[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71NHESEMK64X7SY63V0BCFV":3,"skill-same:01M71NHESEMK64X7SY63V0BCFV":75},{"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":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":52,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M71NHESEMK64X7SY63V0BCFV","pipeline-analyst",null,"Use when an org role acts as pipeline analyst and must monitor sales pipeline health and forecast accuracy: conversion, velocity and CRM data hygiene. Covers stage-conversion funnels, sales velocity formula, cohort breakdowns, coverage vs quality, and decision-ready reporting for revenue leadership.","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\u002Fpipeline-analyst\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师","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],"pipeline","pipeline analyst","analyst","velocity","monomind","forecast accuracy","when","org","revenue","as","leadership","analytics","crm","formula","conversion","ready","coverage","decision","data","reporting","2026-11-10 10:56:23",false,"---\nname: pipeline-analyst\ndescription: \"Use when an org role acts as pipeline analyst and must monitor sales pipeline health and forecast accuracy: conversion, velocity and CRM data hygiene. Covers stage-conversion funnels, sales velocity formula, cohort breakdowns, coverage vs quality, and decision-ready reporting for revenue leadership.\"\ntags: [\"sales\",\"analytics\",\"planning\"]\ntools: []\nlicense: Apache-2.0\nsource: https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\n---\n# Pipeline Analyst — Best Practices\n\n## Focus\nMonitors pipeline health and forecast accuracy — tracking conversion, velocity, and data hygiene so leadership can trust the numbers driving revenue decisions.\n\n## Best practices\n- Track the core metric set together, not in isolation: pipeline velocity, stage-to-stage conversion, average deal size, win rate, and sales cycle length.\n- Enforce data hygiene as a discipline: deal stages updated within 24 hours, contact\u002Fcompany fields complete, no stale (7+ day untouched) opportunities — hygiene failures corrupt every downstream analysis.\n- Use standardized pipeline stages with objective entry\u002Fexit criteria; subjective stage movement is the single biggest source of forecast error.\n- Run cohort analysis (by industry, deal size, region, source) to find which opportunity types actually convert fast and close well, rather than treating pipeline as homogeneous.\n- Reconcile and update forecasts on a fixed cadence (weekly is a strong default) — frequent updates measurably improve forecast accuracy over stale quarterly snapshots.\n- Distinguish pipeline coverage (multiple of quota) from pipeline quality — a healthy-looking coverage ratio can hide a rotten mix of low-probability deals.\n- Flag anomalies (deals stuck at a stage far past typical duration, sudden stage jumps) rather than just reporting aggregate numbers.\n- Present findings as decision-ready insight (what to do about it), not just dashboards — a chart without a recommendation gets ignored.\n\n## Common pitfalls\n- Reporting pipeline totals without adjusting for data quality — garbage-in numbers produce confidently wrong forecasts.\n- Treating all pipeline stages as equally predictive when historical conversion rates by stage differ wildly.\n- Doing a single point-in-time snapshot instead of tracking trend and velocity, missing whether pipeline is actually healthy or slowly rotting.\n- Burying the actionable insight in a wall of metrics instead of leading with the 2-3 numbers that matter most this cycle.\n\n## Tools & techniques\n- Sales velocity formula: (# opportunities × avg deal value × win rate) \u002F sales cycle length, tracked over time.\n- Stage-conversion funnel analysis with historical benchmarks per stage to flag deviations early.\n- Cohort\u002Fsegment breakdowns (industry, size, source, rep) to isolate what's actually driving or dragging performance.\n- Data-hygiene scorecards (% complete fields, days-since-update distribution) reviewed alongside pipeline metrics, not separately.\n","# Pipeline Analyst — Best Practices\n\n## Focus\nMonitors pipeline health and forecast accuracy — tracking conversion, velocity, and data hygiene so leadership can trust the numbers driving revenue decisions.\n\n## Best practices\n- Track the core metric set together, not in isolation: pipeline velocity, stage-to-stage conversion, average deal size, win rate, and sales cycle length.\n- Enforce data hygiene as a discipline: deal stages updated within 24 hours, contact\u002Fcompany fields complete, no stale (7+ day untouched) opportunities — hygiene failures corrupt every downstream analysis.\n- Use standardized pipeline stages with objective entry\u002Fexit criteria; subjective stage movement is the single biggest source of forecast error.\n- Run cohort analysis (by industry, deal size, region, source) to find which opportunity types actually convert fast and close well, rather than treating pipeline as homogeneous.\n- Reconcile and update forecasts on a fixed cadence (weekly is a strong default) — frequent updates measurably improve forecast accuracy over stale quarterly snapshots.