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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'.
npx claudepluginhub ciciliaeth/claude-skills --plugin business-growth-skillsHow this skill is triggered — by the user, by Claude, or both
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/business-growth-skills:sales-engineerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
**Objective:** Understand customer requirements, technical environment, and business drivers.
assets/demo_script_template.mdassets/expected_output.jsonassets/poc_scorecard_template.mdassets/sample_rfp_data.jsonassets/technical_proposal_template.mdreferences/competitive-positioning-framework.mdreferences/poc-best-practices.mdreferences/rfp-response-guide.mdscripts/competitive_matrix_builder.pyscripts/poc_planner.pyscripts/rfp_response_analyzer.pySearches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Executes ctx7 CLI to fetch up-to-date library documentation, manage AI coding skills (install/search/generate/remove/suggest), and configure Context7 MCP. Useful for current API refs, skill handling, or agent setup.
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Objective: Understand customer requirements, technical environment, and business drivers.
Checklist:
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')"
Objective: Design a solution architecture that addresses customer requirements.
Checklist:
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).
Objective: Deliver compelling technical demonstrations tailored to stakeholder priorities.
Checklist:
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']"
Objective: Execute a structured proof-of-concept that validates the solution.
Checklist:
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.
Objective: Deliver a technical proposal that supports the commercial close.
Checklist:
Templates: Use assets/technical_proposal_template.md for the proposal document.
Output: Technical proposal, implementation timeline, risk mitigation plan.
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:
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.
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.
Script: scripts/poc_planner.py
Purpose: Generate structured POC plans with timeline, resource allocation, success criteria, and evaluation scorecards.
Default Phase Breakdown:
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 | 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 |
| 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 |
../../marketing-skill/../../product-team/../../c-level-advisor/../customer-success-manager/Last Updated: February 2026 Status: Production-ready Tools: 3 Python automation scripts References: 3 knowledge base documents Templates: 5 asset files