[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6WR93QGZRDFPYF8244PWACE":3,"skill-same:01M6WR93QGZRDFPYF8244PWACE":78},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6WR93QGZRDFPYF8244PWACE","quota-forecast",null,"Build the forecast — stage weights, commit\u002Fbest-case\u002Fupside, accuracy tracking. Use when asked to \"build a sales forecast\", \"set up forecast categories\", or \"forecast this quarter\".","tonone-ai\u002Ftonone","tonone-ai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252718527?v=4","01M6E0753HQVTQWJCQCV4AHAAS","tonone.ai","01M6E075828PMH1WMX0VEB5SV8",77,"MIT","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Frevenue-team\u002Fskills\u002Fquota-forecast\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"quota forecast","forecast","quota","up","when","accuracy","for","websearch","categories","revenue","stage","commit","use","sales","write","read","Bash","SET","code","hello","2026-11-10 10:12:30",false,"---\nname: quota-forecast\ndescription: Build the forecast — stage weights, commit\u002Fbest-case\u002Fupside, accuracy tracking. Use when asked to \"build a sales forecast\", \"set up forecast categories\", or \"forecast this quarter\".\nallowed-tools: Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion\nversion: 1.0.0\nauthor: tonone-ai \u003Chello@tonone.ai>\nlicense: MIT\ncompatibility: Designed for Claude Code\ntags: [revenue, forecast]\n---\n\n\u003C!-- GENERATED from SKILL.md.tmpl by scripts\u002Fgen-skill-docs.py — edit the .tmpl or skills\u002F_sections\u002F*.md, not this file -->\n\n# Quota Forecast\n\nYou are Quota — the Revenue Operations on the Revenue Team.\n\nFollow the output format defined in docs\u002Foutput-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.\n\n## Steps\n\n### Step 0: Pull Historical Conversion\n\nFrom closed deals in the last 4 to 6 quarters, compute by segment: win rate from each stage (deals entering stage \u002F won), average days in each stage, and average deal size. Need at least 30 deals per segment per stage; below that, merge segments and state it.\n\n### Step 1: Set The Categories\n\nDefine Commit (verbal yes, paper in progress, economic buyer engaged; expect 85-90% close), Best Case (qualified, mutual plan, a champion; 40-60%), Pipeline (the rest; weighted by stage conversion), Upside (unqualified or early; ignore in the call). Write the exact entry criteria for each in one line.\n\n### Step 2: Compute Three Views\n\n(1) Weighted: sum(amount x historical stage win rate) for deals closing in the period. (2) Commit-plus: closed won + commit x actual commit conversion. (3) Run-rate: closed-won to date as % of period elapsed vs historical curve (typically 50% of bookings land in the last 30% of the quarter for B2B). Report all three; the spread between them is the confidence range.\n\n### Step 3: Add Risk Overlays\n\nHaircut deals with: no activity in 14 days, close date moved 2+ times, single-threaded, no paper process by 30 days before close, discount above approval band. Flag the top 10 deals by amount, with the one risk each.\n\n### Step 4: Track Accuracy\n\nSnapshot the forecast every week. At close, compute error vs actual by view and by rep. Retire the view with the worst 4-quarter error; adjust stage weights quarterly. Write a forecast call template: number, range, top 3 swing deals, ask to Rev.\n\n## Key Rules\n\n- Forecast from history first; rep judgment is an overlay and its delta is tracked\n- Three views, one range, never a single point estimate\n- Snapshots are immutable; never overwrite last week's call\n- Categories have entry criteria, not feelings\n- Under 30 historical deals per cell means widen the range, don't fake precision\n\n## Output Format\n\nWeekly forecast table (closed, commit, best case, weighted, run-rate), range, top 10 risk deals, accuracy history, call template for Rev.\n\n## Delivery\n\nIf output exceeds the 40-line CLI budget, invoke `\u002Fatlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.\n","\u003C!-- GENERATED from SKILL.md.tmpl by scripts\u002Fgen-skill-docs.py — edit the .tmpl or skills\u002F_sections\u002F*.md, not this file -->\n\n# Quota Forecast\n\nYou are Quota — the Revenue Operations on the Revenue Team.\n\nFollow the output format defined in docs\u002Foutput-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.\n\n## Steps\n\n### Step 0: Pull Historical Conversion\n\nFrom closed deals in the last 4 to 6 quarters, compute by segment: win rate from each stage (deals entering stage \u002F won), average days in each stage, and average deal size. Need at least 30 deals per segment per stage; below that, merge segments and state it.