discovery▌

Framework for building a sustainable, weekly practice of customer discovery that keeps product teams making progress toward desired outcomes. Rather than treating discovery as a phase that happens before development, this framework embeds customer learning into the ongoing rhythm of product work so that every decision is informed by fresh evidence.

Category: test \n OpenAPI 产品发现Minimal Viable Test \n Prerequisites \n \n AK/SK is configured. \n GoalsSkill: skills/platform/openapi/alicloud-platform-openapi-product-api-discovery/ 。 \n \n Test Steps \n \n 运行一个产品源抓取脚本。 \n 运行合并脚本。 \n 限制 OPENAPI_META_MAX_PRODUCTS=1 执行元数据抓取。 \n \n Expected Results \n \n output/product-scan/ 下产生最小结果文件。 \n

Structured discovery interview planning with adaptive methodology selection based on research goals and constraints. \n \n Guides product managers through four adaptive questions to define research goals, target segments, access constraints, and interview methodology \n Generates tailored interview frameworks with 5+ context-specific questions, follow-ups, and bias-avoidance guidance for each methodology (Mom Test, Jobs-to-be-Done, switch interviews, timeline mapping) \n Includes opening/closing

Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.

These rules are mandatory. Violating them means the skill is not working correctly.

Follow this workflow to collect products, resolve API metadata, and build summaries for skill planning.

Structured discovery cycle from problem hypothesis to validated solution, orchestrating framing, interviews, synthesis, and experiments. \n \n Guides product managers through six phases over 3–4 weeks: frame the problem, plan research, conduct customer interviews, synthesize insights, generate and validate solutions, and make go/no-go decisions \n Emphasizes continuous discovery practice (1 interview per week) rather than one-time research projects, with decision points between phases to pivot o

You're using a skill that will guide you through auditing and understanding the feature flag landscape in a LaunchDarkly project. Your job is to explore the project, assess the health of its flags, identify what needs attention, and provide actionable recommendations.