[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EBNG10TPCDN7ZA517H39CJ":3,"skill-same:01M6EBNG10TPCDN7ZA517H39CJ":111},{"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":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":52,"ownerDescription":53,"updatedAt":54,"install":55,"resources":68},"01M6EBNG10TPCDN7ZA517H39CJ","saas-metrics-coach",null,"SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing.","levalencia\u002Fagent-god-mode","levalencia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6962857?v=4","01M6EB0FZZ4BPQPC4HET4QY6NM","Luis Valencia ","01M6EB0G4HWHPWW0H9X7QKSAHP",3,"MIT","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"metrics","saas metrics","coach","saas metrics coach","saas","health","how","user","financial health","business","arr","nrr","use","advisor","finance","revenue","churn","customer","2026-11-10 10:12:30",false,"---\nname: saas-metrics-coach\ndescription: SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Abbas Mir\n  category: finance\n  updated: 2026-03-08\n---\n\n# SaaS Metrics Coach\n\nAct as a senior SaaS CFO advisor. Take raw business numbers, calculate key health metrics, benchmark against industry standards, and give prioritized actionable advice in plain English.\n\n## Step 1 — Collect Inputs\n\nIf not already provided, ask for these in a single grouped request:\n\n- Revenue: current MRR, MRR last month, expansion MRR, churned MRR\n- Customers: total active, new this month, churned this month\n- Costs: sales and marketing spend, gross margin %\n\nWork with partial data. Be explicit about what is missing and what assumptions are being made.\n\n## Step 2 — Calculate Metrics\n\nRun `scripts\u002Fmetrics_calculator.py` with the user's inputs. If the script is unavailable, use the formulas in `references\u002Fformulas.md`.\n\nAlways attempt to compute: ARR, MRR growth %, monthly churn rate, CAC, LTV, LTV:CAC ratio, CAC payback period, NRR.\n\n**Additional Analysis Tools:**\n- Use `scripts\u002Fquick_ratio_calculator.py` when expansion\u002Fchurn MRR data is available\n- Use `scripts\u002Funit_economics_simulator.py` for forward-looking projections\n\n## Step 3 — Benchmark Each Metric\n\nLoad `references\u002Fbenchmarks.md`. For each metric show:\n- The calculated value\n- The relevant benchmark range for the user's segment and stage\n- A plain status label: HEALTHY \u002F WATCH \u002F CRITICAL\n\nMatch the benchmark tier to the user's market segment (Enterprise \u002F Mid-Market \u002F SMB \u002F PLG) and company stage (Early \u002F Growth \u002F Scale). Ask if unclear.\n\n## Step 4 — Prioritize and Recommend\n\nIdentify the top 2-3 metrics at WATCH or CRITICAL status. For each one state:\n- What is happening (one sentence, plain English)\n- Why it matters to the business\n- Two or three specific actions to take this month\n\nOrder by impact — address the most damaging problem first.\n\n## Step 5 — Output Format\n\nAlways use this exact structure:\n\n```\n# SaaS Health Report — [Month Year]\n\n## Metrics at a Glance\n| Metric | Your Value | Benchmark | Status |\n|--------|------------|-----------|--------|\n\n## Overall Picture\n[2-3 sentences, plain English summary]\n\n## Priority Issues\n\n### 1. [Metric Name]\nWhat is happening: ...\nWhy it matters: ...\nFix it this month: ...\n\n### 2. [Metric Name]\n...\n\n## What is Working\n[1-2 genuine strengths, no padding]\n\n## 90-Day Focus\n[Single metric to move + specific numeric target]\n```\n\n## Examples\n\n**Example 1 — Partial data**\n\nInput: \"MRR is $80k, we have 200 customers, about 3 cancel each month.\"\n\nExpected output: Calculates ARPA ($400), monthly churn (1.5%), ARR ($960k), LTV estimate. Flags CAC and growth rate as missing. Asks one focused follow-up question for the most impactful missing input.\n\n**Example 2 — Critical scenario**\n\nInput: \"MRR $22k (was $23.5k), 80 customers, lost 9, gained 6, spent $15k on ads, 65% gross margin.\"\n\nExpected output: Flags negative MoM growth (-6.4%), critical churn (11.25%), and LTV:CAC of 0.64:1 as CRITICAL. Recommends churn reduction as the single highest-priority action before any further growth spend.\n\n## Key Principles\n\n- Be direct. If a metric is bad, say it is bad.\n- Explain every metric in one sentence before showing the number.\n- Cap priority issues at three. More than three paralyzes action.\n- Context changes benchmarks. Five percent churn is catastrophic for Enterprise SaaS but normal for SMB\u002FPLG. Always confirm the user's target market before scoring.