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Compare synthetic cross-sell portfolio value scenarios for human-reviewed decision support.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "cross-selling-revenue-impact" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/aibast-agents-library/main/solutions/cross-selling/manual/skills/revenue-impact/SKILL.md 2. Save it as ~/.claude/skills/revenue-impact/SKILL.md 3. Reload skills and tell me it's ready
Run the revenue_impact operation and compare the portfolio’s synthetic cross-sell value scenarios using only the two uploaded knowledge files in this package. Use only the fixed synthetic snapshot, do not add external data, and do not make conversion, revenue, or margin claims. Include Synthetic Cross-Sell Value Scenario, Portfolio Totals, and Evidence boundary in the output.
A synthetic evidence-based scenario comparison with portfolio totals and a clear evidence boundary.
Read the two uploaded knowledge files and operating rules, extract verifiable synthetic identifiers and scenario differences, and produce a read-only decision support summary. Do not assign owners, create tasks, update CRM, or contact customers.
A human-review-only analysis summary listing scenario differences and related synthetic evidence.
Check whether this cross-sell value analysis uses only the fixed synthetic snapshot and confirm that the output includes the markers Synthetic Cross-Sell Value Scenario, Portfolio Totals, and Evidence boundary.
A compliance check result stating whether the fixed output structure and read-only boundary requirements are met.
Sales leaders can use it to compare cross-sell value scenarios across a portfolio using fixed synthetic data and produce a read-only analysis for human review. It fits situations where portfolio-level differences must be assessed without making commercial claims.
Analysts can use the two uploaded knowledge files and the defined rules to structure scenario analysis outputs with fixed markers. It is useful when synthetic identifiers, totals, and an explicit evidence boundary must be preserved.
The document describes this skill as a sales-leader-focused “Cross-Selling Opportunities Agent — Revenue Impact,” but its role is tightly limited to comparing scenarios within a fixed synthetic snapshot. It may use only the two uploaded knowledge files, must not bring in external data, and must include the exact markers Synthetic Cross-Sell Value Scenario, Portfolio Totals, and Evidence boundary. All outputs are read-only decision support for authorized human review.
Sales Leader
revenue_impactsynthetic onlyrevenue_impact.Synthetic Cross-Sell Value Scenario, Portfolio Totals, Evidence boundary anchors.All exact names, dates, counts, prices, amounts, scores, percentages, and projections are synthetic test evidence. The response is read-only decision support. Do not claim that outreach was sent, a CRM record changed, a task or alert was created, pricing or an approval was granted, a proposal was delivered, or any customer communication occurred.
Compare the bundled portfolio synthetic value scenarios without making conversion, revenue, or margin claims.
The response must include Synthetic Cross-Sell Value Scenario, Portfolio Totals, Evidence boundary and preserve the explicit synthetic evidence boundary.
It compares portfolio-level synthetic cross-sell value scenarios and outputs read-only decision support analysis. Based on the description and docs, it does not make conversion, revenue, or margin claims.
The documentation says the data source is synthetic only, and it may use only the two uploaded knowledge files in the package. It must not browse, enrich, infer, or use external data.
No. The docs explicitly require the output to remain read-only decision support and forbid outreach, CRM updates, task or alert creation, forecast changes, pricing approvals, or customer contact.
Rank a fictional contract portfolio to prioritize legal review attention.
Explain synthetic cross-sell affinity rules and benchmark assumptions from fixed evidence.
Compare contract clauses against internal policy requirements and identify gaps.
Draft prioritized amendment positions and legal escalation points for contract renegotiations.
Scan synthetic customer records to find evidence-backed cross-sell product gaps.
Draft prioritized cross-sell recommendations and a reviewable engagement plan from synthetic data.
Build weighted sales forecasts with scenario planning, commit splits, and gap analysis.
Synthesize user research into themes, insights, and prioritized recommendations.
Turn raw research feedback into structured insights and prioritized recommendations.
Prepare reviewable retention and service-recovery options for churn-related customer cases.
Compare one prompt across multiple models side by side with public verification receipts.
Qualify leads, score ICP fit, forecast pipeline, and plan outreach.