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    "name": "Case Study Writing",
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    "steps": [
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      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
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      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Case Study Writing",
        "body": "Create compelling B2B case studies with research and visuals via inference.sh CLI."
      },
      {
        "title": "Quick Start",
        "body": "curl -fsSL https://cli.inference.sh | sh && infsh login\n\n# Research the customer's industry\ninfsh app run tavily/search-assistant --input '{\n  \"query\": \"SaaS customer onboarding challenges 2024 statistics\"\n}'\n\nInstall note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available."
      },
      {
        "title": "The STAR Framework",
        "body": "Every case study follows: Situation -> Task -> Action -> Result\n\nSectionLengthContentPurposeSituation100-150 wordsWho the customer is, their contextSet the sceneTask100-150 wordsThe specific challenge they facedCreate empathyAction200-300 wordsWhat solution was implemented, howShow your productResult100-200 wordsMeasurable outcomes, before/afterProve value\n\nTotal: 800-1200 words. Longer loses readers. Shorter lacks credibility."
      },
      {
        "title": "1. Headline (Lead with the Result)",
        "body": "❌ \"How Company X Uses Our Product\"\n❌ \"Company X Case Study\"\n\n✅ \"How Company X Reduced Onboarding Time by 60% with [Product]\"\n✅ \"Company X Grew Revenue 340% in 6 Months Using [Product]\"\n\nThe headline should be specific, quantified, and state the outcome."
      },
      {
        "title": "2. Snapshot Box",
        "body": "Place at the top for skimmers:\n\n┌─────────────────────────────────────┐\n│ Company: Acme Corp                  │\n│ Industry: E-commerce                │\n│ Size: 200 employees                 │\n│ Challenge: Manual order processing  │\n│ Result: 60% faster fulfillment      │\n│ Product: [Your Product]             │\n└─────────────────────────────────────┘"
      },
      {
        "title": "3. Situation",
        "body": "Who is the customer (industry, size, location)\nWhat relevant context existed before the problem\n1-2 sentences of company background"
      },
      {
        "title": "4. Task / Challenge",
        "body": "Quantify the pain: \"spending 40 hours/week on manual data entry\" not \"had data problems\"\nShow stakes: what would happen if unsolved (lost revenue, churn, missed deadlines)\nInclude a customer quote about the frustration"
      },
      {
        "title": "5. Action / Solution",
        "body": "What was implemented (your product/service)\nTimeline: \"deployed in 2 weeks\" / \"3-month rollout\"\nKey decisions or configurations\nWhy they chose you over alternatives (briefly)\n2-3 specific features that addressed the challenge"
      },
      {
        "title": "6. Results",
        "body": "Before/after metrics — always quantified\nTimeframe — \"within 3 months\" / \"in the first quarter\"\nUnexpected benefits beyond the original goal\nCustomer quote about the outcome"
      },
      {
        "title": "How to Present Numbers",
        "body": "❌ \"Improved efficiency\"\n❌ \"Saved time\"\n❌ \"Better results\"\n\n✅ \"Reduced processing time from 4 hours to 45 minutes (81% decrease)\"\n✅ \"Increased conversion rate from 2.1% to 5.8% (176% improvement)\"\n✅ \"Saved $240,000 annually in operational costs\""
      },
      {
        "title": "Metric Categories",
        "body": "CategoryExamplesTimeHours saved, time-to-completion, deployment speedMoneyRevenue increase, cost reduction, ROIEfficiencyThroughput, error rate, automation rateGrowthUsers gained, market expansion, feature adoptionSatisfactionNPS change, retention rate, support tickets reduced"
      },
      {
        "title": "Data Visualization",
