{"id":18717,"plugin_id":"plugins_6a8874a5fe5081919d0e22dacb040180","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T23:14:54.254Z","digest":"9676c05492aa045bee1b95be787fe73cbda047ecb1dbae0d6b6949dfbda77932","against":null,"payload":{"description":"Design and improve pricing architecture using customer value, willingness to pay, segmentation, packaging, metrics, costs, competition, anchors, discounts, subscriptions, experiments, and guardrails. Use for setting prices, packaging tiers, price increases, monetization, usage-based pricing, or discount strategy.","included_files":[{"relative_path":"agents/openai.yaml","size_in_bytes":214}],"name":"optimize-pricing","skill_md_contents":"---\nname: optimize-pricing\ndescription: Design and improve pricing architecture using customer value, willingness to pay, segmentation, packaging, metrics, costs, competition, anchors, discounts, subscriptions, experiments, and guardrails. Use for setting prices, packaging tiers, price increases, monetization, usage-based pricing, or discount strategy.\n---\n# Optimize Pricing\n1. Define customer segments, value created, purchase context, alternatives, costs, capacity, objectives, and constraints.\n2. Research current competitor prices carefully, normalizing currency, tax, billing period, limits, and service level.\n3. Choose value metric, fences, tiers, packaging, anchors, minimums, overages, discounts, and migration policy.\n4. Model revenue, margin, adoption, churn, fairness, complexity, and edge cases across scenarios.\n5. Design research or experiments with guardrail metrics and customer communication.\n6. Return pricing thesis, package table, economics, comparison, experiment, rollout, objection handling, and review triggers.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}