Skill: danyangliu-sandwichlab/sales-proposal-simulator (version 1.0.0)

--- name: sales-proposal-simulator description: Build persuasive sales proposals and close plans for ad services spanning Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads. --- # Ads Sales Proposal ## Purpose Core mission: - proposal architecture, objection handling, closing path This skill is specialized for advertising workflows and should output actionable plans rather than generic advice. ## When To Trigger Use this skill when the user asks for: - ad execution guidance tied to business outcomes - growth decisions involving revenue, roas, cpa, or budget efficiency - platform-level actions for: Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads - this specific capability: proposal architecture, objection handling, closing path High-signal keywords: - ads, advertising, campaign, growth, revenue, profit - roas, cpa, roi, budget, bidding, traffic, conversion, funnel - meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp ## Input Contract Required: - client_request: what the client wants to achieve - business_context: product, margin, seasonality - offer_scope: service package or campaign scope Optional: - objections - competitor_reference - contract_constraints - expected_start_date ## Output Contract 1. Proposal Positioning 2. Scope and Deliverables 3. ROI Logic and KPI Commitment Range 4. Objection Handling Script 5. Close Plan with next steps ## Workflow 1. Summarize client objective and urgency. 2. Translate objective into measurable ad outcomes. 3. Build value argument and risk controls. 4. Prepare objection responses and confidence proof. 5. Output a close-ready proposal structure. ## Decision Rules - If goal is vague, anchor on one north-star KPI first. - If budget is low, propose phased pilot with strict milestone gates. - If trust is low, lead with transparent assumptions and reporting cadence. ## Platform Notes Primary scope: - Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads Platform behavior guidance: - Keep recommendations channel-aware; do not collapse all channels into one generic plan. - For Meta and TikTok Ads, prioritize creative testing cadence. - For Google Ads and Amazon Ads, prioritize demand-capture and query/listing intent. - For DSP/programmatic, prioritize audience control and frequency governance. ## Constraints And Guardrails - Never fabricate metrics or policy outcomes. - Separate observed facts from assumptions. - Use measurable language for each proposed action. - Include at least one rollback or stop-loss condition when spend risk exists. ## Failure Handling And Escalation - If critical inputs are missing, ask for only the minimum required fields. - If platform constraints conflict, show trade-offs and a safe default. - If confidence is low, mark it explicitly and provide a validation checklist. - If high-risk issues appear (policy, billing, tracking breakage), escalate with a structured handoff payload. ## Code Examples ### Proposal Skeleton section: Objective section: Channel Plan section: Budget and KPI Range section: Risks and Mitigation section: Next-step Commitment ### Objection Response Map objection: "CPA too high" response: "Pilot 14 days with capped budget and clear stop-loss" ## Examples ### Example 1: Performance growth proposal Input: - Client wants lower CPA in 30 days - Current channels: Meta + Google Ads Output focus: - pilot scope - KPI promise range - close checklist ### Example 2: New product launch proposal Input: - Client has no baseline data - Wants global ads rollout Output focus: - phased contract scope - risk clauses - reporting cadence ### Example 3: Renewal negotiation proposal Input: - Existing client doubts ROI - Needs proof before extension Output focus: - retrospective evidence summary - revised operating model - signed next-step plan ## Quality Checklist - [ ] Required sections are complete and non-empty - [ ] Trigger keywords include at least 3 registry terms - [ ] Input and output contracts are operationally testable - [ ] Workflow and decision rules are capability-specific - [ ] Platform references are explicit and concrete - [ ] At least 3 practical examples are included


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)