        "body": "# Generate a before/after comparison chart\ninfsh app run infsh/python-executor --input '{\n  \"code\": \"import matplotlib.pyplot as plt\\nimport matplotlib\\nmatplotlib.use(\\\"Agg\\\")\\n\\ncategories = [\\\"Processing Time\\\", \\\"Error Rate\\\", \\\"Cost per Order\\\"]\\nbefore = [4, 12, 8.50]\\nafter = [0.75, 1.5, 2.10]\\n\\nfig, ax = plt.subplots(figsize=(10, 6))\\nx = range(len(categories))\\nwidth = 0.35\\nax.bar([i - width/2 for i in x], before, width, label=\\\"Before\\\", color=\\\"#ef4444\\\")\\nax.bar([i + width/2 for i in x], after, width, label=\\\"After\\\", color=\\\"#22c55e\\\")\\nax.set_ylabel(\\\"Value\\\")\\nax.set_xticks(x)\\nax.set_xticklabels(categories)\\nax.legend()\\nax.set_title(\\\"Impact of Implementation\\\")\\nplt.tight_layout()\\nplt.savefig(\\\"results-chart.png\\\", dpi=150)\\nprint(\\\"Chart saved\\\")\"\n}'"
      },
      {
        "title": "What Makes a Good Quote",
        "body": "❌ \"We love the product.\" (vague, could be about anything)\n❌ \"It's great.\" (meaningless)\n\n✅ \"We went from processing 50 orders a day to 200, without adding a single person to the team.\"\n   — Sarah Chen, VP Operations, Acme Corp\n\n✅ \"Before [Product], our team dreaded Monday mornings because of the report backlog.\n    Now it's automated and they can focus on actual analysis.\"\n   — Marcus Rodriguez, Head of Analytics, DataCo"
      },
      {
        "title": "Quote Placement",
        "body": "1 quote in the Challenge section — about the frustration/pain\n1-2 quotes in the Results section — about the outcome/transformation\nAlways attribute: full name, title, company"
      },
      {
        "title": "Quote Formatting",
        "body": "> \"We went from processing 50 orders a day to 200, without adding anyone to the team.\"\n>\n> — Sarah Chen, VP Operations, Acme Corp"
      },
      {
        "title": "Finding Industry Context",
        "body": "# Industry benchmarks\ninfsh app run tavily/search-assistant --input '{\n  \"query\": \"average e-commerce order processing time industry benchmark 2024\"\n}'\n\n# Competitor landscape\ninfsh app run exa/search --input '{\n  \"query\": \"order management automation solutions market overview\"\n}'\n\n# Supporting statistics\ninfsh app run exa/answer --input '{\n  \"question\": \"What percentage of e-commerce businesses still use manual order processing?\"\n}'"
      },
      {
        "title": "Distribution Formats",
        "body": "FormatWhereNotesWeb page/customers/ or /case-studies/Full version, SEO-optimizedPDFSales team, email attachmentDesigned, downloadable, gated optionalSlide deckSales calls, presentations5-8 slides, visual-heavyOne-pagerTrade shows, quick referenceSnapshot + key metrics + quoteSocial postLinkedIn, TwitterKey stat + quote + link to fullVideoWebsite, YouTubeCustomer interview or animated"
      },
      {
        "title": "Social Media Snippet",
        "body": "Headline stat + brief context + customer quote + CTA\n\nExample:\n\"60% faster order processing.\n\nAcme Corp was drowning in manual fulfillment. 4 hours per batch. 12% error rate.\n\nAfter implementing [Product]: 45 minutes per batch. 1.5% errors.\n\n'We went from 50 orders a day to 200 without adding headcount.' — Sarah Chen, VP Ops\n\nRead the full story → [link]\""
      },
      {
        "title": "Writing Checklist",
        "body": "Headline leads with the quantified result\n Snapshot box with company, industry, challenge, result at top\n Challenge is quantified, not vague\n 2-3 specific customer quotes with attribution\n Before/after metrics with timeframe\n 800-1200 words total\n Skimmable (headers, bold, bullet points)\n Customer approved the final version\n Visual: at least one chart or before/after comparison"
      },
      {
        "title": "Common Mistakes",
