sales-engineer
Scannednpx machina-cli add skill alirezarezvani/claude-skills/sales-engineer --openclawSales Engineer Skill
A production-ready skill package for pre-sales engineering that bridges technical expertise and sales execution. Provides automated analysis for RFP/RFI responses, competitive positioning, and proof-of-concept planning.
Overview
Role: Sales Engineer / Solutions Architect Domain: Pre-Sales Engineering, Solution Design, Technical Demos, Proof of Concepts Business Type: SaaS / Pre-Sales Engineering
What This Skill Does
- RFP/RFI Response Analysis - Score requirement coverage, identify gaps, generate bid/no-bid recommendations
- Competitive Technical Positioning - Build feature comparison matrices, identify differentiators and vulnerabilities
- POC Planning - Generate timelines, resource plans, success criteria, and evaluation scorecards
- Demo Preparation - Structure demo scripts with talking points and objection handling
- Technical Proposal Creation - Framework for solution architecture and implementation planning
- Win/Loss Analysis - Data-driven competitive assessment for deal strategy
Key Metrics
| Metric | Description | Target |
|---|---|---|
| Win Rate | Deals won / total opportunities | >30% |
| Sales Cycle Length | Average days from discovery to close | <90 days |
| POC Conversion Rate | POCs resulting in closed deals | >60% |
| Customer Engagement Score | Stakeholder participation in evaluation | >75% |
| RFP Coverage Score | Requirements fully addressed | >80% |
5-Phase Workflow
Phase 1: Discovery & Research
Objective: Understand customer requirements, technical environment, and business drivers.
Activities:
- Conduct technical discovery calls with stakeholders
- Map customer's current architecture and pain points
- Identify integration requirements and constraints
- Document security and compliance requirements
- Assess competitive landscape for this opportunity
Tools: Use rfp_response_analyzer.py to score initial requirement alignment.
Output: Technical discovery document, requirement map, initial coverage assessment.
Phase 2: Solution Design
Objective: Design a solution architecture that addresses customer requirements.
Activities:
- Map product capabilities to customer requirements
- Design integration architecture
- Identify customization needs and development effort
- Build competitive differentiation strategy
- Create solution architecture diagrams
Tools: Use competitive_matrix_builder.py to identify differentiators and vulnerabilities.
Output: Solution architecture, competitive positioning, technical differentiation strategy.
Phase 3: Demo Preparation & Delivery
Objective: Deliver compelling technical demonstrations tailored to stakeholder priorities.
Activities:
- Build demo environment matching customer's use case
- Create demo script with talking points per stakeholder role
- Prepare objection handling responses
- Rehearse failure scenarios and recovery paths
- Collect feedback and adjust approach
Templates: Use demo_script_template.md for structured demo preparation.
Output: Customized demo, stakeholder-specific talking points, feedback capture.
Phase 4: POC & Evaluation
Objective: Execute a structured proof-of-concept that validates the solution.
Activities:
- Define POC scope, success criteria, and timeline
- Allocate resources and set up environment
- Execute phased testing (core, advanced, edge cases)
- Track progress against success criteria
- Generate evaluation scorecard
Tools: Use poc_planner.py to generate the complete POC plan.
Templates: Use poc_scorecard_template.md for evaluation tracking.
Output: POC plan, evaluation scorecard, go/no-go recommendation.
Phase 5: Proposal & Closing
Objective: Deliver a technical proposal that supports the commercial close.
Activities:
- Compile POC results and success metrics
- Create technical proposal with implementation plan
- Address outstanding objections with evidence
- Support pricing and packaging discussions
- Conduct win/loss analysis post-decision
Templates: Use technical_proposal_template.md for the proposal document.
Output: Technical proposal, implementation timeline, risk mitigation plan.
Python Automation Tools
1. RFP Response Analyzer
Script: scripts/rfp_response_analyzer.py
Purpose: Parse RFP/RFI requirements, score coverage, identify gaps, and generate bid/no-bid recommendations.
Coverage Categories:
- Full (100%) - Requirement fully met by current product
- Partial (50%) - Requirement partially met, workaround or configuration needed
- Planned (25%) - On product roadmap, not yet available
- Gap (0%) - Not supported, no current plan
Priority Weighting:
- Must-Have: 3x weight
- Should-Have: 2x weight
- Nice-to-Have: 1x weight
Bid/No-Bid Logic:
- Bid: Coverage score >70% AND must-have gaps <=3
- Conditional Bid: Coverage score 50-70% OR must-have gaps 2-3
- No-Bid: Coverage score <50% OR must-have gaps >3
Usage:
# Human-readable output
python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json
# JSON output
python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json
# Help
python scripts/rfp_response_analyzer.py --help
Input Format: See assets/sample_rfp_data.json for the complete schema.
2. Competitive Matrix Builder
Script: scripts/competitive_matrix_builder.py
Purpose: Generate feature comparison matrices, calculate competitive scores, identify differentiators and vulnerabilities.
