[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M72ACMDF8NF42DHQJ121FKDB":3,"skill-same:01M72ACMDF8NF42DHQJ121FKDB":47},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":30,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M72ACMDF8NF42DHQJ121FKDB","concept-testing",null,"Test product and messaging concepts with target users to learn appeal, clarity, and preference before heavy build investment. Use when evaluating early ideas, value propositions, or design directions with structured feedback.","itsual\u002Fagent-skills-collection","itsual","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F47685900?v=4","01M7291J4K9RE0EWMQB0JM3WFN","Arockia Liborious","01M7291J58T0363Q45MQP9KESY",7,"MIT","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fconcept-testing\u002FSKILL.md",[],[],"2026-11-10 16:52:40",false,"---\nname: concept-testing\ndescription: >\n  Test product and messaging concepts with target users to learn appeal, clarity, and preference before heavy build investment.\n  Use when evaluating early ideas, value propositions, or design directions with structured feedback.\n---\n\n# Concept Testing\n\n## Overview\n\nConcept testing evaluates ideas while they are still cheap to change. It measures understanding, desirability, and relative preference — not final usability of a finished product.\n\n## When to Use\n\n- Early product or feature concepts\n- Value proposition and messaging tests\n- Comparing alternative directions\n- Go\u002Fno-go inputs before roadmap commitment\n\n## Core Practices\n\n- Present concepts consistently (fidelity matched to the question)\n- Test comprehension before preference\n- Ask about job relevance and differentiation from alternatives\n- Use both qualitative probes and structured ratings when useful\n- Avoid over-indexing on polite enthusiasm\n- Compare concepts against each other and against status quo\n\n## Principles\n\n- Stated intent ≠ future behavior — treat as directional\n- Higher fidelity is not always better for early concepts\n- Audience quality matters more than sample size theater\n- Kill weak concepts quickly; don’t “average” them into mediocrity\n\n## Verification\n\n- [ ] Concepts were understood as intended\n- [ ] Evaluation criteria match the decision needed\n- [ ] Results distinguish winners from polite nods\n","# Concept Testing\n\n## Overview\n\nConcept testing evaluates ideas while they are still cheap to change. It measures understanding, desirability, and relative preference — not final usability of a finished product.\n\n## When to Use\n\n- Early product or feature concepts\n- Value proposition and messaging tests\n- Comparing alternative directions\n- Go\u002Fno-go inputs before roadmap commitment\n\n## Core Practices\n\n- Present concepts consistently (fidelity matched to the question)\n- Test comprehension before preference\n- Ask about job relevance and differentiation from alternatives\n- Use both qualitative probes and structured ratings when useful\n- Avoid over-indexing on polite enthusiasm\n- Compare concepts against each other and against status quo\n\n## Principles\n\n- Stated intent ≠ future behavior — treat as directional\n- Higher fidelity is not always better for early concepts\n- Audience quality matters more than sample size theater\n- Kill weak concepts quickly; don’t “average” them into mediocrity\n\n## Verification\n\n- [ ] Concepts were understood as intended\n- [ ] Evaluation criteria match the decision needed\n- [ ] Results distinguish winners from polite nods\n",{"name":5,"description":7},"main","8d2fd11b59496bfb3792e747e5519817e554e6cf","skills\u002Fresearch\u002Fconcept-testing",1,"https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection","https:\u002F\u002Fgithub.com\u002Fitsual","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Farockialiborious\u002F","Data Science & Analytics Leader | Helping organizations rethink their data-driven decision making process | AI Storyteller","2026-07-22 23:04:48",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add itsual\u002Fagent-skills-collection --skill concept-testing",{"tool":39,"command":40},"bunx","bunx skills add itsual\u002Fagent-skills-collection --skill concept-testing",{"tool":42,"command":43},"pnpm","pnpm dlx skills add itsual\u002Fagent-skills-collection --skill concept-testing","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fresearch\u002Fconcept-testing\u002FSKILL.md",true,[],[48,56,63,70,77,85],{"skillNo":49,"name":50,"nameZh":6,"description":51,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":55},"01M7297PK9X4VZRN6HAD9A58M9","compliance-monitoring","Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fcompliance-monitoring\u002FSKILL.md",[],[],"2026-11-10 16:53:54",{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":62,"publishedAt":55},"01M7297KXVGGV1CH7CEHA62FAT","complaints-handling","Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fcomplaints-handling\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":55},"01M7297HXEX2PSHBEDVWRJC924","aml-kyc-basics","Apply AML\u002FKYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Faml-kyc-basics\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":55},"01M7297G229RT4FXA2KW7EG2K6","ai-model-compliance","Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fai-model-compliance\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":84},"01M729KEEE7VTT48V1J7YMS8GA","data-quality-management","Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-quality-management\u002FSKILL.md",[],[],"2026-11-10 16:53:43",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":84},"01M729K3VDSZC6TF67TYQ4Z8W9","data-lineage","Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-lineage\u002FSKILL.md",[],[]]