{
  "pattern_count": 8,
  "patterns": [
    {
      "name": "builder_map_decomposition",
      "description": "\u2014 Map tool/command identifiers to factory functions to eliminate switch ladders and ease extension (evidence: MCP server Iteration 1)."
    },
    {
      "name": "pipeline_config_struct",
      "description": "\u2014 Gather shared parameters into immutable config structs so orchestration functions stay linear and testable (evidence: MCP server Iteration 1)."
    },
    {
      "name": "helper_specialization",
      "description": "\u2014 Push tracing/metrics/error branches into helpers to keep primary logic readable and reuse instrumentation (evidence: MCP server Iteration 1)."
    },
    {
      "name": "jq_pipeline_segmentation",
      "description": "\u2014 Treat JSONL parsing, jq execution, and serialization as independent helpers to confine failure domains (evidence: MCP server Iteration 2)."
    },
    {
      "name": "automation_first_metrics",
      "description": "\u2014 Bundle metrics capture in scripts/make targets so every iteration records complexity & coverage automatically (evidence: MCP server Iteration 2, CLI Iteration 3)."
    },
    {
      "name": "documentation_templates",
      "description": "\u2014 Use standardized iteration templates + generators to maintain BAIME completeness with minimal overhead (evidence: MCP server Iteration 3, CLI Iteration 3)."
    },
    {
      "name": "conversation_turn_builder",
      "description": "\u2014 Extract user/assistant maps and assemble turns through helper orchestration to control complexity in conversation analytics (evidence: CLI Iteration 4)."
    },
    {
      "name": "prompt_outcome_analyzer",
      "description": "\u2014 Split prompt outcome evaluation into dedicated helpers (confirmation, errors, deliverables, status) for predictable analytics (evidence: CLI Iteration 4)."
    }
  ]
}