Feature Scoring:
- Full (3) - Complete feature support
- Partial (2) - Partial or limited feature support
- Limited (1) - Minimal or basic feature support
- None (0) - Feature not available
Usage:
# Human-readable output
python scripts/competitive_matrix_builder.py competitive_data.json
# JSON output
python scripts/competitive_matrix_builder.py competitive_data.json --format json
Output Includes:
- Feature comparison matrix with scores
- Weighted competitive scores per product
- Differentiators (features where our product leads)
- Vulnerabilities (features where competitors lead)
- Win themes based on differentiators
3. POC Planner
Script: scripts/poc_planner.py
Purpose: Generate structured POC plans with timeline, resource allocation, success criteria, and evaluation scorecards.
Default Phase Breakdown:
- Week 1: Setup - Environment provisioning, data migration, configuration
- Weeks 2-3: Core Testing - Primary use cases, integration testing
- Week 4: Advanced Testing - Edge cases, performance, security
- Week 5: Evaluation - Scorecard completion, stakeholder review, go/no-go
Usage:
# Human-readable output
python scripts/poc_planner.py poc_data.json
# JSON output
python scripts/poc_planner.py poc_data.json --format json
Output Includes:
- POC plan with phased timeline
- Resource allocation (SE, engineering, customer)
- Success criteria with measurable metrics
- Evaluation scorecard (functionality, performance, integration, usability, support)
- Risk register with mitigation strategies
- Go/No-Go recommendation framework
Reference Knowledge Bases
| Reference | Description |
|---|---|
references/rfp-response-guide.md | RFP/RFI response best practices, compliance matrix, bid/no-bid framework |
references/competitive-positioning-framework.md | Competitive analysis methodology, battlecard creation, objection handling |
references/poc-best-practices.md | POC planning methodology, success criteria, evaluation frameworks |
Asset Templates
| Template | Purpose |
|---|---|
assets/technical_proposal_template.md | Technical proposal with executive summary, solution architecture, implementation plan |
assets/demo_script_template.md | Demo script with agenda, talking points, objection handling |
assets/poc_scorecard_template.md | POC evaluation scorecard with weighted scoring |
assets/sample_rfp_data.json | Sample RFP data for testing the analyzer |
assets/expected_output.json | Expected output from rfp_response_analyzer.py |
Communication Style
- Technical yet accessible - Translate complex concepts for business stakeholders
- Confident and consultative - Position as trusted advisor, not vendor
- Evidence-based - Back every claim with data, demos, or case studies
- Stakeholder-aware - Tailor depth and focus to audience (CTO vs. end user vs. procurement)
Integration Points
- Marketing Skills - Leverage competitive intelligence and messaging frameworks from
../../marketing-skill/ - Product Team - Coordinate on roadmap items flagged as "Planned" in RFP analysis from
../../product-team/ - C-Level Advisory - Escalate strategic deals requiring executive engagement from
../../c-level-advisor/ - Customer Success - Hand off POC results and success criteria to CSM from
../customer-success-manager/
Last Updated: February 2026 Status: Production-ready Tools: 3 Python automation scripts References: 3 knowledge base documents Templates: 5 asset files
Source
git clone https://github.com/alirezarezvani/claude-skills/blob/main/business-growth/sales-engineer/SKILL.mdView on GitHub Overview
Sales Engineer Skill is a production-ready package for pre-sales engineering that bridges technical expertise and sales execution. It provides automated analysis of RFP/RFI responses, competitive positioning with feature matrices, and structured POC planning, demo prep, and technical proposals.
How This Skill Works
The skill uses automated analysis scripts to score requirement coverage, identify gaps, and generate bid/no-bid recommendations. It also builds competitive feature matrices to highlight differentiators and vulnerabilities, and plans POC engagements with timelines, resource plans, and evaluation scorecards. Outputs include architecture diagrams, demo scripts, and evaluation templates to standardize pre-sales activities.
When to Use It
- When analyzing an RFP/RFI to identify coverage gaps and risks and determine bid viability
- When creating a competitive positioning plan with feature matrices and differentiators
- When planning and coordinating a Proof-of-Concept (POC) with defined success criteria and timelines
- When preparing tailored demos and talking points for different stakeholder roles
- When developing technical proposals and conducting win/loss assessments to refine deal strategy
Quick Start
- Step 1: Run rfp_response_analyzer.py on the RFP/RFI to score requirement alignment
- Step 2: Use competitive_matrix_builder.py to map capabilities to requirements and identify differentiators
- Step 3: Generate a POC plan with poc_planner.py and create a demo script with demo_script_template.md
Best Practices
- Score requirements systematically with an RFP/RFI analyzer to reveal gaps early
- Map product capabilities to customer requirements and identify clear differentiators
- Define POC scope, success criteria, and timeline before execution
- Build stakeholder-specific demo scripts and objection handling in advance
- Use evaluation scorecards and win/loss data to inform future opportunities
Example Use Cases
- RFP response coverage improved to >80% across key requirements using automated scoring
- Competitive feature matrix highlighted differentiators, revealing vulnerabilities in competitor offerings
- POC plan generated with defined milestones and a go/no-go criteria, accelerating decision cycles
- Demo scripts tailored to CIO, VP of Engineering, and Solution Architect roles, improving engagement
- Technical proposal framework produced to standardize architecture and implementation planning, aiding close