Sales Engineer

What This Skill Does

• This skill guides sales engineers through a 5-phase workflow, from discovery to solution design.
• It helps sales engineers understand customer requirements, design solutions, and build competitive differentiation.
• It provides tools and checklists for each phase of the sales process.

When to Use

Conduct technical discovery callsMap customer architectureDesign integration architectureBuild competitive strategyAssess requirement alignmentIdentify customization needs

Key Features

Analyzes RFP responsesBuilds competitive matricesMaps product capabilitiesDesigns integration architecturesIdentifies customization needsProvides validation checkpoints

Manual Installation

Sales Engineer Skill

5-Phase Workflow

Phase 1: Discovery & Research

Objective: Understand customer requirements, technical environment, and business drivers.

Checklist: [ ] 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: Run rfp_response_analyzer.py to score initial requirement alignment.

python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json > phase1_rfp_results.json

Output: Technical discovery document, requirement map, initial coverage assessment.

Validation checkpoint: Coverage score must be >50% and must-have gaps ≤3 before proceeding to Phase 2. Check with: python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json | python -c "import sys,json; r=json.load(sys.stdin); print('PROCEED' if r['coverage_score']>50 and r['must_have_gaps']<=3 else 'REVIEW')"

Phase 2: Solution Design

Objective: Design a solution architecture that addresses customer requirements.

Checklist: [ ] Map product capabilities to customer requirements [ ] Design integration architecture [ ] Identify customization needs and development effort [ ] Build competitive differentiation strategy [ ] Create solution architecture diagrams

Tools: Run competitive_matrix_builder.py using Phase 1 data to identify differentiators and vulnerabilities.

python scripts/competitive_matrix_builder.py competitive_data.json --format json > phase2_competitive.json

python -c "import json; d=json.load(open('phase2_competitive.json')); print('Differentiators:', d['differentiators']); print('Vulnerabilities:', d['vulnerabilities'])"

Output: Solution architecture, competitive positioning, technical differentiation strategy.

Validation checkpoint: Confirm at least one strong differentiator exists per customer priority before proceeding to Phase 3. If no differentiators found, escalate to Product Team (see Integration Points).

Phase 3: Demo Preparation & Delivery

Objective: Deliver compelling technical demonstrations tailored to stakeholder priorities.

Checklist: [ ] 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 assets/demo_script_template.md for structured demo preparation.

Output: Customized demo, stakeholder-specific talking points, feedback capture.

Validation checkpoint: Demo script must cover every must-have requirement flagged in phase1_rfp_results.json before delivery. Cross-reference with: python -c "import json; rfp=json.load(open('phase1_rfp_results.json')); [print('UNCOVERED:', r) for r in rfp['must_have_requirements'] if r['coverage']=='Gap']"

Phase 4: POC & Evaluation

Objective: Execute a structured proof-of-concept that validates the solution.

Checklist: [ ] 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: Run poc_planner.py to generate the complete POC plan.

python scripts/poc_planner.py poc_data.json --format json > phase4_poc_plan.json

python -c "import json; p=json.load(open('phase4_poc_plan.json')); print('Go/No-Go:', p['recommendation'])"

Templates: Use assets/poc_scorecard_template.md for evaluation tracking.

Output: POC plan, evaluation scorecard, go/no-go recommendation.

Validation checkpoint: POC conversion requires scorecard score >60% across all evaluation dimensions (functionality, performance, integration, usability, support). If score <60%, document gaps and loop back to Phase 2 for solution redesign.

Phase 5: Proposal & Closing

Objective: Deliver a technical proposal that supports the commercial close.

Checklist: [ ] 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 assets/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%), Partial (50%), Planned (25%), Gap (0%).
Priority Weighting: Must-Have 3×, Should-Have 2×, Nice-to-Have 1×.

Bid/No-Bid Logic: Bid: Coverage >70% AND must-have gaps ≤3 Conditional Bid: Coverage 50–70% OR must-have gaps 2–3 No-Bid: Coverage <50% OR must-have gaps >3

Usage: python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json # human-readable python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json # JSON output 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), Partial (2), Limited (1), None (0).

Usage: python scripts/competitive_matrix_builder.py competitive_data.json # human-readable python scripts/competitive_matrix_builder.py competitive_data.json --format json # JSON output

Output Includes: Feature comparison matrix, weighted competitive scores, differentiators, vulnerabilities, and win themes.

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: python scripts/poc_planner.py poc_data.json # human-readable python scripts/poc_planner.py poc_data.json --format json # JSON output

Output Includes: Phased POC plan, resource allocation, success criteria, evaluation scorecard, risk register, and go/no-go recommendation framework.

Reference Knowledge Bases

Asset Templates