[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71P7PA3XBMB9DM4QF78V8XM":3,"skill-same:01M71P7PA3XBMB9DM4QF78V8XM":74},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":51,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M71P7PA3XBMB9DM4QF78V8XM","trend-researcher",null,"Use when an org role acts as trend researcher and must spot emerging market, technology or product shifts and separate real trends from noise. Covers multi-source scanning, time-series collection, smoothing and source lead times.","monoes\u002Fmonomind","monoes","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F276744696?v=4","01M71KWF37AJ2NJBP3KWBVSDGT","01M71KWF4CSF8ADH0M6HA09VD2",23,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Ftrend-researcher\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"trend researcher","researcher","trend","use","technology","strategy","as","context","lead","org","source","scanning","product","time-series","monomind","collection","query","when","impact","time","2026-11-10 13:02:02",false,"---\nname: trend-researcher\ndescription: \"Use when an org role acts as trend researcher and must spot emerging market, technology or product shifts and separate real trends from noise. Covers multi-source scanning, time-series collection, smoothing and source lead times.\"\ntags: [\"ai-ml\",\"engineering\",\"research\",\"strategy\"]\ntools: [\"monograph_query\",\"monograph_context\",\"monograph_impact\"]\nlicense: Apache-2.0\nsource: https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\n---\n# Trend Researcher — Best Practices\n\n## Focus\nScans for early signals of emerging shifts (market, technology, or product) and separates genuine trends from short-lived noise, using structured methodology rather than gut feel.\n\n## Best practices\n- Scan multiple independent zones (regulatory filings, academic research, startup funding, niche\u002Ffringe communities, adjacent tech stacks) — a signal appearing in only one source is weaker than one corroborated across several.\n- Collect time-series data at consistent intervals rather than one-off snapshots — trend detection requires a trajectory, not a point-in-time observation.\n- Apply smoothing (moving averages, exponential smoothing) before concluding a pattern exists — raw volatile data looks trendy even when it's just noise.\n- Distinguish lead time by source type — regulatory signals tend to precede market shifts by 18-24 months, academic research by 3-5 years; weight recency and horizon accordingly.\n- Validate any detected pattern against real business\u002Fproduct context before acting on it — a statistically significant blip isn't automatically a strategically relevant trend.\n- Track weak signals over time rather than dismissing them after one scan — early-stage trends are, by definition, faint.\n- Distinguish popularity spikes (temporary attention) from structural shifts (sustained changes in underlying behavior or capability).\n- Present findings with explicit confidence levels and time horizons, not as flat assertions — trend research is inherently probabilistic.\n\n## Common pitfalls\n- Treating a single viral moment or news cycle as a durable trend without checking for sustained signal afterward.\n- Relying only on mainstream\u002Fhigh-visibility sources, missing early signals that show up first in niche communities or adjacent domains.\n- Reporting raw, unsmoothed data as evidence of a trend when the apparent pattern is within normal noise bounds.\n- No corroboration across independent sources — one anecdote or one dataset is treated as proof.\n- Skipping the \"so what\" step — cataloging signals without connecting them to a concrete, actionable implication.\n\n## Tools & techniques\n- Statistical smoothing (moving averages, exponential smoothing, Hodrick-Prescott filter) to separate structural trend from short-term volatility.\n- Multi-zone weak-signal scanning (regulatory, academic, funding, fringe community, adjacent-stack) with source-specific lead-time assumptions.\n- Topic modeling \u002F clustering over time-series text data to detect emerging themes before they're mainstream-labeled.\n- Cross-source corroboration checklists — require signal presence in 2+ independent zones before elevating a \"trend\" flag.\n- Confidence-and-horizon labeling on every reported trend (e.g. \"high confidence, 6-12 month horizon\" vs. \"weak signal, monitor only\").\n","# Trend Researcher — Best Practices\n\n## Focus\nScans for early signals of emerging shifts (market, technology, or product) and separates genuine trends from short-lived noise, using structured methodology rather than gut feel.\n\n## Best practices\n- Scan multiple independent zones (regulatory filings, academic research, startup funding, niche\u002Ffringe communities, adjacent tech stacks) — a signal appearing in only one source is weaker than one corroborated across several.\n- Collect time-series data at consistent intervals rather than one-off snapshots — trend detection requires a trajectory, not a point-in-time observation.\n- Apply smoothing (moving averages, exponential smoothing) before concluding a pattern exists — raw volatile data looks trendy even when it's just noise.\n- Distinguish lead time by source type — regulatory signals tend to precede market shifts by 18-24 months, academic research by 3-5 years; weight recency and horizon accordingly.\n- Validate any detected pattern against real business\u002Fproduct context before acting on it — a statistically significant blip isn't automatically a strategically relevant trend.\n- Track weak signals over time rather than dismissing them after one scan — early-stage trends are, by definition, faint.\n- Distinguish popularity spikes (temporary attention) from structural shifts (sustained changes in underlying behavior or capability).\n- Present findings with explicit confidence levels and time horizons, not as flat assertions — trend research is inherently probabilistic.\n\n## Common pitfalls\n- Treating a single viral moment or news cycle as a durable trend without checking for sustained signal afterward.\n- Relying only on mainstream\u002Fhigh-visibility sources, missing early signals that show up first in niche communities or adjacent domains.\n- Reporting raw, unsmoothed data as evidence of a trend when the apparent pattern is within normal noise bounds.\n- No corroboration across independent sources — one anecdote or one dataset is treated as proof.