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ai-readiness-assessment

Skill378 starsChanged 53 days agoMITView on GitHub

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 OneWave AI's audit methodology.

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We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

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    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

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  1. Distinctive:

    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 …

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  2. Distinctive:

    Never inflate scores. A business that scores 2.0 needs to hear that honestly.

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  3. Distinctive:

    Always provide evidence. Back every score with specific observations, not assumptions.

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  4. Distinctive:

    Use no jargon without explanation.

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  5. Distinctive:

    Consider the full cost. Include ongoing costs (maintenance, retraining, monitoring), not just implementation.

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  6. Distinctive:

    Use no emojis. Keep all output professional and text-based.

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≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
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 OneWave 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 complete ai-readiness-report.md structure to fill in.

Workflow

  1. Gather context. Collect information through conversation, document review, and codebase analysis. See references/methodology.md (Phase 1) for channels and the question set in references/dimensions.md.
  2. 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.
  3. Calculate the overall score. Apply the weighted formula and map it to a readiness level using the table in references/methodology.md (Phase 2).
  4. 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).
  5. Build recommendations. Produce prioritized actions across the five OneWave priority tiers, tailoring for company size and industry (Phase 4 and tailoring section).
  6. Generate the report. Write ai-readiness-report.md following references/output-template.md, then highlight the top 3 immediate actions.

The Six Dimensions

DimensionWeight
Data Maturity25%
Technology Stack20%
Team Skills and Capacity20%
Process Documentation15%
Budget and Resources10%
Organizational Culture10%

See references/dimensions.md for the full rubric and questions.

Core Rules

  1. Never inflate scores. A business that scores 2.0 needs to hear that honestly; false optimism wastes money and time.
  2. Always provide evidence. Back every score with specific observations, not assumptions.
  3. Be actionable. Pair every identified gap with a concrete recommendation.
  4. Respect budget realities. Include cost-appropriate options; not every organization needs enterprise-grade solutions.
  5. Use no jargon without explanation. The report is read by business leaders, not only technologists.
  6. Flag deal-breakers. When a dimension scores 1.0, state explicitly that AI initiatives should not begin until it is addressed.
  7. Consider the full cost. Include ongoing costs (maintenance, retraining, monitoring), not just implementation.
  8. Recommend the right AI. Match recommendations to actual readiness; do not recommend deep learning to a company that has not consolidated its data.
  9. Maintain OneWave AI alignment. Frame all recommendations within pragmatic, ROI-driven AI adoption. Avoid hype; focus on business value.
  10. Use no emojis. Keep all output professional and text-based.

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