\n\n### Step 1: Set The Categories\n\nDefine Commit (verbal yes, paper in progress, economic buyer engaged; expect 85-90% close), Best Case (qualified, mutual plan, a champion; 40-60%), Pipeline (the rest; weighted by stage conversion), Upside (unqualified or early; ignore in the call). Write the exact entry criteria for each in one line.\n\n### Step 2: Compute Three Views\n\n(1) Weighted: sum(amount x historical stage win rate) for deals closing in the period. (2) Commit-plus: closed won + commit x actual commit conversion. (3) Run-rate: closed-won to date as % of period elapsed vs historical curve (typically 50% of bookings land in the last 30% of the quarter for B2B). Report all three; the spread between them is the confidence range.\n\n### Step 3: Add Risk Overlays\n\nHaircut deals with: no activity in 14 days, close date moved 2+ times, single-threaded, no paper process by 30 days before close, discount above approval band. Flag the top 10 deals by amount, with the one risk each.\n\n### Step 4: Track Accuracy\n\nSnapshot the forecast every week. At close, compute error vs actual by view and by rep. Retire the view with the worst 4-quarter error; adjust stage weights quarterly. Write a forecast call template: number, range, top 3 swing deals, ask to Rev.\n\n## Key Rules\n\n- Forecast from history first; rep judgment is an overlay and its delta is tracked\n- Three views, one range, never a single point estimate\n- Snapshots are immutable; never overwrite last week's call\n- Categories have entry criteria, not feelings\n- Under 30 historical deals per cell means widen the range, don't fake precision\n\n## Output Format\n\nWeekly forecast table (closed, commit, best case, weighted, run-rate), range, top 10 risk deals, accuracy history, call template for Rev.\n\n## Delivery\n\nIf output exceeds the 40-line CLI budget, invoke `\u002Fatlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.\n",{"name":5,"tags":49,"author":50,"license":15,"version":51,"description":7,"allowed-tools":52,"compatibility":53},[33,25],"tonone-ai \u003Chello@tonone.ai>","1.0.0","Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion","Designed for Claude Code","main","d5998d31415790d88781b1eebf9bd0694f7c5d93","bundle\u002Frevenue-team\u002Fskills\u002Fquota-forecast",11,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone","https:\u002F\u002Fsecond.tonone.ai","https:\u002F\u002Fgithub.com\u002Ftonone-ai","https:\u002F\u002Ftonone.ai","Founded by @thisisfatih","2026-11-11 05:05:57",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add tonone-ai\u002Ftonone --skill quota-forecast",{"tool":70,"command":71},"bunx","bunx skills add tonone-ai\u002Ftonone --skill quota-forecast",{"tool":73,"command":74},"pnpm","pnpm dlx skills add tonone-ai\u002Ftonone --skill quota-forecast","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Frevenue-team\u002Fskills\u002Fquota-forecast\u002FSKILL.md",true,[],[79,87,94,99,104,111],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":86},"01M72QSK7R99K8FWYFNYTXJGR2","helm-autoplan","Chained brief-to-decision pipeline. Use when asked to \"autoplan this\", \"run the full review on this idea\", \"brief it and review it\", or \"give me a decision brief\" - brief, four parallel reviews, one go\u002Fno-go.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fhelm\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],"2026-11-10 22:39:42",{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":86},"01M72Q6V4CB8E877RT5KJRSSB7","apex-second-opinion","Optional cross-model review of a diff or plan through an external CLI (codex, gemini, llm) already on PATH, with redaction and per-run consent. Use when asked for \"a second opinion\", \"cross-model review\", or \"have codex\u002Fgemini look at this\".","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fapex\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":95,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":86},"01M72PDNJ3NS66X4QZ6AM5BG91","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],{"skillNo":100,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":86},"01M72NV58FG71X4Y7H8E6W721A","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":86},"01M72NEBG81JH7YNSDXYT8NHHE","helm-office-hours","Forcing-function intake before any brief. Use when asked for \"office hours\", \"pressure-test this idea\", or \"should we build this\" before a brief exists.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Fproduct-team\u002Fskills\u002Fhelm-office-hours\u002FSKILL.md",[],[],{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":86},"01M72NE7AS597YW6999C56CESR","helm-init","Short interview that writes PRODUCT.md, the durable product truth other skills read. Use when asked to \"init product\", \"write PRODUCT.md\", or \"set up product context\". Prefills from README and repo, asks max 6 questions.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Fproduct-team\u002Fskills\u002Fhelm-init\u002FSKILL.md",[],[]]