        "body": "MistakeProblemFixNo specific numbersReads like marketing fluffQuantify everythingAll about your productReads like a sales pitchStory is about the CUSTOMERGeneric quotesNo credibilityGet specific, attributed quotesMissing the \"before\"No contrast to show impactAlways show the starting pointToo longLoses reader attention800-1200 words maxNo customer approvalLegal/relationship riskAlways get sign-off"
      },
      {
        "title": "Related Skills",
        "body": "npx skills add inference-sh/skills@web-search\nnpx skills add inference-sh/skills@prompt-engineering\n\nBrowse all apps: infsh app list"
      }
    ],
    "body": "Case Study Writing\n\nCreate compelling B2B case studies with research and visuals via inference.sh CLI.\n\nQuick Start\ncurl -fsSL https://cli.inference.sh | sh && infsh login\n\n# Research the customer's industry\ninfsh app run tavily/search-assistant --input '{\n  \"query\": \"SaaS customer onboarding challenges 2024 statistics\"\n}'\n\n\nInstall note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.\n\nThe STAR Framework\n\nEvery case study follows: Situation -> Task -> Action -> Result\n\nSection\tLength\tContent\tPurpose\nSituation\t100-150 words\tWho the customer is, their context\tSet the scene\nTask\t100-150 words\tThe specific challenge they faced\tCreate empathy\nAction\t200-300 words\tWhat solution was implemented, how\tShow your product\nResult\t100-200 words\tMeasurable outcomes, before/after\tProve value\n\nTotal: 800-1200 words. Longer loses readers. Shorter lacks credibility.\n\nStructure Template\n1. Headline (Lead with the Result)\n❌ \"How Company X Uses Our Product\"\n❌ \"Company X Case Study\"\n\n✅ \"How Company X Reduced Onboarding Time by 60% with [Product]\"\n✅ \"Company X Grew Revenue 340% in 6 Months Using [Product]\"\n\n\nThe headline should be specific, quantified, and state the outcome.\n\n2. Snapshot Box\n\nPlace at the top for skimmers:\n\n┌─────────────────────────────────────┐\n│ Company: Acme Corp                  │\n│ Industry: E-commerce                │\n│ Size: 200 employees                 │\n│ Challenge: Manual order processing  │\n│ Result: 60% faster fulfillment      │\n│ Product: [Your Product]             │\n└─────────────────────────────────────┘\n\n3. Situation\nWho is the customer (industry, size, location)\nWhat relevant context existed before the problem\n1-2 sentences of company background\n4. Task / Challenge\nQuantify the pain: \"spending 40 hours/week on manual data entry\" not \"had data problems\"\nShow stakes: what would happen if unsolved (lost revenue, churn, missed deadlines)\nInclude a customer quote about the frustration\n5. Action / Solution\nWhat was implemented (your product/service)\nTimeline: \"deployed in 2 weeks\" / \"3-month rollout\"\nKey decisions or configurations\nWhy they chose you over alternatives (briefly)\n2-3 specific features that addressed the challenge\n6. Results\nBefore/after metrics — always quantified\nTimeframe — \"within 3 months\" / \"in the first quarter\"\nUnexpected benefits beyond the original goal\nCustomer quote about the outcome\nMetrics That Matter\nHow to Present Numbers\n❌ \"Improved efficiency\"\n❌ \"Saved time\"\n❌ \"Better results\"\n\n✅ \"Reduced processing time from 4 hours to 45 minutes (81% decrease)\"\n✅ \"Increased conversion rate from 2.1% to 5.8% (176% improvement)\"\n✅ \"Saved $240,000 annually in operational costs\"\n\nMetric Categories\nCategory\tExamples\nTime\tHours saved, time-to-completion, deployment speed\nMoney\tRevenue increase, cost reduction, ROI\nEfficiency\tThroughput, error rate, automation rate\nGrowth\tUsers gained, market expansion, feature adoption\nSatisfaction\tNPS change, retention rate, support tickets reduced\nData Visualization\n# Generate a before/after comparison chart\ninfsh app run infsh/python-executor --input '{\n  \"code\": \"import matplotlib.pyplot as plt\\nimport matplotlib\\nmatplotlib.use(\\\"Agg\\\")\\n\\ncategories = [\\\"Processing Time\\\", \\\"Error Rate\\\", \\\"Cost per Order\\\"]\\nbefore = [4, 12, 8.50]\\nafter = [0.75, 1.5, 2.10]\\n\\nfig, ax = plt.subplots(figsize=(10, 6))\\nx = range(len(categories))\\nwidth = 0.35\\nax.bar([i - width/2 for i in x], before, width, label=\\\"Before\\\", color=\\\"#ef4444\\\")\\nax.bar([i + width/2 for i in x], after, width, label=\\\"After\\\", color=\\\"#22c55e\\\")\\nax.set_ylabel(\\\"Value\\\")\\nax.set_xticks(x)\\nax.set_xticklabels(categories)\\nax.legend()\\nax.set_title(\\\"Impact of Implementation\\\")\\nplt.tight_layout()\\nplt.savefig(\\\"results-chart.png\\\", dpi=150)\\nprint(\\\"Chart saved\\\")\"\n}'\n\nCustomer Quotes\nWhat Makes a Good Quote\n❌ \"We love the product.