hack25-cutvac-risk-cleaner/repo-manifest.json at main · Projecting-Success-Solutions-Portal/hack25-cutvac-risk-cleaner · GitHub

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{
  "event": {
    "name": "hack25",
    "slug": "hack25",
    "repo": "hack25",
    "topics": [
      "solution-centre",
      "hack25",
      "github",
      "microsoft-azure",
      "power-platform",
      "python",
      "hackathon",
      "innovation",
      "data",
      "artificial-intelligence",
      "collaboration",
      "prototyping",
      "challenge1",
      "large-language-models",
      "natural-language-processing",
      "project-assurance",
      "automation",
      "evidence-management",
      "assurance",
      "llm"
    ],
    "technologies": [
      "GitHub",
      "Microsoft Azure",
      "Power Platform",
      "Python",
      "Large Language Models",
      "Natural Language Processing",
      "HTML",
      "JavaScript",
      "D3.js",
      "Microsoft Copilot",
      "Microsoft Excel",
      "Power Query",
      "CSV",
      "Data Analysis",
      "Prompt Engineering",
      "JSON",
      "Power BI",
      "ChatGPT",
      "Word Documents",
      "Ollama",
      "DeepSeek-R1",
      "LangChain",
      "Streamlit",
      "Tesseract OCR",
      "Agentic AI",
      "Email Automation",
      "Data Visualisation",
      "Data Analytics",
      "Presentation",
      "Video"
    ]
  },
  "visibility": {
    "event_repo": "public",
    "team_repos": "public"
  },
  "repos": [
    {
      "type": "team",
      "challenge_name": "Risk Register Regulator: Agentic Review",
      "challenge_description": "This challenge focuses on improving the effectiveness of project risk management by using agentic AI to automatically review risk registers, identify quality issues and opportunities, and prompt action. Teams are invited to design a prototype that applies AI personas to assess risk data, generate tailored and actionable feedback, and issue automated email nudges to relevant stakeholders. The approach aims to create a closed\u2011loop system that improves risk data quality, drives timely mitigation activity, and visualises change over time, enabling organisations to demonstrate tangible return on investment from automated risk assurance.",
      "challenge_slug": "challenge3",
      "team_name": "CutVac Risk Cleaner",
      "team_slug": "d",
      "name": "hack25-cutvac-risk-cleaner",
      "child_repo_path": "submissions/hack25-challenge3-d",
      "url": "https://github.com/Projecting-Success-Solutions-Portal/hack25-cutvac-risk-cleaner.git",
      "description": "CutVac Risk Cleaner implemented a Python\u2011based risk\u2011register processing and feedback pipeline that cleans, validates and analyses risk and mitigation data, then generates automated, insight\u2011driven email prompts to drive corrective action.",
      "key_outcomes": "Expected to improve risk\u2011register data quality, reduce inconsistencies and duplicates, and accelerate corrective action by automatically notifying risk and mitigation owners when outcomes worsen or improve.",
      "important_files": [
        {
          "path": "Solution/Python Code/cutvac_cleaner.py",
          "purpose": "Cleans and normalises risk and mitigation data, enforces consistency, and produces a master dataset for analysis."
        },
        {
          "path": "Solution/Python Code/mit_success.py",
          "purpose": "Evaluates mitigation effectiveness and generates insight\u2011driven email prompts highlighting changes in cost, probability and criticality."
        },
        {
          "path": "Solution/Python Code/auto_run_cleaner.py",
          "purpose": "Watches source files and automatically triggers the cleaning and analysis pipeline when data changes."
        }
      ],
      "required_fields": {
        "summary": "An automated risk\u2011register cleaning and insight engine that drives action through targeted email feedback.",
        "problem": "Risk registers often contain inconsistent, incomplete or duplicated data and fail to trigger timely follow\u2011up actions.",
        "approach": "Apply automated data\u2011cleaning, validation and comparative analysis to risk registers, then generate role\u2011relevant email prompts based on changes in risk outcomes.",
        "deliverables": "Python data\u2011cleaning scripts, automated file\u2011watcher, mitigation\u2011effectiveness analysis, and generated email prompt outputs."
      },
      "thumbnail_path": "",
      "topics": [
        "solution-centre",
        "hack25",
        "challenge3",
        "python",
        "pandas",
        "watchdog",
        "excel",
        "email-automation",
        "risk-management",
        "risk-register",
        "automation",
        "data-quality",
        "agentic-ai",
        "email-nudges"
      ],
      "technologies": [
        "Python",
        "Pandas",
        "Watchdog",
        "Excel",
        "Email Automation"
      ],
      "members": []
    }
  ]
}