\n\n## Reference Files\n\n- `references\u002Fformulas.md` — All metric formulas with worked examples\n- `references\u002Fbenchmarks.md` — Industry benchmark ranges by stage and segment\n- `assets\u002Finput-template.md` — Blank input form to share with users\n- `scripts\u002Fmetrics_calculator.py` — Core metrics calculator (ARR, MRR, churn, CAC, LTV, NRR)\n- `scripts\u002Fquick_ratio_calculator.py` — Growth efficiency metric (Quick Ratio)\n- `scripts\u002Funit_economics_simulator.py` — 12-month forward projection\n\n## Tools\n\n### 1. Metrics Calculator (`scripts\u002Fmetrics_calculator.py`)\nCore SaaS metrics from raw business numbers.\n\n```bash\n# Interactive mode\npython scripts\u002Fmetrics_calculator.py\n\n# CLI mode\npython scripts\u002Fmetrics_calculator.py --mrr 50000 --customers 100 --churned 5 --json\n```\n\n### 2. Quick Ratio Calculator (`scripts\u002Fquick_ratio_calculator.py`)\nGrowth efficiency metric: (New MRR + Expansion) \u002F (Churned + Contraction)\n\n```bash\npython scripts\u002Fquick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500\npython scripts\u002Fquick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --json\n```\n\n**Benchmarks:**\n- \u003C 1.0 = CRITICAL (losing faster than gaining)\n- 1-2 = WATCH (marginal growth)\n- 2-4 = HEALTHY (good efficiency)\n- \\> 4 = EXCELLENT (strong growth)\n\n### 3. Unit Economics Simulator (`scripts\u002Funit_economics_simulator.py`)\nProject metrics forward 12 months based on growth\u002Fchurn assumptions.\n\n```bash\npython scripts\u002Funit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000\npython scripts\u002Funit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000 --json\n```\n\n**Use for:**\n- \"What if we grow at X% per month?\"\n- Runway projections\n- Scenario planning (best\u002Fbase\u002Fworst case)\n\n## Related Skills\n\n- **financial-analyst**: Use for DCF valuation, budget variance analysis, and traditional financial modeling. NOT for SaaS-specific metrics like CAC, LTV, or churn.\n- **business-growth\u002Fcustomer-success**: Use for retention strategies and customer health scoring. Complements this skill when churn is flagged as CRITICAL.\n","# SaaS Metrics Coach\n\nAct as a senior SaaS CFO advisor. Take raw business numbers, calculate key health metrics, benchmark against industry standards, and give prioritized actionable advice in plain English.\n\n## Step 1 — Collect Inputs\n\nIf not already provided, ask for these in a single grouped request:\n\n- Revenue: current MRR, MRR last month, expansion MRR, churned MRR\n- Customers: total active, new this month, churned this month\n- Costs: sales and marketing spend, gross margin %\n\nWork with partial data. Be explicit about what is missing and what assumptions are being made.\n\n## Step 2 — Calculate Metrics\n\nRun `scripts\u002Fmetrics_calculator.py` with the user's inputs. If the script is unavailable, use the formulas in `references\u002Fformulas.md`.\n\nAlways attempt to compute: ARR, MRR growth %, monthly churn rate, CAC, LTV, LTV:CAC ratio, CAC payback period, NRR.\n\n**Additional Analysis Tools:**\n- Use `scripts\u002Fquick_ratio_calculator.py` when expansion\u002Fchurn MRR data is available\n- Use `scripts\u002Funit_economics_simulator.py` for forward-looking projections\n\n## Step 3 — Benchmark Each Metric\n\nLoad `references\u002Fbenchmarks.md`. For each metric show:\n- The calculated value\n- The relevant benchmark range for the user's segment and stage\n- A plain status label: HEALTHY \u002F WATCH \u002F CRITICAL\n\nMatch the benchmark tier to the user's market segment (Enterprise \u002F Mid-Market \u002F SMB \u002F PLG) and company stage (Early \u002F Growth \u002F Scale). Ask if unclear.\n\n## Step 4 — Prioritize and Recommend\n\nIdentify the top 2-3 metrics at WATCH or CRITICAL status. For each one state:\n- What is happening (one sentence, plain English)\n- Why it matters to the business\n- Two or three specific actions to take this month\n\nOrder by impact — address the most damaging problem first.\n\n## Step 5 — Output Format\n\nAlways use this exact structure:\n\n```\n# SaaS Health Report — [Month Year]\n\n## Metrics at a Glance\n| Metric | Your Value | Benchmark | Status |\n|--------|------------|-----------|--------|\n\n## Overall Picture\n[2-3 sentences, plain English summary]\n\n## Priority Issues\n\n### 1. [Metric Name]\nWhat is happening: ...\nWhy it matters: ...\nFix it this month: ...\n\n### 2. [Metric Name]\n...\n\n## What is Working\n[1-2 genuine strengths, no padding]\n\n## 90-Day Focus\n[Single metric to move + specific numeric target]\n```\n\n## Examples\n\n**Example 1 — Partial data**\n\nInput: \"MRR is $80k, we have 200 customers, about 3 cancel each month.\"\n\nExpected output: Calculates ARPA ($400), monthly churn (1.5%), ARR ($960k), LTV estimate. Flags CAC and growth rate as missing. Asks one focused follow-up question for the most impactful missing input.