[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X7YEX3NTXGXAY8SQRSK7YG":3,"skill-same:01M6X7YEX3NTXGXAY8SQRSK7YG":67},{"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":23,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":51,"updatedAt":52,"install":53,"resources":66},"01M6X7YEX3NTXGXAY8SQRSK7YG","trend-to-product-mapper",null,"Maps viral social content and trending topics to concrete app opportunities by extracting the underlying problem and validating monetization fit.","MaxKmet\u002Fidea-validation-agents","MaxKmet","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19517077?v=4","01M6X7QNAFCSMWXN7FDJS2KFGY","Max Kmet","01M6X7QNEEA69XPH7DCQRS5NJE",512,"MIT","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-to-product-mapper\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"product","trend","mapper","trend to product mapper","content","social content","maps","social","monetization","viral","app","fit","problem","trending","extracting","topics","2026-11-10 10:12:30",false,"---\nname: trend-to-product-mapper\ndescription: Maps viral social content and trending topics to concrete app opportunities by extracting the underlying problem and validating monetization fit.\n---\n\n\u003C!-- version: 0.3.0 | outputs: memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md (up to 10 per run) -->\n\n# Skill: trend-to-product-mapper\n\n## Purpose\n\nSurface app ideas from real-world signals rather than speculation. The pipeline is: viral content → extract problem → map to app → validate monetization. This skill bridges social listening and product ideation.\n\n## User Interaction\n\nBefore executing, clarify which niche to analyze. Use available context to suggest options — don't ask blindly.\n\n**Step 1 — Infer candidates from context:**\n\nCheck in this order:\n1. `memory\u002Fmarket_insights\u002F` — list any existing trend-analysis files; extract niche names from filenames (e.g., `nutrition-tiktok-2026-04.md` → \"nutrition\")\n2. `memory\u002Fuser_profile.md` — check for `domain`, `interests`, or `background` fields\n3. The current conversation — did the user mention a topic or market earlier?\n\n**Step 2 — Present options:**\n\nIf candidates were found from context:\n\n> Which niche would you like to map to a product opportunity?\n>\n> Based on available research:\n> - **[inferred niche]** _(trend data: [platform] [period])_\n> - **[other inferred niches if any]**\n>\n> Or describe your own — e.g., \"productivity tools for freelancers\", \"pet care\", \"language learning\"\n\nIf no candidates were found:\n\n> What niche or market category would you like to explore? A few starting points:\n> - Fitness \u002F nutrition \u002F weight loss\n> - Personal finance \u002F investing\n> - Mental health \u002F mindfulness\n> - Productivity \u002F focus\n> - Or describe your own in a few words\n\n**Step 3 — Confirm:**\n\nOnce the user selects or describes a niche, confirm it back and proceed to Process.\n\n## Input\n\n- Target niche (e.g., \"nutrition\", \"fitness\", \"personal finance\")\n- `memory\u002Fmarket_insights\u002F\u003Cniche>-\u003Cplatform>-\u003CYYYY>-\u003CMM>.md` — one or more trend-analysis output files for the niche. Read the full narrative (Part 2) from each file; do not rely solely on the YAML frontmatter.\n- `memory\u002Fuser_profile.md` to filter for user's domain fit and constraints\n\n## Pipeline\n\n```\ntrend-analysis output → scan for distinct opportunities → extract problem per opportunity → validate monetization → write up to 10 idea.md files\n```\n\n### What to Read from Trend-Analysis Output\n\n| Section in trend-analysis file | What to extract |\n|---|---|\n| Emerging \u002F Rising Trends | Fastest-moving problems and content angles |\n| Financial Opportunities | Willingness-to-pay evidence and market size estimates |\n| Key Hashtags \u002F Subreddits \u002F Keyword Clusters | Vocabulary the audience uses for the problem |\n| Strategic Insights | Creator gaps and underserved segments |\n| `monetization_evidence` (YAML frontmatter) | Quick-scan: is anyone already paying? |\n\nPrefer signals that appear across **multiple platforms** — cross-platform resonance is a stronger product signal than single-platform virality.\n\n## Process\n\n1. Read available trend-analysis files relevant for the niche from `memory\u002Fmarket_insights\u002F`.\n2. Scan the full narrative of each file and identify **distinct** product opportunities — different underlying problems, different audience segments, or different app categories count as distinct. Do not list variations of the same idea.\n3. Rank candidates by signal strength: weight cross-platform resonance and willingness-to-pay evidence most heavily. Drop candidates with no monetization signal.\n4. Take the top 5–10 candidates (only include as many as have genuine signal — do not pad to reach 10).