[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7AP8MBZWH4FDFE7KKEYTG3H":3,"skill-same:01M7AP8MBZWH4FDFE7KKEYTG3H":45},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":14,"repositoryHtmlUrl":27,"repositoryHomepageUrl":6,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":29,"updatedAt":30,"install":31,"resources":44},"01M7AP8MBZWH4FDFE7KKEYTG3H","weekly-ops-report",null,"Turn raw operational data into a weekly management report that answers exactly three questions - what changed, where is it concentrated, what needs a decision. Use when the user mentions weekly report, haftalık rapor, yönetim raporu, ops review, KPI summary, or asks to summarize operational data for managers. Differentiator - contract-first reporting with plain-language findings and driver decomposition, never a chart dump.","GongYuanCaiJi\u002Fdsh-claude-code-templates","GongYuanCaiJi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F129952120?v=4","01M7AK39Q4W3VPF7C1K8M58BFX","劉昱廷 Yu-Ting Liu","01M7AK39QPS2RAJKRQY0EPQJPZ",0,"MIT","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Foperations\u002Fweekly-ops-report\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: weekly-ops-report\ndescription: Turn raw operational data into a weekly management report that answers exactly three questions - what changed, where is it concentrated, what needs a decision. Use when the user mentions weekly report, haftalık rapor, yönetim raporu, ops review, KPI summary, or asks to summarize operational data for managers. Differentiator - contract-first reporting with plain-language findings and driver decomposition, never a chart dump.\n---\n\n# Weekly Ops Report\n\nA weekly report is a contract: *what changed, where is it concentrated, what needs a decision.* Anything that does not serve one of those three questions is decoration and gets cut.\n\n## Workflow\n\n1. **Data-quality gate first.** Before any KPI: duplicated rows, missing calendar days, impossible values (negative quantities, dates out of range). Fix what is safe to fix, and report every finding in a footer - an unattended report must audit its own inputs.\n2. **Compute the KPI set** agreed for the audience (typical core: revenue\u002Fvolume, service level, stock cover; keep it under ~6). For each: this week, vs last week, and vs the trailing 8-week average. The baseline comparison prevents one unusual prior week from faking a trend.\n3. **Apply movement thresholds.** Only movements beyond agreed thresholds (state defaults, e.g. |revenue| >= 5%, |service| >= 1.5 pts) become findings. Reporting every wiggle trains readers to ignore the report.\n4. **Decompose every finding.** A movement without its driver is not a finding. Break the moved metric by its main dimension (region, carrier, category...) and name the concentrated segment with its share of the move.\n5. **Write findings as sentences**, one line each, using: *METRIC moved X (vs baseline) - DRIVER is the main contributor (Y, ~Z% of the move) - SUGGESTED NEXT STEP.* Tag each finding positive \u002F negative \u002F warning. Maximum five findings; if more qualify, keep the five largest by impact.\n6. **Assemble in fixed order:** headline KPI cards, findings list, trend view (13 weeks), attention lists (e.g. low-cover SKUs), data-quality footer. Same structure every week - familiarity is what makes deltas visible.\n7. **Validate before delivering.** Recompute one headline KPI directly from raw rows and match it against the report value. If automation is involved, this check runs inside the pipeline, not in someone's head.\n\n## Pitfalls to check explicitly\n\n- Week-over-week only (no baseline) - manufactures fake trends\n- Unstated metric definitions - pair every service\u002Fon-time figure with its definition footnote\n- Commentary drift - keep generated findings rule-based and auditable; human judgment belongs in a clearly separated commentary block, not mixed into computed statements\n- Silent denominator changes (cancelled orders, excluded lines) between weeks\n\n## Output format\n\nThe report skeleton in order: title + period + generation stamp; 4-6 KPI cards with deltas; \"What changed and where to look\" findings (max 5, tagged); 13-week trends; attention table; data-quality footer listing every issue found.\n\nWorked implementation (scheduled pipeline, rule-based insight engine): https:\u002F\u002Fgithub.com\u002Fgulmezeren2-byte\u002Fauto-report-pipeline\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","# Weekly Ops Report\n\nA weekly report is a contract: *what changed, where is it concentrated, what needs a decision.* Anything that does not serve one of those three questions is decoration and gets cut.