\" (vague, could be about anything)\n❌ \"It's great.\" (meaningless)\n\n✅ \"We went from processing 50 orders a day to 200, without adding a single person to the team.\"\n   — Sarah Chen, VP Operations, Acme Corp\n\n✅ \"Before [Product], our team dreaded Monday mornings because of the report backlog.\n    Now it's automated and they can focus on actual analysis.\"\n   — Marcus Rodriguez, Head of Analytics, DataCo\n\nQuote Placement\n1 quote in the Challenge section — about the frustration/pain\n1-2 quotes in the Results section — about the outcome/transformation\nAlways attribute: full name, title, company\nQuote Formatting\n> \"We went from processing 50 orders a day to 200, without adding anyone to the team.\"\n>\n> — Sarah Chen, VP Operations, Acme Corp\n\nResearch Support\nFinding Industry Context\n# Industry benchmarks\ninfsh app run tavily/search-assistant --input '{\n  \"query\": \"average e-commerce order processing time industry benchmark 2024\"\n}'\n\n# Competitor landscape\ninfsh app run exa/search --input '{\n  \"query\": \"order management automation solutions market overview\"\n}'\n\n# Supporting statistics\ninfsh app run exa/answer --input '{\n  \"question\": \"What percentage of e-commerce businesses still use manual order processing?\"\n}'\n\nDistribution Formats\nFormat\tWhere\tNotes\nWeb page\t/customers/ or /case-studies/\tFull version, SEO-optimized\nPDF\tSales team, email attachment\tDesigned, downloadable, gated optional\nSlide deck\tSales calls, presentations\t5-8 slides, visual-heavy\nOne-pager\tTrade shows, quick reference\tSnapshot + key metrics + quote\nSocial post\tLinkedIn, Twitter\tKey stat + quote + link to full\nVideo\tWebsite, YouTube\tCustomer interview or animated\nSocial Media Snippet\nHeadline stat + brief context + customer quote + CTA\n\nExample:\n\"60% faster order processing.\n\nAcme Corp was drowning in manual fulfillment. 4 hours per batch. 12% error rate.\n\nAfter implementing [Product]: 45 minutes per batch. 1.5% errors.\n\n'We went from 50 orders a day to 200 without adding headcount.' — Sarah Chen, VP Ops\n\nRead the full story → [link]\"\n\nWriting Checklist\n Headline leads with the quantified result\n Snapshot box with company, industry, challenge, result at top\n Challenge is quantified, not vague\n 2-3 specific customer quotes with attribution\n Before/after metrics with timeframe\n 800-1200 words total\n Skimmable (headers, bold, bullet points)\n Customer approved the final version\n Visual: at least one chart or before/after comparison\nCommon Mistakes\nMistake\tProblem\tFix\nNo specific numbers\tReads like marketing fluff\tQuantify everything\nAll about your product\tReads like a sales pitch\tStory is about the CUSTOMER\nGeneric quotes\tNo credibility\tGet specific, attributed quotes\nMissing the \"before\"\tNo contrast to show impact\tAlways show the starting point\nToo long\tLoses reader attention\t800-1200 words max\nNo customer approval\tLegal/relationship risk\tAlways get sign-off\nRelated Skills\nnpx skills add inference-sh/skills@web-search\nnpx skills add inference-sh/skills@prompt-engineering\n\n\nBrowse all apps: infsh app list"
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/okaris/case-study-writing",
    "publisherUrl": "https://clawhub.ai/okaris/case-study-writing",
    "owner": "okaris",
    "version": "0.1.5",
    "license": null,
    "verificationStatus": "Indexed source record"
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