Opus sub-agent

DataLens spawns Opus sub-agent for system design decisions, complex refactoring, ambiguous requirements, or high-stakes decisions. DataLens Agent (Opus) developed the project goal field design proposal. Phase 2 Strategy Research & Decision Point involves the use of Opus 4.6 agent for generating PHASE2_UNIFIED_STRATEGY.md document DataLens agent spawns Opus sub-agent to handle system design decisions, complex refactoring, ambiguous requirements, and high-stakes decisions. Opus 4.6 created the PHASE2_UNIFIED_STRATEGY.md which contains pipeline design, tool justifications, and question-to-data mapping. Opus 4.6 and Sonnet 4.6 together were used to triangulate decision for the Phase 2 strategy due to GPT-5.2 unavailability. Opus 4.6 created the PHASE2_SCOPE_DECISION.md which compares MVP vs Ambitious approaches, provides timeline, risk analyses, and file type strategies. Opus 4.6 recommends text extraction from all 48 PDFs with OCR only where relevant due to medium-high ROI and 2-3 hours effort estimation. Opus 4.6 recommends parsing all DOCX files if policy questions are real; otherwise selectively processing top 20, valuing DOCX as having high ROI for 3-4 hours effort. Opus 4.6 recommends processing all 3 PPTX files due to low cost and consistent summary value. Opus 4.6 recommends processing all 2 MSG files as they provide governance signals. Opus 4.6 recommends the MVP approach as a minimum viable product completing 25 of 35 analytical questions in 5 to 7 days with selective ingestion. Opus 4.6 recommends against full 153-file load due to complexity and risk, suggesting a phased selective ingestion instead. Opus analysis investigates the behavior of agent_skills.py process_message() to diagnose async exception issues.