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yogsoth-aiyogsoth-ai/convergence/skills/maturity-diagnosis/SKILL.md

maturity-diagnosis

Skill2 starsChanged 6 months agoApache-2.0View on GitHub

Assess current readiness of candidates using TRL 9-level, NASSS 7-dimension, and Innovation Readiness Level frameworks.

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    Run bottleneck-identification on the radar data to surface limiting factors

    line 53

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SKILL.md

Maturity Diagnosis

Purpose: Determine how ready a candidate is for implementation by scoring it against established maturity frameworks. Produces a multi-dimensional readiness profile that reveals which aspects are mature and which need development.

When to use:

  • A candidate has been selected and needs readiness assessment before resource commitment
  • Stakeholders need evidence-based maturity scores to inform go/no-go decisions
  • You need to identify which dimensions are lagging and require focused maturation effort

Budget

MetricTarget
Dimensions scored>= 5
Evidence items per dimension>= 2
Bottlenecks identified>= 1

State Ledger

KeyTypeDescription
candidateobjectThe candidate under assessment
dimensions[]arrayDimensions being assessed
scores{}mapDimension -> score mapping
radar_dataobjectSynthesized radar chart data
bottlenecks[]arrayIdentified bottleneck dimensions

Available Tactics

TacticWhen
multi-dimensional-readiness-scanDefault — full readiness assessment across all dimensions

Available SOPs

SOPPurpose
dimension-assessmentScore a single dimension
radar-synthesisCombine scores into radar
bottleneck-identificationFind limiting dimensions

Execution Guidance

  1. Identify relevant dimensions for the candidate (minimum: technical, market, regulatory, resource, organizational)
  2. Deploy dimension-assessment for each dimension — can run in parallel
  3. Feed all scores to radar-synthesis to produce the composite view
  4. Run bottleneck-identification on the radar data to surface limiting factors
  5. Record all findings in state ledger

Output Format

maturity_diagnosis:
  candidate: <name>
  overall_readiness: <1-9 TRL scale>
  dimension_scores:
    technical: {score: N, evidence: [...], gaps: [...]}
    market: {score: N, evidence: [...], gaps: [...]}
    regulatory: {score: N, evidence: [...], gaps: [...]}
    resource: {score: N, evidence: [...], gaps: [...]}
    organizational: {score: N, evidence: [...], gaps: [...]}
  bottlenecks: [{dimension, severity, reason}]
  radar_summary: <text>

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