\n\n**Example 2 — Critical scenario**\n\nInput: \"MRR $22k (was $23.5k), 80 customers, lost 9, gained 6, spent $15k on ads, 65% gross margin.\"\n\nExpected output: Flags negative MoM growth (-6.4%), critical churn (11.25%), and LTV:CAC of 0.64:1 as CRITICAL. Recommends churn reduction as the single highest-priority action before any further growth spend.\n\n## Key Principles\n\n- Be direct. If a metric is bad, say it is bad.\n- Explain every metric in one sentence before showing the number.\n- Cap priority issues at three. More than three paralyzes action.\n- Context changes benchmarks. Five percent churn is catastrophic for Enterprise SaaS but normal for SMB\u002FPLG. Always confirm the user's target market before scoring.\n\n## Reference Files\n\n- `references\u002Fformulas.md` — All metric formulas with worked examples\n- `references\u002Fbenchmarks.md` — Industry benchmark ranges by stage and segment\n- `assets\u002Finput-template.md` — Blank input form to share with users\n- `scripts\u002Fmetrics_calculator.py` — Core metrics calculator (ARR, MRR, churn, CAC, LTV, NRR)\n- `scripts\u002Fquick_ratio_calculator.py` — Growth efficiency metric (Quick Ratio)\n- `scripts\u002Funit_economics_simulator.py` — 12-month forward projection\n\n## Tools\n\n### 1. Metrics Calculator (`scripts\u002Fmetrics_calculator.py`)\nCore SaaS metrics from raw business numbers.\n\n```bash\n# Interactive mode\npython scripts\u002Fmetrics_calculator.py\n\n# CLI mode\npython scripts\u002Fmetrics_calculator.py --mrr 50000 --customers 100 --churned 5 --json\n```\n\n### 2. Quick Ratio Calculator (`scripts\u002Fquick_ratio_calculator.py`)\nGrowth efficiency metric: (New MRR + Expansion) \u002F (Churned + Contraction)\n\n```bash\npython scripts\u002Fquick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500\npython scripts\u002Fquick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --json\n```\n\n**Benchmarks:**\n- \u003C 1.0 = CRITICAL (losing faster than gaining)\n- 1-2 = WATCH (marginal growth)\n- 2-4 = HEALTHY (good efficiency)\n- \\> 4 = EXCELLENT (strong growth)\n\n### 3. Unit Economics Simulator (`scripts\u002Funit_economics_simulator.py`)\nProject metrics forward 12 months based on growth\u002Fchurn assumptions.\n\n```bash\npython scripts\u002Funit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000\npython scripts\u002Funit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000 --json\n```\n\n**Use for:**\n- \"What if we grow at X% per month?\"\n- Runway projections\n- Scenario planning (best\u002Fbase\u002Fworst case)\n\n## Related Skills\n\n- **financial-analyst**: Use for DCF valuation, budget variance analysis, and traditional financial modeling. NOT for SaaS-specific metrics like CAC, LTV, or churn.\n- **business-growth\u002Fcustomer-success**: Use for retention strategies and customer health scoring. Complements this skill when churn is flagged as CRITICAL.\n",{"name":5,"license":15,"metadata":42,"description":7},{"author":43,"updated":44,"version":45,"category":33},"Abbas Mir",1772928000000,"1.0.0","main","fa638b97a18ff6d9f26cf0eaf3dfa123531bcc87","organized-skills\u002F.gemini-saas-metrics-coach",1,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode","https:\u002F\u002Fgithub.com\u002Flevalencia","https:\u002F\u002Fmedium.com\u002F@luisevalencia","Microsoft MVP - Artificial Intelligence\r\nMachine Learning Enthusiast\r\n","2026-05-02 06:55:36",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add levalencia\u002Fagent-god-mode --skill saas-metrics-coach",{"tool":61,"command":62},"bunx","bunx skills add levalencia\u002Fagent-god-mode --skill saas-metrics-coach",{"tool":64,"command":65},"pnpm","pnpm dlx skills add levalencia\u002Fagent-god-mode --skill saas-metrics-coach","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002FSKILL.md",true,[69,75,81,85,89,93,97,103,107],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"assets\u002Finput-template.md","ASSET","资产文件",718,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002Fassets\u002Finput-template.md",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fbenchmarks.md","REFERENCE","参考文档",2530,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002Freferences\u002Fbenchmarks.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fexamples.md",18983,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fresearch\u002Fdspy\u002Freferences\u002Fexamples.