours → 15 minutes
• 📈 Forecast accuracy: 65% → 88%
• 💰 Time savings: $15K/month
• 🎯 Rep gaming eliminated: Objective scoring
• 📊 Executive confidence: Data-driven commits

Payback: Month 1 Status: Production (8 months)

7. ContentFlow - Marketing Automation Pipeline

Problem

Marketing team creating content manually, inconsistent SEO optimization, slow campaign launches. Content creation took 2 weeks, optimization was ad-hoc.

Solution

4-skill chain for content automation:

content_generator → seo_optimizer → campaign_launcher →
performance_tracker

How It Works

1. content_generator — AI-powered content drafts based on briefs
2. seo_optimizer — Keyword optimization, meta tags, internal linking
3. campaign_launcher — Multi-channel campaign deployment
4. performance_tracker — Real-time engagement and conversion tracking

Skills Used

• content_generator (Marketing)
• seo_optimizer (Marketing)
• campaign_launcher (Marketing)
• performance_tracker (Marketing)

Impact

• ⚡ Content creation time: 2 weeks → 2 days (85% reduction)
• 📈 Content volume: 4 pieces/month → 20 pieces/month (5x)
• 💰 Cost per piece: $2K → $400 (80% reduction)
• 🎯 SEO rankings: 30% improvement in top 10 rankings
• 📊 Organic traffic: 45% increase

Payback: Month 2 Status: Production (10 months)

8. DrugDiscovery - Target Identification Agent

Problem

Biotech company spending 6+ months on target identification and validation. Manual literature review, protein analysis, and compound screening.

Solution

6-skill chain for accelerated drug discovery:

uniprot_search → protein_structure_analyzer → drug_target_identifier →
compound_similarity_search → molecular_docking → toxicity_predictor

How It Works

1. uniprot_search — Protein database queries for disease pathways
2. protein_structure_analyzer — AlphaFold structure prediction
3. drug_target_identifier — Identifies druggable targets
4. compound_similarity_search — Screens compound libraries
5. molecular_docking — Predicts binding affinity
6. toxicity_predictor — Early safety assessment

Skills Used

• uniprot_search (Scientific)
• protein_structure_analyzer (Scientific)
• drug_target_identifier (Scientific)
• compound_similarity_search (Scientific)
• molecular_docking (Scientific)
• toxicity_predictor (Scientific)

Impact

• ⚡ Target ID time: 6 months → 3 weeks (95% reduction)
• 📈 Targets evaluated: 5 → 50 in same timeframe (10x)
• 💡 Novel targets: 3 new targets identified
• 🎯 Hit rate: 15% improvement in compound screening
• 📊 Development timeline: 6 months saved

Payback: First successful target Status: Production (4 months)

Common Patterns

What Makes These Successful?

1. Clear Problem Definition — Each agent solves a specific, measurable pain point
2. Skill Chain Composition — 3-6 skills chained together for complete workflows
3. Exit State Routing — Skills pass data to next skill seamlessly
4. Measurable Impact — ROI tracked from day one
5. Iterative Improvement — Chains refined based on real usage

Typical Implementation Timeline

• Week 1: Identify use case, select skills
• Week 2: Build and test skill chain
• Week 3: Deploy to production, measure baseline
• Week 4+: Iterate based on results

Want to Build Your Own?

1. Pick Your Use Case — Start with a specific pain point
2. Browse Skills — Find relevant skills in directory.md
3. Follow a Recipe — Use SKILL_CHAINS.md as template
4. Deploy & Measure — Track impact from day one

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