\n- Skipping the \"so what\" step — cataloging signals without connecting them to a concrete, actionable implication.\n\n## Tools & techniques\n- Statistical smoothing (moving averages, exponential smoothing, Hodrick-Prescott filter) to separate structural trend from short-term volatility.\n- Multi-zone weak-signal scanning (regulatory, academic, funding, fringe community, adjacent-stack) with source-specific lead-time assumptions.\n- Topic modeling \u002F clustering over time-series text data to detect emerging themes before they're mainstream-labeled.\n- Cross-source corroboration checklists — require signal presence in 2+ independent zones before elevating a \"trend\" flag.\n- Confidence-and-horizon labeling on every reported trend (e.g. \"high confidence, 6-12 month horizon\" vs. \"weak signal, monitor only\").\n",{"name":5,"tags":43,"tools":47,"source":51,"license":14,"description":7},[44,45,46,23],"ai-ml","engineering","research",[48,49,50],"monograph_query","monograph_context","monograph_impact","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","main","f7cd4595d0f8959822163e14f9787117765c931a","packages\u002F@monomind\u002Fcli\u002Forg-skills\u002Ftrend-researcher",2,"https:\u002F\u002Fmonoes.github.io\u002Fmonomind\u002F","https:\u002F\u002Fgithub.com\u002Fmonoes","https:\u002F\u002Fmonoes.me","2026-11-10 01:53:48",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add monoes\u002Fmonomind --skill trend-researcher",{"tool":66,"command":67},"bunx","bunx skills add monoes\u002Fmonomind --skill trend-researcher",{"tool":69,"command":70},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill trend-researcher","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Ftrend-researcher\u002FSKILL.md",true,[],[75,98,132,158,183,210],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":38},"01M71PB3SHWKMNJM6MD65VX8D6","zk-steward","Org role guidance for a ZK steward: engineer zero-knowledge proof systems with circuit design, SNARK vs STARK choice and security review. This is zero-knowledge cryptography, not Zettelkasten note-taking.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fzk-steward\u002FSKILL.md",[],[82,83,84,85,86,87,88,89,36,34,90,91,32,92,93,94,95,96,27,97],"zk","zk steward","steward","zero-knowledge","engineer","choice","design","knowledge","snark","review","security review","stark","zettelkasten","cryptography","security","blockchain",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":102,"licenseFileUrl":103,"sourceHtmlUrl":104,"categories":105,"keywords":116,"publishedAt":38},"01M71PB18SDG28WBKPKJNB814K","writing-plans","Use when you have a spec or requirements for a multi-step coding task and must write an implementation plan before touching code. Produces bite-sized TDD tasks with exact file paths, code and commands for an engineer with zero context.","MIT","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwriting-plans\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwriting-plans\u002FSKILL.md",[106,111],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[117,118,119,86,120,121,35,122,34,123,124,125,30,126,127,128,129,130,25,131],"writing","writing plans","plans","superpowers","implementation plan","you","code","tasks","plan","write","commands","paths","suggest","coding","ZeRO",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":136,"sourceHtmlUrl":137,"categories":138,"keywords":142,"publishedAt":38},"01M71PAXFM488X9ARXK0T04TF5","write-spec","Write a feature spec or PRD from a problem statement or feature idea. Use when turning a vague idea or user request into a structured document, scoping a feature with goals and non-goals, defining success metrics and acceptance criteria, or breaking a big ask into a phased spec.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-spec\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-spec\u002FSKILL.md",[139],{"categoryNo":140,"categoryName":141,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000031","产品经理",[126,143,144,145,146,89,147,35,148,149,150,30,151,152,153,154,155,156,21,157],"write spec","spec","user request","problem","acceptance","ask","vague","work","request","scoping","idea","metrics","feature","problem statement","document",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":162,"sourceHtmlUrl":163,"categories":164,"keywords":168,"publishedAt":38},"01M71PASSZY800C0RPEARBS918","write-query","Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-query\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-query\u002FSKILL.md",[165],{"categoryNo":166,"categoryName":167,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000033","数据分析师",[34,126,169,170,171,172,89,21,35,173,174,175,176,150,177,178,179,180,181,182],"write query","postgres","for","snowflake","best practices","natural-language","with","multi","github","sql","https","syntax","plugins","data",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":195,"publishedAt":38},"01M71PAPTX0T3PF6ETFVR96SJJ","workflow-automation","Org role guidance for workflow automation: build and maintain CI\u002FCD definitions in GitHub Actions or equivalent: triggers, jobs, matrices, caching. Covers path filters, dependency caching, parallel jobs, timeouts and fail-fast ordering.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-automation\u002FSKILL.md",[189,192],{"categoryNo":190,"categoryName":191,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":193,"categoryName":194,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000023","Skill与智能体开发",[196,197,198,199,171,200,201,202,203,36,204,205,206,34,32,177,207,208,209,179],"Workflow Automation","automation","workflow","parallel","actions","ci","triggers","github actions","maintain","filters","caching","build","dependency","devops",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":214,"categories":215,"keywords":217,"publishedAt":38},"01M71PAM19Z1XTV9NPCFSE2MY8","workflow-architect","Org role guidance for a workflow architect: map full workflow trees for systems, user journeys and agents, covering every branch and failure. Covers discovering workflows from code, handoff contracts, timeouts, recovery paths and observable state.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-architect\u002FSKILL.md",[216],{"categoryNo":193,"categoryName":194,"taxonomyType":109,"taxonomyTypeDesc":110},[198,218,219,220,221,222,223,171,224,225,123,226,227,228,32,177,229,230,179,231],"workflow architect","architect","trees","user journeys","branch","planning","map","workflows","architecture","state","leadership","recovery","operations","systems"]