\n\n## Workflow\n\n1. **Data-quality gate first.** Before any KPI: duplicated rows, missing calendar days, impossible values (negative quantities, dates out of range). Fix what is safe to fix, and report every finding in a footer - an unattended report must audit its own inputs.\n2. **Compute the KPI set** agreed for the audience (typical core: revenue\u002Fvolume, service level, stock cover; keep it under ~6). For each: this week, vs last week, and vs the trailing 8-week average. The baseline comparison prevents one unusual prior week from faking a trend.\n3. **Apply movement thresholds.** Only movements beyond agreed thresholds (state defaults, e.g. |revenue| >= 5%, |service| >= 1.5 pts) become findings. Reporting every wiggle trains readers to ignore the report.\n4. **Decompose every finding.** A movement without its driver is not a finding. Break the moved metric by its main dimension (region, carrier, category...) and name the concentrated segment with its share of the move.\n5. **Write findings as sentences**, one line each, using: *METRIC moved X (vs baseline) - DRIVER is the main contributor (Y, ~Z% of the move) - SUGGESTED NEXT STEP.* Tag each finding positive \u002F negative \u002F warning. Maximum five findings; if more qualify, keep the five largest by impact.\n6. **Assemble in fixed order:** headline KPI cards, findings list, trend view (13 weeks), attention lists (e.g. low-cover SKUs), data-quality footer. Same structure every week - familiarity is what makes deltas visible.\n7. **Validate before delivering.** Recompute one headline KPI directly from raw rows and match it against the report value. If automation is involved, this check runs inside the pipeline, not in someone's head.\n\n## Pitfalls to check explicitly\n\n- Week-over-week only (no baseline) - manufactures fake trends\n- Unstated metric definitions - pair every service\u002Fon-time figure with its definition footnote\n- Commentary drift - keep generated findings rule-based and auditable; human judgment belongs in a clearly separated commentary block, not mixed into computed statements\n- Silent denominator changes (cancelled orders, excluded lines) between weeks\n\n## Output format\n\nThe report skeleton in order: title + period + generation stamp; 4-6 KPI cards with deltas; \"What changed and where to look\" findings (max 5, tagged); 13-week trends; attention table; data-quality footer listing every issue found.\n\nWorked implementation (scheduled pipeline, rule-based insight engine): https:\u002F\u002Fgithub.com\u002Fgulmezeren2-byte\u002Fauto-report-pipeline\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","51493f354fd03e3f941d4df7fd64c0b8c7a2632d","skills\u002Foperations\u002Fweekly-ops-report","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi","漏洞研究、根因分析、黑箱測試｜做 AI agent 工具｜Vulnerability research, root-cause analysis, AI agent tooling","2026-08-16 02:24:07",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add GongYuanCaiJi\u002Fdsh-claude-code-templates --skill weekly-ops-report",{"tool":37,"command":38},"bunx","bunx skills add GongYuanCaiJi\u002Fdsh-claude-code-templates --skill weekly-ops-report",{"tool":40,"command":41},"pnpm","pnpm dlx skills add GongYuanCaiJi\u002Fdsh-claude-code-templates --skill weekly-ops-report","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Foperations\u002Fweekly-ops-report\u002FSKILL.md",true,[],[46,53,62,69,76,83],{"skillNo":47,"name":48,"nameZh":6,"description":49,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":50,"categories":51,"keywords":52,"publishedAt":19},"01M7ARMYTZ5D4B7N5SKFXV345V","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[],[],{"skillNo":54,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":57,"licenseFileUrl":58,"sourceHtmlUrl":59,"categories":60,"keywords":61,"publishedAt":19},"01M7ARMX1BVNEAR98SBANMDF2V","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","Apache-2.0","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":68,"publishedAt":19},"01M7ARMPR4RQ417MG1HMPGNQHH","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[],[],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":19},"01M7ARMHBERDMKGDMV7K6MQ6YP","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M7ARME0A7KFT330Q2H25HE2B","slack-automation","Automate Slack workspace operations including messaging, search, channel management, and reaction workflows through Composio's Slack toolkit.","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fslack-automation\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":19},"01M7ARM8D87V18WDG0GEZS11ZT","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[],[]]