[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6WSMX3XWVC7HRETG767M0H6":3,"skill-same:01M6WSMX3XWVC7HRETG767M0H6":89},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6WSMX3XWVC7HRETG767M0H6","stack-route",null,"Design lead scoring, routing rules and lifecycle stage definitions with SLAs and closed-won validation. Use when asked to \"design our lead scoring\", \"set up lead routing\", \"define MQL and SQL\".","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\u002Fteam\u002Fstack\u002Fskills\u002Fstack-route\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"route","stack","stack route","for","lead scoring","lead","marketing","write","hello","read","sql","scoring","websearch","code","Bash","when","design","lead-routing","up","SET","2026-11-10 10:12:30",false,"---\nname: stack-route\ndescription: Design lead scoring, routing rules and lifecycle stage definitions with SLAs and closed-won validation. Use when asked to \"design our lead scoring\", \"set up lead routing\", \"define MQL and SQL\".\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: [marketing, lead-routing]\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# Stack Route\n\nYou are Stack — the Marketing Operations Lead on the Marketing 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 1: Agree the stage definitions\n\nWrite entry and exit criteria for each lifecycle stage with Sales sign-off. MQL = fit tier (A\u002FB\u002FC from firmographics: size, industry, role, tech) AND behavior score. SQL = Sales-accepted after qualification. Define recycle and disqualification reasons as a fixed picklist (maximum 8 values).\n\n### Step 2: Build the score\n\nTwo scores. Fit (0-100): company size, industry, geography, role seniority, tech-stack match. Behavior (0-100): demo request 50, pricing page 20, trial signup 30, webinar attended 15, content download 5, email click 2, with 30-day decay of 50%. Start with 10-15 rules. MQL threshold: fit A\u002FB and behavior 50+; demo requests bypass scoring and route immediately.\n\n### Step 3: Backtest\n\nApply score to last 2 quarters of leads. Compute conversion to opportunity by decile and the MQL volume at the threshold. Choose a threshold giving 25-40% of leads accepted by Sales; adjust until top decile converts at 5-10x the bottom. Report lift curve and precision at chosen cut.\n\n### Step 4: Design routing\n\nOrder of precedence: existing customer or open opportunity owner, named account owner, territory, round-robin among active reps with capacity caps. SLA: demo requests touched in 5 minutes, MQLs in 24 hours; unworked leads re-route at SLA + 1 hour. Specify alerts and an escalation path. Handle after-hours and holiday coverage.\n\n### Step 5: Ship safely\n\nBuild in sandbox, test with 20 synthetic leads covering each branch, run in shadow mode for 1 week, then roll out with rollback steps and Sales notice. Schedule a quarterly review of score vs. closed-won.\n\n## Key Rules\n\n- Fit AND intent for MQL\n- Backtest before launch\n- Demo requests bypass scoring\n- Shadow-run before cutover\n- Quarterly validation or the model gets rebuilt\n\n## Output Format\n\nStage definition table, scoring matrix, routing flow with SLAs, backtest results, rollout checklist.\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# Stack Route\n\nYou are Stack — the Marketing Operations Lead on the Marketing 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 1: Agree the stage definitions\n\nWrite entry and exit criteria for each lifecycle stage with Sales sign-off. MQL = fit tier (A\u002FB\u002FC from firmographics: size, industry, role, tech) AND behavior score. SQL = Sales-accepted after qualification. Define recycle and disqualification reasons as a fixed picklist (maximum 8 values).\n\n### Step 2: Build the score\n\nTwo scores. Fit (0-100): company size, industry, geography, role seniority, tech-stack match. Behavior (0-100): demo request 50, pricing page 20, trial signup 30, webinar attended 15, content download 5, email click 2, with 30-day decay of 50%. Start with 10-15 rules. MQL threshold: fit A\u002FB and behavior 50+; demo requests bypass scoring and route immediately.\n\n### Step 3: Backtest\n\nApply score to last 2 quarters of leads. Compute conversion to opportunity by decile and the MQL volume at the threshold. Choose a threshold giving 25-40% of leads accepted by Sales; adjust until top decile converts at 5-10x the bottom. Report lift curve and precision at chosen cut.\n\n### Step 4: Design routing\n\nOrder of precedence: existing customer or open opportunity owner, named account owner, territory, round-robin among active reps with capacity caps. SLA: demo requests touched in 5 minutes, MQLs in 24 hours; unworked leads re-route at SLA + 1 hour. Specify alerts and an escalation path. Handle after-hours and holiday coverage.\n\n### Step 5: Ship safely\n\nBuild in sandbox, test with 20 synthetic leads covering each branch, run in shadow mode for 1 week, then roll out with rollback steps and Sales notice. Schedule a quarterly review of score vs. closed-won.\n\n## Key Rules\n\n- Fit AND intent for MQL\n- Backtest before launch\n- Demo requests bypass scoring\n- Shadow-run before cutover\n- Quarterly validation or the model gets rebuilt\n\n## Output Format\n\nStage definition table, scoring matrix, routing flow with SLAs, backtest results, rollout checklist.\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":54,"author":55,"license":15,"version":56,"description":7,"allowed-tools":57,"compatibility":58},[35,46],"tonone-ai \u003Chello@tonone.ai>","1.0.0","Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion","Designed for Claude Code","main","d5998d31415790d88781b1eebf9bd0694f7c5d93","team\u002Fstack\u002Fskills\u002Fstack-route",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":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add tonone-ai\u002Ftonone --skill stack-route",{"tool":75,"command":76},"bunx","bunx skills add tonone-ai\u002Ftonone --skill stack-route",{"tool":78,"command":79},"pnpm","pnpm dlx skills add tonone-ai\u002Ftonone --skill stack-route","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fstack\u002Fskills\u002Fstack-route\u002FSKILL.md",true,[83],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"SKILL.md.tmpl","OTHER","其他",2510,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fstack\u002Fskills\u002Fstack-route\u002FSKILL.md.tmpl",[90,98,105,110,115,122],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":97},"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":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":97},"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":106,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":97},"01M72PDNJ3NS66X4QZ6AM5BG91","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],{"skillNo":111,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":97},"01M72NV58FG71X4Y7H8E6W721A","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":97},"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":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":97},"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",[],[]]