[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71ND0JXY8TXFPWS69CJTM52":3,"skill-same:01M71ND0JXY8TXFPWS69CJTM52":78},{"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":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":55,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M71ND0JXY8TXFPWS69CJTM52","ml-developer",null,"Use when an org role acts as ML developer and must train and evaluate models rigorously: leakage-safe splits, baselines, right metrics, calibration, fairness. Model development practice; for pipeline infrastructure see ml-pipeline.","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\u002Fml-developer\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"ml","developer","ml developer","https","ai-ml","models","evaluate","github","monomind","splits","query","when","use","impact","python","pipeline","for","model","development","as","2026-11-10 10:56:23",false,"---\nname: ml-developer\ndescription: \"Use when an org role acts as ML developer and must train and evaluate models rigorously: leakage-safe splits, baselines, right metrics, calibration, fairness. Model development practice; for pipeline infrastructure see ml-pipeline.\"\ntags: [\"ai-ml\",\"engineering\",\"python\",\"evaluation\"]\ntools: [\"monograph_query\",\"monograph_context\",\"monograph_impact\"]\nlicense: Apache-2.0\nsource: https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\n---\n# ML Developer — Best Practices\n\n## Focus\nDevelops and trains machine learning models end-to-end — data preparation, feature engineering, training, evaluation — with rigor about what makes a model actually trustworthy, not just accurate on paper.\n\n## Best practices\n- Split data (train\u002Fvalidation\u002Ftest, plus out-of-time where applicable) before any feature engineering to avoid leakage from future or held-out data into training.\n- Establish a baseline model first (simple heuristic or linear model) so later complexity is justified by measured lift, not assumed.\n- Evaluate with metrics matched to the problem (F1\u002FAUC for imbalanced classification, RMSE for regression, calibration for probability outputs) — accuracy alone is often misleading.\n- Check feature distributions and label definitions against the actual business\u002Fproduct definition before trusting the target variable.\n- Test for data leakage explicitly — features that encode the label, or that wouldn't be available at prediction time in production.\n- Version data, code, and model artifacts together so any training run is fully reproducible from a clean environment.\n- Run bias\u002Ffairness checks across relevant subgroups before considering a model release-ready, not as an afterthought.\n- Keep a documented rationale for every included feature — undocumented features become unexplainable liabilities during audits or debugging.\n\n## Common pitfalls\n- Data leakage from improper train\u002Ftest splitting (e.g. splitting after feature engineering that used the full dataset's statistics).\n- Chasing a single aggregate metric while ignoring subgroup or segment-level performance where the model quietly fails.\n- No baseline comparison — a complex model's \"good\" accuracy is meaningless without a simple reference point.\n- Skipping calibration checks — a model can have great discrimination (AUC) but badly miscalibrated probabilities, breaking any downstream decision threshold.\n- Not testing for feature stability over time (distribution drift) before deployment, leading to silent degradation in production.\n\n## Tools & techniques\n- Population Stability Index (PSI) and similar drift metrics to check feature\u002Flabel stability across time windows.\n- Cross-validation with stratification for imbalanced classes, and time-based splits for temporal data.\n- SHAP values and partial dependence plots for interpretability — verify the model learned sensible relationships, not spurious correlations.\n- Calibration diagnostics (Brier score, reliability diagrams, Hosmer-Lemeshow test) whenever predicted probabilities drive decisions.\n- Champion-challenger evaluation against the current production model before promoting a new one.\n","# ML Developer — Best Practices\n\n## Focus\nDevelops and trains machine learning models end-to-end — data preparation, feature engineering, training, evaluation — with rigor about what makes a model actually trustworthy, not just accurate on paper.\n\n## Best practices\n- Split data (train\u002Fvalidation\u002Ftest, plus out-of-time where applicable) before any feature engineering to avoid leakage from future or held-out data into training.\n- Establish a baseline model first (simple heuristic or linear model) so later complexity is justified by measured lift, not assumed.\n- Evaluate with metrics matched to the problem (F1\u002FAUC for imbalanced classification, RMSE for regression, calibration for probability outputs) — accuracy alone is often misleading.\n- Check feature distributions and label definitions against the actual business\u002Fproduct definition before trusting the target variable.\n- Test for data leakage explicitly — features that encode the label, or that wouldn't be available at prediction time in production.\n- Version data, code, and model artifacts together so any training run is fully reproducible from a clean environment.\n- Run bias\u002Ffairness checks across relevant subgroups before considering a model release-ready, not as an afterthought.