\n5. For each candidate: extract the underlying problem (frustration or desire, not content topic), emotional trigger, audience vocabulary, app category, key features, key differentiator, and monetization evidence.\n6. Assign a slug to each idea (kebab-case, max 40 chars, derived from the app concept).\n7. Write one `idea.md` per idea to its own directory: `memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md`.\n\n## Output\n\nFor each identified opportunity, write to `memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md`.\n\nThe file uses YAML frontmatter for machine-readable metadata and a full narrative body for human readability and downstream skill consumption.\n\n### Frontmatter\n\n```yaml\n---\nidea_slug: \u003Cslug>\nstatus: candidate\ncreated_at: \u003CISO date>\nsource_niche: \u003Cniche>\nsource_files: []        # memory\u002Fmarket_insights\u002F filenames read\nplatforms_covered: []   # e.g. [\"tiktok\", \"reddit\"]\ntrend_velocity: rising-fast | rising | stable | declining\ncross_platform_resonance: true | false\nmonetization_validated: true | false\nconfidence: high | medium | low\n---\n```\n\n### Body\n\nWrite the following sections in full prose or structured lists — no abbreviation:\n\n```markdown\n# \u003CApp Concept Name>\n\n## The Problem\nWhat specific frustration or unmet desire is this idea addressing? Describe it from the user's perspective — the emotional experience, not the feature gap. Include the exact vocabulary the audience uses.\n\n**Emotional trigger:** \u003Cthe core feeling driving the behavior — anxiety, FOMO, shame, aspiration, etc.>\n\n**Audience vocabulary:** \u003C3–5 exact phrases pulled from hashtags, post titles, or search queries>\n\n## Market Signal Evidence\nWhat trend data supports this? For each platform covered, cite the specific signal:\n\n- **TikTok:** \u003Chashtag, view count, content angle>\n- **Reddit:** \u003Csubreddit, recurring post type, upvote pattern>\n- **App Store:** \u003Ccategory trend, review complaint pattern, new entrant activity>\n- **Web Search:** \u003Crising query, search volume indicator>\n\n**Trend velocity:** \u003Crising-fast | rising | stable | declining>\n**Cross-platform resonance:** \u003Cyes\u002Fno — does the same problem appear on 2+ platforms?>\n\n## App Concept\nWhat is the app? Describe it in 2–3 sentences as if pitching to a user, not an investor. Focus on what it does and who it's for.\n\n**App category:** \u003Ce.g., habit tracker, AI coach, marketplace, tool>\n\n## Key Features\nThe 3–5 core features that directly address the problem. Each feature should map to a specific pain point or desire from the Market Signal Evidence section.\n\n1. **\u003CFeature name>** — \u003Cwhat it does and why it matters>\n2. ...\n\n## Key Differentiator\nWhat makes this meaningfully different from what already exists? Reference the saturation assessment from the trend-analysis Financial Opportunities section. One clear wedge — not a feature list.\n\n## Monetization Evidence\nWhat proof exists that people pay for solutions to this problem?\n\n- \u003Cexisting product \u002F revenue signal \u002F pricing evidence>\n- ...\n\n**Monetization validated:** \u003Cyes\u002Fno>\n\n## Confidence Assessment\n**Overall confidence:** \u003Chigh | medium | low>\n\nReasoning: \u003C1–2 sentences explaining the confidence level — what's strong, what's uncertain>\n```\n\nAfter writing all files, present a summary table to the user:\n\n| # | Slug | App Concept | Confidence | Cross-Platform | Monetization |\n|---|---|---|---|---|---|\n| 1 | `\u003Cslug>` | ... | high\u002Fmedium\u002Flow | yes\u002Fno | validated\u002Funvalidated |\n| ... | | | | | |\n\n## Notes\n\n\u003C!-- TODO: Define what counts as \"monetization validated\" — IH revenue post? App Store paid app? -->\n","\u003C!-- version: 0.3.0 | outputs: memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md (up to 10 per run) -->\n\n# Skill: trend-to-product-mapper\n\n## Purpose\n\nSurface app ideas from real-world signals rather than speculation. The pipeline is: viral content → extract problem → map to app → validate monetization. This skill bridges social listening and product ideation.\n\n## User Interaction\n\nBefore executing, clarify which niche to analyze. Use available context to suggest options — don't ask blindly.\n\n**Step 1 — Infer candidates from context:**\n\nCheck in this order:\n1. `memory\u002Fmarket_insights\u002F` — list any existing trend-analysis files; extract niche names from filenames (e.g., `nutrition-tiktok-2026-04.md` → \"nutrition\")\n2. `memory\u002Fuser_profile.md` — check for `domain`, `interests`, or `background` fields\n3. The current conversation — did the user mention a topic or market earlier?\n\n**Step 2 — Present options:**\n\nIf candidates were found from context:\n\n> Which niche would you like to map to a product opportunity?