[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AF4JYEF9CVWZDRKGNKVHSS":3,"skill-same:01M6AF4JYEF9CVWZDRKGNKVHSS":74},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6AF4JYEF9CVWZDRKGNKVHSS","otif-analysis",null,"Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what customers experience. Use when the user mentions OTIF, on-time delivery, delivery performance, late orders, teslimat performansı, zamanında teslimat, or asks why customers complain despite a high on-time score. Differentiator - exposes measurement choices before optimizing operations.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Foperations\u002Fotif-analysis\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"otif analysis","analysis","delivery","when","metric","ladder","high","measurement","gap","orders","time","compute","performance","use","operations","find","why","kpi","data","audit","2026-11-10 10:12:30",false,"---\nname: otif-analysis\ndescription: Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what customers experience. Use when the user mentions OTIF, on-time delivery, delivery performance, late orders, teslimat performansı, zamanında teslimat, or asks why customers complain despite a high on-time score. Differentiator - exposes measurement choices before optimizing operations.\n---\n\n# OTIF Analysis\n\nMost \"delivery problems\" are measurement problems first. Before recommending any operational fix, establish what the honest number is and which definition choices inflate the reported one.\n\n## Required data\n\nOrder-level rows with: `order_id`, `requested_delivery_date` (what the customer asked for), `promised_delivery_date` (what was confirmed), `actual_delivery_date`, completeness info (lines ordered vs delivered, or qty ordered vs delivered), and a status\u002Fcancelled flag. Useful cuts: carrier, region, customer, product family.\n\nIf `requested_delivery_date` is missing, say so explicitly: only the promised-date rungs are computable, and the analysis cannot see sales-padding. Recommend capturing the requested date going forward.\n\n## Workflow\n\n1. **Validate before computing.** Count duplicated order rows; flag impossible dates (actual before order date); count cancelled orders and state how they will be treated. Report these counts in the output - an audit that silently cleans data is not an audit.\n2. **Compute the metric ladder** on the same population, strictest last:\n   - L1: on-time vs promised date, with the tolerance window currently in use (ask what it is; if unknown, show +3 days and label it)\n   - L2: on-time vs promised, zero tolerance\n   - L3: on-time vs *requested*, zero tolerance\n   - L4: **OTIF** = on-time vs requested AND order complete (all lines \u002F full qty). In-full is judged at order level - a 9-of-10-lines delivery is not 90% on-time, it is one incomplete order\n   - L5: OTIF with cancelled orders kept in the denominator\n   Present the ladder as a table with the delta and the cause of each drop (tolerance, padding, partials, cancellations).\n3. **Decompose the gap.** For the dimension with the largest spread (try carrier, region, month, customer, product family), show OTIF per segment. Name the concentrated driver, not just the average.\n4. **Analyze the tail, not the mean.** Average lateness hides the distribution. Report the share of orders 4+ days late and the worst decile - those are the orders customers remember.\n5. **Reconcile before reporting.** Recompute the headline OTIF once more directly from raw rows (single pass, no intermediate tables) and confirm it matches. If it does not, stop and find out why.\n\n## Pitfalls to check explicitly\n\n- **Anchor choice**: promised-date metrics hide sales padding. Compute average (promised - requested) days; if > 0.5, quantify its KPI effect.\n- **Tolerance windows** are policy, not truth. Show the tolerance-sensitivity curve if the tolerance is contested.\n- **Cancelled orders** quietly leaving the denominator flatter the metric.\n- **Line-level averaging** overstates performance versus order-level in-full.\n\n## Output format\n\n1. The ladder table (definition, result %, delta, cause)\n2. Three finding sentences, each: what moved \u002F where it concentrates \u002F what decision it needs\n3. A definitions footnote stating anchor date, tolerance, in-full rule and cancellation treatment - so the number cannot be misread\n\nWorked example with charts and synthetic data: https:\u002F\u002Fgithub.com\u002Fgulmezeren2-byte\u002Fotif-analytics\n\n---\n\nSource: [industrial-engineering-ai-skills](https:\u002F\u002Fgithub.com\u002Fgulmezeren2-byte\u002Findustrial-engineering-ai-skills) by Eren Gulmez (MIT). The full method pack - entry skill, role agents, data-hygiene rules and artifact templates - lives there.\n","# OTIF Analysis\n\nMost \"delivery problems\" are measurement problems first. Before recommending any operational fix, establish what the honest number is and which definition choices inflate the reported one.\n\n## Required data\n\nOrder-level rows with: `order_id`, `requested_delivery_date` (what the customer asked for), `promised_delivery_date` (what was confirmed), `actual_delivery_date`, completeness info (lines ordered vs delivered, or qty ordered vs delivered), and a status\u002Fcancelled flag. Useful cuts: carrier, region, customer, product family.\n\nIf `requested_delivery_date` is missing, say so explicitly: only the promised-date rungs are computable, and the analysis cannot see sales-padding. Recommend capturing the requested date going forward.\n\n## Workflow\n\n1. **Validate before computing.