\n- Keep a documented rationale for every included feature — undocumented features become unexplainable liabilities during audits or debugging.\n\n## Common pitfalls\n- Data leakage from improper train\u002Ftest splitting (e.g. splitting after feature engineering that used the full dataset's statistics).\n- Chasing a single aggregate metric while ignoring subgroup or segment-level performance where the model quietly fails.\n- No baseline comparison — a complex model's \"good\" accuracy is meaningless without a simple reference point.\n- Skipping calibration checks — a model can have great discrimination (AUC) but badly miscalibrated probabilities, breaking any downstream decision threshold.\n- Not testing for feature stability over time (distribution drift) before deployment, leading to silent degradation in production.\n\n## Tools & techniques\n- Population Stability Index (PSI) and similar drift metrics to check feature\u002Flabel stability across time windows.\n- Cross-validation with stratification for imbalanced classes, and time-based splits for temporal data.\n- SHAP values and partial dependence plots for interpretability — verify the model learned sensible relationships, not spurious correlations.\n- Calibration diagnostics (Brier score, reliability diagrams, Hosmer-Lemeshow test) whenever predicted probabilities drive decisions.\n- Champion-challenger evaluation against the current production model before promoting a new one.\n",{"name":5,"tags":48,"tools":51,"source":55,"license":14,"description":7},[27,49,37,50],"engineering","evaluation",[52,53,54],"monograph_query","monograph_context","monograph_impact","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","main","f7cd4595d0f8959822163e14f9787117765c931a","packages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fml-developer",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":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add monoes\u002Fmonomind --skill ml-developer",{"tool":70,"command":71},"bunx","bunx skills add monoes\u002Fmonomind --skill ml-developer",{"tool":73,"command":74},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill ml-developer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fml-developer\u002FSKILL.md",true,[],[79,104,138,164,186,213],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":103},"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",[],[86,87,88,89,90,91,92,93,36,33,94,95,31,96,97,98,99,100,101,102],"zk","zk steward","steward","zero-knowledge","engineer","choice","design","knowledge","snark","review","security review","stark","zettelkasten","cryptography","security","org","blockchain","2026-11-10 13:02:02",{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":108,"licenseFileUrl":109,"sourceHtmlUrl":110,"categories":111,"keywords":120,"publishedAt":103},"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",[112,117],{"categoryNo":113,"categoryName":114,"taxonomyType":115,"taxonomyTypeDesc":116},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":118,"categoryName":119,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000036","内容创作者",[121,122,123,90,124,125,34,126,33,127,128,129,130,131,132,133,134,135,136,137],"writing","writing plans","plans","superpowers","implementation plan","you","code","tasks","plan","product","write","commands","paths","suggest","coding","context","ZeRO",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":142,"sourceHtmlUrl":143,"categories":144,"keywords":148,"publishedAt":103},"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",[145],{"categoryNo":146,"categoryName":147,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000031","产品经理",[131,149,150,151,152,93,153,34,154,155,156,130,157,158,159,160,161,162,35,163],"write spec","spec","user request","problem","acceptance","ask","vague","work","request","scoping","idea","metrics","feature","problem statement","document",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":168,"sourceHtmlUrl":169,"categories":170,"keywords":174,"publishedAt":103},"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",[171],{"categoryNo":172,"categoryName":173,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师",[33,131,175,176,39,177,93,35,34,178,179,180,181,156,30,182,26,183,184,185],"write query","postgres","snowflake","best practices","natural-language","with","multi","sql","syntax","plugins","data",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":198,"publishedAt":103},"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",[192,195],{"categoryNo":193,"categoryName":194,"taxonomyType":115,"taxonomyTypeDesc":116},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":196,"categoryName":197,"taxonomyType":115,"taxonomyTypeDesc":116},"01JBSEEDCAT000000000000023","Skill与智能体开发",[199,200,201,202,39,203,204,205,206,36,207,208,209,33,31,30,210,211,212,26],"Workflow Automation","automation","workflow","parallel","actions","ci","triggers","github actions","maintain","filters","caching","build","dependency","devops",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":220,"publishedAt":103},"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",[219],{"categoryNo":196,"categoryName":197,"taxonomyType":115,"taxonomyTypeDesc":116},[201,221,222,223,224,225,226,39,227,228,127,229,230,231,31,30,232,233,26,234],"workflow architect","architect","trees","user journeys","branch","planning","map","workflows","architecture","state","leadership","recovery","operations","systems"]