How to install sales-engineer
npx skills add https://github.com/alirezarezvani/claude-skills --skill sales-engineerFull instructions (SKILL.md)
Source of truth, from alirezarezvani/claude-skills.
name: "sales-engineer" description: Analyzes RFP/RFI responses for coverage gaps, builds competitive feature comparison matrices, and plans proof-of-concept (POC) engagements for pre-sales engineering. Use when responding to RFPs, bids, or proposal requests; comparing product features against competitors; planning or scoring a customer POC or sales demo; preparing a technical proposal; or performing win/loss competitor analysis. Handles tasks described as 'RFP response', 'bid response', 'proposal response', 'competitor comparison', 'feature matrix', 'POC planning', 'sales demo prep', or 'pre-sales engineering'.
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
| 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 |
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
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