1
    software-risk-analysis

    software-risk-analysis

    by Matthew King

    Deep repository inspection to generate a pragmatic quality risk strategy and interactive HTML dashboard.

    Updated Jun 2026
    Security scanned

    $5

    · or 25 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Identify high-risk technical debt in a legacy repository
    • Audit a new codebase to establish a "Minimum Viable Quality" baseline
    • file_read, file_write, browser automation included
    • Includes example output and usage patterns
    • Instant install

    See it in action

    A real example of what this skill takes in and produces.

    Sample output

    Risk Score: Critical (Auth Logic) Recommendation: Implement Playwright integration tests. Files created:

    • quality/1-quality-risk-strategy.md
    • web-app-dashboard.html (Visualizing 4 key risk tiers and 12-month quality roadmap).

    About This Skill

    Automated Quality Risk Strategy & Dashboarding

    Modern repositories often lack a cohesive quality strategy, leading to excessive tech debt and inconsistent testing. This skill functions as an embedded Senior QA Engineer that performs a deep inspection of your codebase to generate a pragmatic, risk-based Quality Risk Strategy (QRS) and a visual HTML dashboard.

    What it does

    • Codebase Inspection: Analyzes your repository's structure, tools, and maturity level.
    • Risk Assessment: Identifies high-risk areas based on actual file composition and existing test coverage.
    • Actionable Strategy: Generates a repo-specific 1-quality-risk-strategy.md detailing exactly what to test and why.
    • Visual Dashboard: Produces a self-contained HTML dashboard for stakeholders to visualize the quality posture.

    Why use this skill?

    Generic AI prompts often produce high-level, "fluffy" advice. This skill is governed by strict workflows and checklists (Repo Inspection, Maturity Assessment, Archetype Guidance) to ensure the output is grounded in your specific technical stack. It saves hours of manual audit time by automatically identifying gaps in your CI/CD, unit testing, and integration patterns, providing a "Minimum Viable Quality" roadmap that is immediately implementable.

    Output

    The skill delivers two main assets: a detailed Markdown strategy document and a portable HTML dashboard that renders the risk profiles and maturity scores found during the audit.

    Use Cases

    • Identify high-risk technical debt in a legacy repository
    • Audit a new codebase to establish a "Minimum Viable Quality" baseline
    • Generate a stakeholder-ready quality dashboard for executive review
    • Determine the most effective testing patterns for a specific tech stack

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    Security Scanned

    Passed automated security review

    Permissions

    Read Files
    Write Files
    Browser

    Allowed Hosts

    github.com
    cdn.jsdelivr.net

    File Scopes

    software-risk-analysis/**

    Tags

    qa
    software-architecture
    testing-strategy
    risk-management
    documentation

    Creator

    Matthew King

    I'm a Quality Engineering and Automation specialist with over 10 years of experience designing reliable systems, scalable automation solutions, and quality-focused development practices. My background spans software testing, framework architecture, CI/CD, and process automation, with a strong focus on building systems that are both effective and trustworthy. Throughout my career, I've worked at the intersection of technology, process, and user experience—breaking down complex workflows, identifying failure points, and designing automated solutions that improve efficiency while maintaining quality. This systems-thinking approach naturally extends to AI agents and intelligent automation, where reliability, observability, and clear decision-making processes are critical. I have extensive experience translating business requirements into structured workflows, designing reusable automation frameworks, defining validation strategies, and creating safeguards that ensure predictable outcomes. Whether building test automation, deployment pipelines, or agent-driven processes, my focus is always on creating solutions that are maintainable, measurable, and aligned with user needs. My expertise includes automation architecture, API integration, workflow orchestration, quality engineering, performance testing, and continuous delivery. I enjoy working with emerging technologies and exploring how AI-powered agents can augment teams, automate repetitive tasks, and improve decision-making while maintaining appropriate controls and quality standards.

    Frequently Asked Questions

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