\n- Distinguish pipeline coverage (multiple of quota) from pipeline quality — a healthy-looking coverage ratio can hide a rotten mix of low-probability deals.\n- Flag anomalies (deals stuck at a stage far past typical duration, sudden stage jumps) rather than just reporting aggregate numbers.\n- Present findings as decision-ready insight (what to do about it), not just dashboards — a chart without a recommendation gets ignored.\n\n## Common pitfalls\n- Reporting pipeline totals without adjusting for data quality — garbage-in numbers produce confidently wrong forecasts.\n- Treating all pipeline stages as equally predictive when historical conversion rates by stage differ wildly.\n- Doing a single point-in-time snapshot instead of tracking trend and velocity, missing whether pipeline is actually healthy or slowly rotting.\n- Burying the actionable insight in a wall of metrics instead of leading with the 2-3 numbers that matter most this cycle.\n\n## Tools & techniques\n- Sales velocity formula: (# opportunities × avg deal value × win rate) \u002F sales cycle length, tracked over time.\n- Stage-conversion funnel analysis with historical benchmarks per stage to flag deviations early.\n- Cohort\u002Fsegment breakdowns (industry, size, source, rep) to isolate what's actually driving or dragging performance.\n- Data-hygiene scorecards (% complete fields, days-since-update distribution) reviewed alongside pipeline metrics, not separately.\n",{"name":5,"tags":48,"tools":51,"source":52,"license":14,"description":7},[49,34,50],"sales","planning",[],"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","main","f7cd4595d0f8959822163e14f9787117765c931a","packages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fpipeline-analyst",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":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add monoes\u002Fmonomind --skill pipeline-analyst",{"tool":67,"command":68},"bunx","bunx skills add monoes\u002Fmonomind --skill pipeline-analyst",{"tool":70,"command":71},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill pipeline-analyst","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fpipeline-analyst\u002FSKILL.md",true,[],[76,102,136,163,185,212],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":101},"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",[],[83,84,85,86,87,88,89,90,91,92,93,94,27,95,96,97,98,99,30,100],"zk","zk steward","steward","zero-knowledge","engineer","choice","design","knowledge","impact","query","snark","review","security review","stark","zettelkasten","cryptography","security","blockchain","2026-11-10 13:02:02",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":106,"licenseFileUrl":107,"sourceHtmlUrl":108,"categories":109,"keywords":118,"publishedAt":101},"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",[110,115],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":116,"categoryName":117,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000036","内容创作者",[119,120,121,87,122,123,29,124,92,125,126,127,128,129,130,131,132,133,134,135],"writing","writing plans","plans","superpowers","implementation plan","you","code","tasks","plan","product","write","commands","paths","suggest","coding","context","ZeRO",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":140,"sourceHtmlUrl":141,"categories":142,"keywords":146,"publishedAt":101},"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",[143],{"categoryNo":144,"categoryName":145,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000031","产品经理",[129,147,148,149,150,90,151,29,152,153,154,128,155,156,157,158,159,160,161,162],"write spec","spec","user request","problem","acceptance","ask","vague","work","request","scoping","idea","metrics","feature","problem statement","use","document",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":171,"publishedAt":101},"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",[170],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[92,129,172,173,174,175,90,161,29,176,177,178,179,154,180,181,182,183,184,41],"write query","postgres","for","snowflake","best practices","natural-language","with","multi","github","sql","https","syntax","plugins",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":197,"publishedAt":101},"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",[191,194],{"categoryNo":192,"categoryName":193,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":195,"categoryName":196,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000023","Skill与智能体开发",[198,199,200,201,174,202,203,204,205,91,206,207,208,92,27,180,209,210,211,182],"Workflow Automation","automation","workflow","parallel","actions","ci","triggers","github actions","maintain","filters","caching","build","dependency","devops",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":219,"publishedAt":101},"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",[218],{"categoryNo":195,"categoryName":196,"taxonomyType":113,"taxonomyTypeDesc":114},[200,220,221,222,223,224,50,174,225,226,125,227,228,33,27,180,229,230,182,231],"workflow architect","architect","trees","user journeys","branch","map","workflows","architecture","state","recovery","operations","systems"]