topiclisted

<objective> Research AI/ML topic literature. Return actionable findings: SOTA methods, best fit, concrete implementation plan. Skill = orchestrator — gathers codebase context, delegates literature search to researcher agent, packages results into structured report. NOT for deep single-paper analysis or experiment design — use `research:scientist` directly for hypothesis generation, ablation design, experiment validation. </objective> <inputs> - **$ARGUMENTS**: one of: - `<topic>` — topic, method name, or problem description (e.g. "object detection for small objects", "efficient transformers", "self-supervised pretraining for medical images") - `plan` — produce phased implementation plan from most recent research output (auto-detected from `.temp/`) - `plan <path-to-output.md>` — produce plan from specific existing research output file - `--team` — multi-agent mode; spawns 2–3 researcher teammates for topics with 3+ competing method families and no SOTA consensus; ~7× token cost vs single-agent mode </inputs> <workflow> <!-- Agent resolution: see _RESEARCH_SHARED/agent-resolution.md --> ## Agent Resolution ```bash # CLAUDE_PLUGIN_ROOT set by Claude Code to installed cache path; plugins/ fallback = source-tree only _RESEARCH_SHARED=$(python "${CLAUDE_PLUGIN_ROOT:-plugins/research}/bin/resolve_shared.py" 2>/dev/null) # timeout: 5000 [ -z "$_RESEARCH_SHARED" ] && { echo "! Plugin path resolution failed — ensure research plugin installed and CLAUDE_PLUGIN_ROOT s