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fformulas.md",2493,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002Freferences\u002Fformulas.md",{"path":90,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fmodules.md",12363,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fresearch\u002Fdspy\u002Freferences\u002Fmodules.md",{"path":94,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Foptimizers.md",14933,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fmlops\u002Fresearch\u002Fdspy\u002Freferences\u002Foptimizers.md",{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"scripts\u002Fmetrics_calculator.py","SCRIPT","脚本",9733,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002Fscripts\u002Fmetrics_calculator.py",{"path":104,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":105,"sourceHtmlUrl":106},"scripts\u002Fquick_ratio_calculator.py",6142,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002Fscripts\u002Fquick_ratio_calculator.py",{"path":108,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":109,"sourceHtmlUrl":110},"scripts\u002Funit_economics_simulator.py",7105,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-saas-metrics-coach\u002Fscripts\u002Funit_economics_simulator.py",[112,134,171,198,224,252],{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":37},"01M6EM8ES5M9J4S8N78NJST0YC","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzarr-python\u002FSKILL.md",[],[119,120,121,122,123,124,125,126,127,128,129,130,131,132,133],"python","zarr python","storage","integration","s3","gcs","cloud","dask","parallel","pipelines","arrays","numpy","scientific computing","scale","scientific",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":153,"publishedAt":37},"01M6EM8AR6ZFKZ9YVW8TTG850Z","zabbix","Zabbix monitoring system automation via API and Python. Use when: (1) Managing hosts, templates, items, triggers, or host groups, (2) Automating monitoring configuration, (3) Sending data via Zabbix trapper\u002Fsender, (4) Querying historical data or events, (5) Bulk operations on Zabbix objects, (6) Maintenance window management, (7) User\u002Fpermission management","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzabbix-api-skill\u002FSKILL.md",[140,145,148],{"categoryNo":141,"categoryName":142,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":146,"categoryName":147,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[136,119,154,155,156,157,158,159,160,161,31,162,163,164,165,166,167,168,169,170],"automation","window management","monitoring","data","templates","api","system","operations","maintenance","objects","permission","bulk operations","triggers","host","events","via","bulk",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":37},"01M6EM6Q9JG846ECQY9A210A26","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fx-twitter-growth\u002FSKILL.md",[],[178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197],"twitter","x twitter growth","growth","competitive intelligence","search","analyzing","plan","marketing","web search","competitors","analyze","write","optimize","social","intelligence","viral","growth engine","mechanics","grow","competitive",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":37},"01M6EM3FQNPYNKPC4CV9TCVNGD","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[205,206,207,208,209,210,211,212,213,214,215,216,31,217,218,219,220,221,222,223],"oracle","what if oracle","test","planning","ux","futures","risk","analysis","explore","decision","branch","question","exploration","options","stress-test","strategies","trigger","case","multi",{"skillNo":225,"name":226,"nameZh":6,"description":227,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":233,"publishedAt":37},"01M6EM08C06RGDYCJ86HAPK0FF","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvercel-react-best-practices\u002FSKILL.md",[230],{"categoryNo":231,"categoryName":232,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000009","前端开发",[234,235,236,237,238,239,240,241,242,243,244,245,246,157,247,248,249,250,251,166],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","tasks","code","Optimization","Next.js","refactoring","performance optimization","next","pages",{"skillNo":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":256,"categories":257,"keywords":268,"publishedAt":37},"01M6EM00S13326QM08DTM94J6Z","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvenue-templates\u002FSKILL.md",[258,261,264,265],{"categoryNo":259,"categoryName":260,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":262,"categoryName":263,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":146,"categoryName":147,"taxonomyType":143,"taxonomyTypeDesc":144},{"categoryNo":266,"categoryName":267,"taxonomyType":151,"taxonomyTypeDesc":152},"01JBSEEDCAT000000000000036","内容创作者",[158,269,270,243,271,272,273,274,275,276,277,189,278,279,280,281,282,283,133,284],"venue templates","research","NeurIPS","ICML","LaTeX","science","Bash","access","read","edit","proposals","Academic","requirements","formatting","journal","chi"]