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Get Started Free →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'
.claude/skills/alirezarezvani-sales-engineer/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 2 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 650% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 106% | 0% |
Objective: Understand customer requirements, technical environment, and business drivers.
Checklist:
Tools: Run rfp_response_analyzer.py to score initial requirement alignment.
bashpython 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:
bashpython 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.
bashpython 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:
bashpython -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.
bashpython 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:
bashpython 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:
bashpython 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:
bashpython 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
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | pass→pass | 9,171 | 3,643 | -60% | 1 | 1 | 0% | 1,116 | 2,736 | +145% | 0 | 0 | — |
case-13 | fail→pass | 10,172 | 2,597 | -74% | 1 | 1 | 0% | 1,829 | 2,533 | +38% | 0 | 0 | — |
case-01 | fail→fail | 5,738 | 12,420 | +116% | 1 | 1 | 0% | 366 | 4,699 | +1184% | 0 | 0 | — |
case-02 | fail→pass | 3,986 | 13,533 | +240% | 1 | 1 | 0% | 647 | 4,851 | +650% | 0 | 0 | — |
case-03 | fail→fail | 26,616 | 23,399 | -12% | 1 | 1 | 0% | 5,071 | 6,392 | +26% | 0 | 0 | — |
case-14 | fail→pass | 11,932 | 1,955 | -84% | 1 | 1 | 0% | 2,062 | 2,458 | +19% | 0 | 0 | — |
case-04 | fail→pass | 9,310 | 4,968 | -47% | 1 | 1 | 0% | 1,650 | 3,244 | +97% | 0 | 0 | — |
case-05 | fail→pass | 13,298 | 12,458 | -6% | 1 | 1 | 0% | 2,272 | 4,675 | +106% | 0 | 0 | — |
case-06 | pass→pass | 9,642 | 2,669 | -72% | 1 | 1 | 0% | 1,722 | 2,531 | +47% | 0 | 0 | — |
case-07 | fail→pass | 13,502 | 3,560 | -74% | 1 | 1 | 0% | 614 | 2,747 | +347% | 0 | 0 | — |
case-08 | pass→pass | 7,120 | 4,409 | -38% | 1 | 1 | 0% | 1,246 | 2,905 | +133% | 0 | 0 | — |
case-09 | pass→pass | 8,823 | 3,226 | -63% | 1 | 1 | 0% | 1,377 | 2,651 | +93% | 0 | 0 | — |
case-10 | fail→pass | 8,219 | 3,028 | -63% | 1 | 1 | 0% | 1,093 | 2,622 | +140% | 0 | 0 | — |
case-11 | fail→pass | 25,690 | 1,838 | -93% | 1 | 1 | 0% | 2,421 | 2,383 | -2% | 0 | 0 | — |
case-15 | fail→pass | 7,997 | 1,396 | -83% | 1 | 1 | 0% | 1,175 | 2,340 | +99% | 0 | 0 | — |
case-16 | fail→pass | 7,596 | 1,756 | -77% | 1 | 1 | 0% | 1,290 | 2,414 | +87% | 0 | 0 | — |
case-17 | fail→pass | 6,142 | 1,922 | -69% | 1 | 1 | 0% | 914 | 2,386 | +161% | 0 | 0 | — |
case-18 | fail→pass | 2,960 | 1,519 | -49% | 1 | 1 | 0% | 460 | 2,345 | +410% | 0 | 0 | — |
case-19 | pass→pass | 9,470 | 1,670 | -82% | 1 | 1 | 0% | 1,591 | 2,368 | +49% | 0 | 0 | — |
case-20 | fail→fail | 15,533 | 14,818 | -5% | 1 | 1 | 0% | 2,735 | 4,555 | +67% | 0 | 0 | — |
case-21 | fail→fail | 16,934 | 14,720 | -13% | 1 | 1 | 0% | 3,205 | 5,161 | +61% | 0 | 0 | — |
case-22 | fail→fail | 11,016 | 9,135 | -17% | 1 | 1 | 0% | 2,207 | 3,712 | +68% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +55 percentage points is the difference between those two pass rates over the 20 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.