ai-readiness-assessment
Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with Marq AI's audit methodology.
适合你，如果你需要系统评估公司采用AI的能力差距。
npx oh-my-skill add pmkshar/marqaiskills/ai-readiness-assessmentcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- pmkshar/marqaiskills/ai-readiness-assessmentnpx oh-my-skill verify pmkshar/marqaiskills/ai-readiness-assessment怎么用
商店整理自技能原文 · 版本 d5999f8 · 表述以原文为准安装后，Claude 会通过对话、文档和代码分析，从数据成熟度、技术栈、团队技能、流程文档、预算和文化六个维度评估企业AI就绪度，并生成一份详细的 ai-readiness-report.md 报告，包含评分、差距分析和优先行动建议。
当用户要求评估企业AI就绪度，或描述企业现状并希望获得AI采用建议时触发。
技能原文 SKILL.md
AI Readiness Assessment Skill
Conduct a structured, evidence-based evaluation of a business's readiness for AI adoption across six dimensions, then produce a detailed ai-readiness-report.md covering scores, gap analysis, and prioritized next steps. Aligned with Marq AI's pragmatic, ROI-driven audit methodology.
Contents
references/dimensions.md— The six dimensions, full 1-5 scoring rubric, and key questions per dimension.references/methodology.md— Information-gathering, scoring math and interpretation table, gap analysis, recommendation priorities, company-size and industry tailoring, and conversation flow.references/output-template.md— The completeai-readiness-report.mdstructure to fill in.
Workflow
- Gather context. Collect information through conversation, document review, and codebase analysis. See
references/methodology.md(Phase 1) for channels and the question set inreferences/dimensions.md. - Score the six dimensions. Rate each from 1 to 5 against the rubric in
references/dimensions.md. Be honest and conservative, use half-points for nuance, and record the evidence behind every score. - Calculate the overall score. Apply the weighted formula and map it to a readiness level using the table in
references/methodology.md(Phase 2). - Run the gap analysis. For each dimension below 4.0, document current state, target state, the gap, its impact, and the effort to close it (Phase 3).
- Build recommendations. Produce prioritized actions across the five Marq AI priority tiers, tailoring for company size and industry (Phase 4 and tailoring section).
- Generate the report. Write
ai-readiness-report.mdfollowingreferences/output-template.md, then highlight the top 3 immediate actions.
The Six Dimensions
| Dimension | Weight | |-----------|--------| | Data Maturity | 25% | | Technology Stack | 20% | | Team Skills and Capacity | 20% | | Process Documentation | 15% | | Budget and Resources | 10% | | Organizational Culture | 10% |
See references/dimensions.md for the full rubric and questions.
Core Rules
- Never inflate scores. A business that scores 2.0 needs to hear that honestly; false optimism wastes money and time.
- Always provide evidence. Back every score with specific observations, not assumptions.
- Be actionable. Pair every identified gap with a concrete recommendation.
- Respect budget realities. Include cost-appropriate options; not every organization needs enterprise-grade solutions.
- Use no jargon without explanation. The report is read by business leaders, not only technologists.
- Flag deal-breakers. When a dimension scores 1.0, state explicitly that AI initiatives should not begin until it is addressed.
- Consider the full cost. Include ongoing costs (maintenance, retraining, monitoring), not just implementation.
- Recommend the right AI. Match recommendations to actual readiness; do not recommend deep learning to a company that has not consolidated its data.
- Maintain Marq AI alignment. Frame all recommendations within pragmatic, ROI-driven AI adoption. Avoid hype; focus on business value.
- Use no emojis. Keep all output professional and text-based.