Extracted Fields

After processor.runSync(tree, file), normalized field metadata is available at file.data.aimdFields.

What Stays As Arrays

These scopes are still simple string[], and each string is an identifier:

• var
• step
• check
• ref_step
• ref_var
• ref_fig
• ref_media
• cite

What Uses Structured Objects

• var_definitions[] mirrors simple var fields with id, type, default, title, description, examples, and raw AIMD kwargs
• var_table[] exposes canonical id plus optional table-level title, description, examples, and raw AIMD kwargs
• var_table[].subvars[] exposes canonical id plus optional column-level title, description, and examples
• client_assigner[] exposes id, mode, dependent_fields, assigned_fields, and function_source extracted from assigner(config, function ...) client blocks
• workflow[] exposes workflow definitions from fenced workflow blocks, including version, id, nodes, assigners, transition ids, normalized from / to arrays, transition inputs, grouped target assign, logic, and default_initial_node
• quiz[] already exposes id
• fig[] exposes id, src, title, and legend from fenced fig blocks
• media[] exposes id, kind, src, mime, provider, poster, title, and legend from fenced media blocks
• refs[] exposes BibTeX entries from fenced refs blocks with id, entry_type, raw, normalized fields, and display fields such as title, author, year, doi, and url
• step_hierarchy[] exposes id, step, parent_id, prev_id, next_id, estimated_duration_ms, timer_mode, has_check, and has_children

Example

{
  "var": ["temperature"],
  "var_definitions": [
    {
      "id": "temperature",
      "type": "float",
      "default": 36.5,
      "title": "Temperature",
      "description": "Ambient temperature in Celsius",
      "examples": [25.0, 37.0],
      "kwargs": {
        "title": "Temperature",
        "description": "Ambient temperature in Celsius",
        "examples": [25.0, 37.0],
        "gt": 0
      }
    }
  ],
  "var_table": [
    {
      "id": "samples",
      "scope": "var_table",
      "title": "Samples",
      "description": "Measured sample rows",
      "examples": ["S-001 row"],
      "subvars": [
        {
          "id": "sample_id",
          "title": "Sample ID",
          "description": "Tube identifier",
          "examples": ["S-001"]
        },
        {
          "id": "concentration",
          "title": "Concentration",
          "examples": [1.0]
        }
      ]
    }
  ],
  "client_assigner": [
    {
      "id": "calculate_total",
      "runtime": "client",
      "mode": "auto",
      "dependent_fields": ["a", "b"],
      "assigned_fields": ["total"],
      "function_source": "function calculate_total({ a, b }) { return { total: a + b }; }"
    }
  ],
  "workflow": [
    {
      "version": "airalogy.workflow.v1",
      "id": "parameter_optimization",
      "title": "Parameter Optimization Workflow",
      "nodes": [
        {
          "id": "prep",
          "protocol": "./protocols/sample-prep/protocol.aimd",
          "title": "Sample Preparation"
        },
        {
          "id": "analysis",
          "protocol": "./protocols/analysis/protocol.aimd",
          "title": "QC Analysis"
        }
      ],
      "assigners": [
        {
          "id": "optimize_parameters",
          "runtime": "python",
          "entrypoint": "./assigners/optimize_parameters.py:assign"
        }
      ],
      "transitions": [
        {
          "id": "retry_after_qc_failure",
          "from": ["analysis"],
          "to": ["prep"],
          "when": "${analysis.check.pass_qc.checked} == false",
          "run": "optimize_parameters",
          "inputs": {
            "summary": "${analysis.var.summary}",
            "failed_metrics": "${analysis.var.failed_metrics}"
          },
          "max_iterations": 5,
          "assign": {
            "prep": {
              "var.target_temperature_c": "${retry_after_qc_failure.outputs.recommended_temperature_c}",
              "var.target_concentration_m": "${retry_after_qc_failure.outputs.recommended_concentration_m}",
              "var.retry_note": "${retry_after_qc_failure.outputs.retry_reason}"
            }
          }
        }
      ],
      "default_initial_node": "prep"
    }
  ],
  "refs": [
    {
      "id": "yang2025airalogyaiempowereduniversaldata",
      "entry_type": "misc",
      "title": "Airalogy: AI-empowered universal data digitization for research automation",
      "author": "Zijie Yang and Qiji Zhou and Fang Guo and Sijie Zhang and Yexun Xi and Jinglei Nie and Yudian Zhu and Liping Huang and Chou Wu and Yonghe Xia and Xiaoyu Ma and Yingming Pu and Panzhong Lu and Junshu Pan and Mingtao Chen and Tiannan Guo and Yanmei Dou and Hongyu Chen and Anping Zeng and Jiaxing Huang and Tian Xu and Yue Zhang",
      "year": "2025",
      "url": "https://arxiv.org/abs/2506.18586"
    }
  ],
  "media": [
    {
      "id": "lecture_video",
      "kind": "video",
      "src": "files/videos/lecture.mp4",
      "mime": "video/mp4",
      "poster": "files/videos/lecture-poster.jpg",
      "title": "Lecture Video"
    }
  ],
  "step_hierarchy": [
    {
      "id": "sample_preparation",
      "level": 1,
      "sequence": 0,
      "step": "1",
      "next_id": "data_analysis"
    }
  ]
}

If you are upgrading older integrations, note that the old name aliases have been removed. Read Migration.