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    "sections": [
      {
        "title": "Technical Debt Audit",
        "body": "Systematic technical debt assessment for engineering teams. Identifies, scores, and prioritizes debt across your codebase with business impact analysis and remediation roadmaps."
      },
      {
        "title": "What It Does",
        "body": "Debt Discovery — Categorizes debt: architecture, code quality, dependency, testing, infrastructure, documentation\nImpact Scoring — Rates each item on effort (1-5), risk (1-5), and business impact (1-5) using a weighted formula\nCost Modeling — Estimates carrying cost per sprint in developer-hours and dollars\nRemediation Roadmap — Generates a prioritized paydown plan with quick wins, scheduled work, and strategic rewrites\nExecutive Summary — One-page board-ready report showing debt-to-velocity ratio and projected savings"
      },
      {
        "title": "Usage",
        "body": "Describe your system, stack, and known pain points. The agent audits systematically:\n\n\"Audit our technical debt. We're a Node.js/React SaaS with 180K LOC, \n12 engineers. Known issues: monolithic API, no integration tests, \n3 deprecated dependencies, manual deployments.\""
      },
      {
        "title": "Scoring Formula",
        "body": "Priority Score = (Risk × 3) + (Business Impact × 2) + (1/Effort × 1)\n\nHigher score = fix first. Quick wins (low effort, high risk) surface to the top."
      },
      {
        "title": "Debt Categories",
        "body": "CategoryExamplesTypical Carrying CostArchitectureMonoliths, tight coupling, wrong patterns15-25% velocity dragCode QualityDuplication, god classes, no standards10-20% velocity dragDependenciesOutdated libs, security vulns, EOL frameworks5-15% + incident riskTestingNo tests, flaky tests, manual QA only20-40% bug-fix overheadInfrastructureManual deploys, no monitoring, snowflake servers10-30% ops overheadDocumentationNo onboarding docs, tribal knowledge2-4 weeks per new hire"
      },
      {
        "title": "Output Format",
        "body": "# Technical Debt Audit Report\n## Executive Summary\n- Total debt items: [N]\n- Estimated carrying cost: $[X]/month\n- Debt-to-velocity ratio: [X]%\n- Quick wins available: [N] items, [X] dev-days\n\n## Critical (Fix This Sprint)\n...\n\n## High Priority (Next 30 Days)  \n...\n\n## Scheduled (Next Quarter)\n...\n\n## Strategic (Plan & Budget)\n...\n\n## Remediation Roadmap\nWeek 1-2: [Quick wins]\nMonth 1: [High priority]\nQuarter: [Scheduled items]"
      },
      {
        "title": "Why This Matters",
        "body": "Engineering teams spend 23-42% of development time on technical debt (Stripe Developer Report). Most don't measure it. What you don't measure, you can't manage.\n\nBuilt by AfrexAI — AI-powered business operations tools.\n\nNeed the full engineering context pack? Browse our AI Context Packs ($47) or try the free AI Revenue Calculator."
      }
    ],
    "body": "Technical Debt Audit\n\nSystematic technical debt assessment for engineering teams. Identifies, scores, and prioritizes debt across your codebase with business impact analysis and remediation roadmaps.\n\nWhat It Does\nDebt Discovery — Categorizes debt: architecture, code quality, dependency, testing, infrastructure, documentation\nImpact Scoring — Rates each item on effort (1-5), risk (1-5), and business impact (1-5) using a weighted formula\nCost Modeling — Estimates carrying cost per sprint in developer-hours and dollars\nRemediation Roadmap — Generates a prioritized paydown plan with quick wins, scheduled work, and strategic rewrites\nExecutive Summary — One-page board-ready report showing debt-to-velocity ratio and projected savings\nUsage\n\nDescribe your system, stack, and known pain points. The agent audits systematically:\n\n\"Audit our technical debt. We're a Node.js/React SaaS with 180K LOC, \n12 engineers. Known issues: monolithic API, no integration tests, \n3 deprecated dependencies, manual deployments.\"\n\nScoring Formula\n\nPriority Score = (Risk × 3) + (Business Impact × 2) + (1/Effort × 1)\n\nHigher score = fix first. Quick wins (low effort, high risk) surface to the top.\n\nDebt Categories\nCategory\tExamples\tTypical Carrying Cost\nArchitecture\tMonoliths, tight coupling, wrong patterns\t15-25% velocity drag\nCode Quality\tDuplication, god classes, no standards\t10-20% velocity drag\nDependencies\tOutdated libs, security vulns, EOL frameworks\t5-15% + incident risk\nTesting\tNo tests, flaky tests, manual QA only\t20-40% bug-fix overhead\nInfrastructure\tManual deploys, no monitoring, snowflake servers\t10-30% ops overhead\nDocumentation\tNo onboarding docs, tribal knowledge\t2-4 weeks per new hire\nOutput Format\n# Technical Debt Audit Report\n## Executive Summary\n- Total debt items: [N]\n- Estimated carrying cost: $[X]/month\n- Debt-to-velocity ratio: [X]%\n- Quick wins available: [N] items, [X] dev-days\n\n## Critical (Fix This Sprint)\n...\n\n## High Priority (Next 30 Days)  \n...\n\n## Scheduled (Next Quarter)\n...\n\n## Strategic (Plan & Budget)\n...\n\n## Remediation Roadmap\nWeek 1-2: [Quick wins]\nMonth 1: [High priority]\nQuarter: [Scheduled items]\n\nWhy This Matters\n\nEngineering teams spend 23-42% of development time on technical debt (Stripe Developer Report). Most don't measure it. What you don't measure, you can't manage.\n\nBuilt by AfrexAI — AI-powered business operations tools.\n\nNeed the full engineering context pack? Browse our AI Context Packs ($47) or try the free AI Revenue Calculator."
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