\n>\n> Based on available research:\n> - **[inferred niche]** _(trend data: [platform] [period])_\n> - **[other inferred niches if any]**\n>\n> Or describe your own — e.g., \"productivity tools for freelancers\", \"pet care\", \"language learning\"\n\nIf no candidates were found:\n\n> What niche or market category would you like to explore? A few starting points:\n> - Fitness \u002F nutrition \u002F weight loss\n> - Personal finance \u002F investing\n> - Mental health \u002F mindfulness\n> - Productivity \u002F focus\n> - Or describe your own in a few words\n\n**Step 3 — Confirm:**\n\nOnce the user selects or describes a niche, confirm it back and proceed to Process.\n\n## Input\n\n- Target niche (e.g., \"nutrition\", \"fitness\", \"personal finance\")\n- `memory\u002Fmarket_insights\u002F\u003Cniche>-\u003Cplatform>-\u003CYYYY>-\u003CMM>.md` — one or more trend-analysis output files for the niche. Read the full narrative (Part 2) from each file; do not rely solely on the YAML frontmatter.\n- `memory\u002Fuser_profile.md` to filter for user's domain fit and constraints\n\n## Pipeline\n\n```\ntrend-analysis output → scan for distinct opportunities → extract problem per opportunity → validate monetization → write up to 10 idea.md files\n```\n\n### What to Read from Trend-Analysis Output\n\n| Section in trend-analysis file | What to extract |\n|---|---|\n| Emerging \u002F Rising Trends | Fastest-moving problems and content angles |\n| Financial Opportunities | Willingness-to-pay evidence and market size estimates |\n| Key Hashtags \u002F Subreddits \u002F Keyword Clusters | Vocabulary the audience uses for the problem |\n| Strategic Insights | Creator gaps and underserved segments |\n| `monetization_evidence` (YAML frontmatter) | Quick-scan: is anyone already paying? |\n\nPrefer signals that appear across **multiple platforms** — cross-platform resonance is a stronger product signal than single-platform virality.\n\n## Process\n\n1. Read available trend-analysis files relevant for the niche from `memory\u002Fmarket_insights\u002F`.\n2. Scan the full narrative of each file and identify **distinct** product opportunities — different underlying problems, different audience segments, or different app categories count as distinct. Do not list variations of the same idea.\n3. Rank candidates by signal strength: weight cross-platform resonance and willingness-to-pay evidence most heavily. Drop candidates with no monetization signal.\n4. Take the top 5–10 candidates (only include as many as have genuine signal — do not pad to reach 10).\n5. For each candidate: extract the underlying problem (frustration or desire, not content topic), emotional trigger, audience vocabulary, app category, key features, key differentiator, and monetization evidence.\n6. Assign a slug to each idea (kebab-case, max 40 chars, derived from the app concept).\n7. Write one `idea.md` per idea to its own directory: `memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md`.\n\n## Output\n\nFor each identified opportunity, write to `memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md`.\n\nThe file uses YAML frontmatter for machine-readable metadata and a full narrative body for human readability and downstream skill consumption.\n\n### Frontmatter\n\n```yaml\n---\nidea_slug: \u003Cslug>\nstatus: candidate\ncreated_at: \u003CISO date>\nsource_niche: \u003Cniche>\nsource_files: []        # memory\u002Fmarket_insights\u002F filenames read\nplatforms_covered: []   # e.g. [\"tiktok\", \"reddit\"]\ntrend_velocity: rising-fast | rising | stable | declining\ncross_platform_resonance: true | false\nmonetization_validated: true | false\nconfidence: high | medium | low\n---\n```\n\n### Body\n\nWrite the following sections in full prose or structured lists — no abbreviation:\n\n```markdown\n# \u003CApp Concept Name>\n\n## The Problem\nWhat specific frustration or unmet desire is this idea addressing? Describe it from the user's perspective — the emotional experience, not the feature gap. Include the exact vocabulary the audience uses.\n\n**Emotional trigger:** \u003Cthe core feeling driving the behavior — anxiety, FOMO, shame, aspiration, etc.>\n\n**Audience vocabulary:** \u003C3–5 exact phrases pulled from hashtags, post titles, or search queries>\n\n## Market Signal Evidence\nWhat trend data supports this? For each platform covered, cite the specific signal:\n\n- **TikTok:** \u003Chashtag, view count, content angle>\n- **Reddit:** \u003Csubreddit, recurring post type, upvote pattern>\n- **App Store:** \u003Ccategory trend, review complaint pattern, new entrant activity>\n- **Web Search:** \u003Crising query, search volume indicator>\n\n**Trend velocity:** \u003Crising-fast | rising | stable | declining>\n**Cross-platform resonance:** \u003Cyes\u002Fno — does the same problem appear on 2+ platforms?