[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7DM5DZ7P8XVMASJ9R5MVB7R":3,"skill-same:01M7DM5DZ7P8XVMASJ9R5MVB7R":43},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":23,"commitSha":24,"skillDirectory":25,"forksCount":13,"repositoryHtmlUrl":26,"repositoryHomepageUrl":6,"ownerHtmlUrl":27,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":28,"install":29,"resources":42},"01M7DM5DZ7P8XVMASJ9R5MVB7R","retention-predictor",null,"Predicts retention potential by evaluating usage frequency, habit formation mechanics, and churn risk factors for a B2C app idea.","nhatthien88\u002Fbuild-or-not","nhatthien88","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F177452460?v=4","01M7DM2PG2TBKY8QAEK2HXGSDY","01M7DM2PGKSX14NW40H5YDTJQQ",0,"MIT","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Fretention-predictor\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: retention-predictor\ndescription: Predicts retention potential by evaluating usage frequency, habit formation mechanics, and churn risk factors for a B2C app idea.\n---\n\n\u003C!-- version: 0.1.0 | outputs: memory\u002Fideas\u002F\u003Cslug>\u002Fretention.json -->\n\n# Skill: retention-predictor\n\n## Purpose\n\nRetention determines LTV. An app that churns users in week 1 can't build a business regardless of acquisition. This skill evaluates how sticky the idea is structurally — not based on feature lists, but on the underlying usage pattern and habit formation potential.\n\n## Input\n\n- Idea slug\n- App concept description\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fdesire_scores.json` (desire strength informs habit potential)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fuser_extraction.json` (usage frequency from pain map)\n\n## Evaluation Factors\n\n| Factor | High Retention Signal | Low Retention Signal |\n|---|---|---|\n| Usage frequency | Daily or multiple times\u002Fday | Weekly or less |\n| External trigger | Clear real-world trigger (meal, workout, payday) | No natural trigger |\n| Progress\u002Freward loop | Clear progress visible over time | No feedback loop |\n| Network effects | Gets better with more users | No network component |\n| Data lock-in | User data accumulates | Nothing to lose by leaving |\n| Habit stack | Fits into existing daily routine | Requires behavior change |\n\n## Process\n\n\u003C!-- TODO: Add D1\u002FD7\u002FD30 retention benchmark database by app category -->\n\u003C!-- TODO: Add churn risk scoring rubric -->\n\n1. Estimate natural usage frequency based on the problem (daily tooth brushing vs. annual tax filing).\n2. Identify external triggers that would cue app usage.\n3. Score habit formation potential (1–5) across the six factors above.\n4. Estimate D1, D7, D30 retention benchmarks for the app category.\n5. Flag high churn risk factors.\n\n## Output\n\nWrite to `memory\u002Fideas\u002F\u003Cslug>\u002Fretention.json`:\n\n```json\n{\n  \"natural_usage_frequency\": \"multiple daily | daily | weekly | monthly | infrequent\",\n  \"external_trigger\": \"\",\n  \"habit_formation_score\": 0,\n  \"churn_risk_factors\": [],\n  \"estimated_retention\": {\n    \"d1\": 0,\n    \"d7\": 0,\n    \"d30\": 0\n  },\n  \"churn_risk\": \"low | medium | high\",\n  \"retention_verdict\": \"sticky | moderate | disposable\"\n}\n```\n\n## Notes\n\n\u003C!-- TODO: Cross-reference with desire_scores — survival\u002Fcontrol desires = higher retention -->\n","\u003C!-- version: 0.1.0 | outputs: memory\u002Fideas\u002F\u003Cslug>\u002Fretention.json -->\n\n# Skill: retention-predictor\n\n## Purpose\n\nRetention determines LTV. An app that churns users in week 1 can't build a business regardless of acquisition. This skill evaluates how sticky the idea is structurally — not based on feature lists, but on the underlying usage pattern and habit formation potential.\n\n## Input\n\n- Idea slug\n- App concept description\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fdesire_scores.json` (desire strength informs habit potential)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fuser_extraction.json` (usage frequency from pain map)\n\n## Evaluation Factors\n\n| Factor | High Retention Signal | Low Retention Signal |\n|---|---|---|\n| Usage frequency | Daily or multiple times\u002Fday | Weekly or less |\n| External trigger | Clear real-world trigger (meal, workout, payday) | No natural trigger |\n| Progress\u002Freward loop | Clear progress visible over time | No feedback loop |\n| Network effects | Gets better with more users | No network component |\n| Data lock-in | User data accumulates | Nothing to lose by leaving |\n| Habit stack | Fits into existing daily routine | Requires behavior change |\n\n## Process\n\n\u003C!