nika-init 0.64.0

Nika project scaffolding — course generator, workflow templates, showcase
Documentation 
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# =============================================================================
# SHOWCASE 04 — Data-Driven Chart Generator
# =============================================================================
# requires_llm: true
# category: advanced
#
# Gathers system data via exec:, uses an LLM to analyze, generates
# chart images, then creates a visual report using vision.

schema: "nika/workflow@0.12"
workflow: chart-generator
provider: "{{PROVIDER}}"
model: "{{MODEL}}"

artifacts:
  dir: .

tasks:
  - id: gather_data
    exec: "echo '{\"cpu_cores\": 8, \"memory_gb\": 16, \"disk_usage_pct\": 67}'"

  - id: analyze_data
    depends_on: [gather_data]
    with:
      metrics: $gather_data
    infer:
      prompt: |
        Given these system metrics: {{with.metrics}}
        Return JSON with chart_type, title, and 3 insights.
      response_format: json
      temperature: 0.4
      max_tokens: 800

  - id: resource_chart
    invoke:
      tool: "nika:chart"
      params:
        type: "bar"
        title: "System Resource Usage"
        width: 900
        height: 600
        series:
          - name: "Current"
            data: [67, 45, 78, 52]
          - name: "Threshold"
            data: [80, 80, 80, 80]
        labels: ["Disk %", "Memory %", "CPU %", "Swap %"]
    artifact:
      path: output/resource-chart.png
      format: binary

  - id: trend_chart
    invoke:
      tool: "nika:chart"
      params:
        type: "line"
        title: "Load Average Trend (24h)"
        width: 900
        height: 500
        series:
          - name: "1-min avg"
            data: [3.2, 2.8, 2.1, 1.5, 1.2, 2.4, 3.1, 2.7]
        labels: ["00:00", "03:00", "06:00", "09:00", "12:00", "15:00", "18:00", "21:00"]
    artifact:
      path: output/trend-chart.png
      format: binary

  - id: visual_report
    depends_on: [resource_chart, trend_chart, analyze_data]
    with:
      resources: $resource_chart
      trends: $trend_chart
      analysis: $analyze_data
    infer:
      content:
        - type: image
          source: "{{with.resources.media[0].hash}}"
          detail: high
        - type: image
          source: "{{with.trends.media[0].hash}}"
          detail: high
        - type: text
          text: |
            Two system monitoring charts above. LLM analysis: {{with.analysis}}
            Write a monitoring report covering utilization, trends, and recommendations.
      temperature: 0.3
      max_tokens: 1500
    artifact:
      path: output/system-report.md