** Count duplicated order rows; flag impossible dates (actual before order date); count cancelled orders and state how they will be treated. Report these counts in the output - an audit that silently cleans data is not an audit.\n2. **Compute the metric ladder** on the same population, strictest last:\n   - L1: on-time vs promised date, with the tolerance window currently in use (ask what it is; if unknown, show +3 days and label it)\n   - L2: on-time vs promised, zero tolerance\n   - L3: on-time vs *requested*, zero tolerance\n   - L4: **OTIF** = on-time vs requested AND order complete (all lines \u002F full qty). In-full is judged at order level - a 9-of-10-lines delivery is not 90% on-time, it is one incomplete order\n   - L5: OTIF with cancelled orders kept in the denominator\n   Present the ladder as a table with the delta and the cause of each drop (tolerance, padding, partials, cancellations).\n3. **Decompose the gap.** For the dimension with the largest spread (try carrier, region, month, customer, product family), show OTIF per segment. Name the concentrated driver, not just the average.\n4. **Analyze the tail, not the mean.** Average lateness hides the distribution. Report the share of orders 4+ days late and the worst decile - those are the orders customers remember.\n5. **Reconcile before reporting.** Recompute the headline OTIF once more directly from raw rows (single pass, no intermediate tables) and confirm it matches. If it does not, stop and find out why.\n\n## Pitfalls to check explicitly\n\n- **Anchor choice**: promised-date metrics hide sales padding. Compute average (promised - requested) days; if > 0.5, quantify its KPI effect.\n- **Tolerance windows** are policy, not truth. Show the tolerance-sensitivity curve if the tolerance is contested.\n- **Cancelled orders** quietly leaving the denominator flatter the metric.\n- **Line-level averaging** overstates performance versus order-level in-full.\n\n## Output format\n\n1. The ladder table (definition, result %, delta, cause)\n2. Three finding sentences, each: what moved \u002F where it concentrates \u002F what decision it needs\n3. A definitions footnote stating anchor date, tolerance, in-full rule and cancellation treatment - so the number cannot be misread\n\nWorked example with charts and synthetic data: https:\u002F\u002Fgithub.com\u002Fgulmezeren2-byte\u002Fotif-analytics\n\n---\n\nSource: [industrial-engineering-ai-skills](https:\u002F\u002Fgithub.com\u002Fgulmezeren2-byte\u002Findustrial-engineering-ai-skills) by Eren Gulmez (MIT). The full method pack - entry skill, role agents, data-hygiene rules and artifact templates - lives there.\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Foperations\u002Fotif-analysis",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add davila7\u002Fclaude-code-templates --skill otif-analysis",{"tool":60,"command":61},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill otif-analysis",{"tool":63,"command":64},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill otif-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Foperations\u002Fotif-analysis\u002FSKILL.md",true,[68],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fapi_reference.md","REFERENCE","参考文档",12538,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fzarr-python\u002Freferences\u002Fapi_reference.md",[75,105,131,164,199,223],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":86,"publishedAt":39},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[87,88,89,90,91,92,93,94,32,22,95,96,97,98,99,100,101,102,103,104],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":112,"publishedAt":39},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[111],{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},[113,114,115,116,117,118,119,120,121,122,123,22,124,125,126,127,128,129,38,130],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","dependency",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":146,"publishedAt":39},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[137,140,141],{"categoryNo":138,"categoryName":139,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[147,148,149,150,151,152,153,92,154,155,156,157,158,159,160,32,161,162,22,163],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":168,"licenseFileUrl":169,"sourceHtmlUrl":170,"categories":171,"keywords":175,"publishedAt":39},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[172],{"categoryNo":173,"categoryName":174,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000009","前端开发",[176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,92,198],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":168,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":210,"publishedAt":39},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[206,209],{"categoryNo":207,"categoryName":208,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":173,"categoryName":174,"taxonomyType":84,"taxonomyTypeDesc":85},[211,212,213,214,215,177,216,186,187,217,218,219,220,92,221,96,222],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":168,"licenseFileUrl":227,"sourceHtmlUrl":228,"categories":229,"keywords":237,"publishedAt":39},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[230,233,234],{"categoryNo":231,"categoryName":232,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},{"categoryNo":235,"categoryName":236,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000036","内容创作者",[238,239,240,241,242,243,37,244,179,245,189,124,246,247,248,249,250,159,251,22,252,20,253,254,255,102,256],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]