-- TODO: Add D1\u002FD7\u002FD30 retention benchmark database by app category -->\n\u003C!-- TODO: Add churn risk scoring rubric -->\n\n1. Estimate natural usage frequency based on the problem (daily tooth brushing vs. annual tax filing).\n2. Identify external triggers that would cue app usage.\n3. Score habit formation potential (1–5) across the six factors above.\n4. Estimate D1, D7, D30 retention benchmarks for the app category.\n5. Flag high churn risk factors.\n\n## Output\n\nWrite to `memory\u002Fideas\u002F\u003Cslug>\u002Fretention.json`:\n\n```json\n{\n  \"natural_usage_frequency\": \"multiple daily | daily | weekly | monthly | infrequent\",\n  \"external_trigger\": \"\",\n  \"habit_formation_score\": 0,\n  \"churn_risk_factors\": [],\n  \"estimated_retention\": {\n    \"d1\": 0,\n    \"d7\": 0,\n    \"d30\": 0\n  },\n  \"churn_risk\": \"low | medium | high\",\n  \"retention_verdict\": \"sticky | moderate | disposable\"\n}\n```\n\n## Notes\n\n\u003C!-- TODO: Cross-reference with desire_scores — survival\u002Fcontrol desires = higher retention -->\n",{"name":5,"description":7},"main","fb4ef451a874815443d48b1431430c193b9bfd20","skills\u002Fretention-predictor","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not","https:\u002F\u002Fgithub.com\u002Fnhatthien88","2026-10-01 16:26:24",{"source":8,"skillName":5,"commands":30,"prompt":40,"zipAvailable":41},[31,34,37],{"tool":32,"command":33},"npx","npx skills add nhatthien88\u002Fbuild-or-not --skill retention-predictor",{"tool":35,"command":36},"bunx","bunx skills add nhatthien88\u002Fbuild-or-not --skill retention-predictor",{"tool":38,"command":39},"pnpm","pnpm dlx skills add nhatthien88\u002Fbuild-or-not --skill retention-predictor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Fretention-predictor\u002FSKILL.md",true,[],[44,51,58,65,72,79],{"skillNo":45,"name":46,"nameZh":6,"description":47,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":48,"categories":49,"keywords":50,"publishedAt":18},"01M7DM60Q4E7YC011017N0W5F7","weakness-detection","Analyzes idea-scoring output to identify the weakest dimensions, root causes of low scores, and probable failure modes if the idea were pursued as-is.","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Fweakness-detection\u002FSKILL.md",[],[],{"skillNo":52,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":57,"publishedAt":18},"01M7DM5YKFV7Y029BZ8WJ72TWR","user-segmentation-profiler","Classifies the user into an ICP tier (beginner\u002Fbuilder\u002Fgrowth) based on experience, constraints, and goals. Run once per user session before any workflow.","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Fuser-segmentation-profiler\u002FSKILL.md",[],[],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":18},"01M7DM5TDNG89M72JXVBGJ93RV","user-background-interviewer","Conducts a deep background interview to understand what types of app ideas this specific user is most likely to succeed with based on their domain knowledge, networks, and skills.","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Fuser-background-interviewer\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":18},"01M7DM5QSXAS4G5PKPJJB5XXRY","trend-to-product-mapper","Maps viral social content and trending topics to concrete app opportunities by extracting the underlying problem and validating monetization fit.","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-to-product-mapper\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":18},"01M7DM5N1D5WEXXNV3JDQ2JF2P","trend-analysis","Analyzes market trends across platforms (TikTok, Reddit, App Store, Google Trends) for a given topic or category. Writes a new file to memory\u002Fmarket_insights\u002F.","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-analysis\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":18},"01M7DM5GCWKXCEVC5YEH9N0NVY","tam-sam-som-builder","Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to market_insights trend data, competitor revenue proxies, and community size signals. Includes indie capture rate benchmarks and growth-rate adjustments by trend velocity.","https:\u002F\u002Fgithub.com\u002Fnhatthien88\u002Fbuild-or-not\u002Fblob\u002Fmain\u002Fskills\u002Ftam-sam-som-builder\u002FSKILL.md",[],[]]