>\n\n## App Concept\nWhat is the app? Describe it in 2–3 sentences as if pitching to a user, not an investor. Focus on what it does and who it's for.\n\n**App category:** \u003Ce.g., habit tracker, AI coach, marketplace, tool>\n\n## Key Features\nThe 3–5 core features that directly address the problem. Each feature should map to a specific pain point or desire from the Market Signal Evidence section.\n\n1. **\u003CFeature name>** — \u003Cwhat it does and why it matters>\n2. ...\n\n## Key Differentiator\nWhat makes this meaningfully different from what already exists? Reference the saturation assessment from the trend-analysis Financial Opportunities section. One clear wedge — not a feature list.\n\n## Monetization Evidence\nWhat proof exists that people pay for solutions to this problem?\n\n- \u003Cexisting product \u002F revenue signal \u002F pricing evidence>\n- ...\n\n**Monetization validated:** \u003Cyes\u002Fno>\n\n## Confidence Assessment\n**Overall confidence:** \u003Chigh | medium | low>\n\nReasoning: \u003C1–2 sentences explaining the confidence level — what's strong, what's uncertain>\n```\n\nAfter writing all files, present a summary table to the user:\n\n| # | Slug | App Concept | Confidence | Cross-Platform | Monetization |\n|---|---|---|---|---|---|\n| 1 | `\u003Cslug>` | ... | high\u002Fmedium\u002Flow | yes\u002Fno | validated\u002Funvalidated |\n| ... | | | | | |\n\n## Notes\n\n\u003C!-- TODO: Define what counts as \"monetization validated\" — IH revenue post? App Store paid app? -->\n",{"name":5,"description":7},"main","3a4c800a3022e73e9997533b603c79b60ee7597a","skills\u002Ftrend-to-product-mapper",58,"https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents","https:\u002F\u002Fgithub.com\u002FMaxKmet","Senior AI Engineer","2026-06-16 23:18:56",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add MaxKmet\u002Fidea-validation-agents --skill trend-to-product-mapper",{"tool":59,"command":60},"bunx","bunx skills add MaxKmet\u002Fidea-validation-agents --skill trend-to-product-mapper",{"tool":62,"command":63},"pnpm","pnpm dlx skills add MaxKmet\u002Fidea-validation-agents --skill trend-to-product-mapper","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-to-product-mapper\u002FSKILL.md",true,[],[68,84,110,128,150,169],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":40},"01M6X7YZ5WBGSAB23K0DAARY1N","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\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fweakness-detection\u002FSKILL.md",[],[75,76,77,78,79,80,81,82,83],"weakness detection","detection","modes","root","output","as","dimensions","scoring","idea",{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":95,"publishedAt":40},"01M6X7YRHWXGBHBT70ZY1YTCC6","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\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fuser-segmentation-profiler\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[96,97,98,99,100,101,102,103,104,105,106,107,108,109],"segmentation","user","user segmentation","user segmentation profiler","profiler","workflow","run","constraints","session","growth","builder","goals","icp","beginner",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":40},"01M6X7YJKM9DRPMEDW22PETKDE","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\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fuser-background-interviewer\u002FSKILL.md",[],[97,117,118,119,120,121,122,123,124,34,125,126,127],"background","user background interviewer","networks","knowledge","interview","domain","ideas","deep","understand","types","with",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":40},"01M6X7Y9PJGB5W9Y2RGC7MMC8T","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\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-analysis\u002FSKILL.md",[],[135,25,136,137,138,139,140,141,142,34,143,144,145,146,147,148,149],"trend analysis","analysis","trends","app store","file","google trends","insights","store","across","tiktok","reddit","Market","for","memory","google",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":40},"01M6X7Y5GHKXC295FGVJN4Y93R","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\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftam-sam-som-builder\u002FSKILL.md",[],[157,158,106,159,25,160,161,83,162,163,146,164,105,165,166,167,147,141,168,34],"tam sam som builder","sam","data","signals","velocity","community","benchmarks","up","proxies","competitor","rate","revenue",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":40},"01M6X7Y2P6FGZWH8GNJMDNZ2E0","retention-predictor","Predicts retention potential by evaluating usage frequency, habit formation mechanics, and churn risk factors for a B2C app idea.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fretention-predictor\u002FSKILL.md",[],[176,177,178,147,179,180,34,181,83,182,183],"retention","retention predictor","churn risk","usage","churn","mechanics","habit","risk"]