sales-engineer
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'.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/alirezarezvani/claude-skills --skill sales-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sales-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
FAQ
What does the sales-engineer skill do?
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'.
How do I install sales-engineer?
Run npx skills add https://github.com/alirezarezvani/claude-skills --skill sales-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sales-engineer/ (personal) or .claude/skills/sales-engineer/ (project).
Which AI agents can use sales-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sales-engineer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains sales-engineer?
It is published by alirezarezvani in alirezarezvani/claude-skills. It has 777 recorded installs.
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