topic

<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> <compaction> Key boundary: end of Step 2 — SOTA literature gathered and written to AGENT_OUT; before Step 3 report synthesis. Preserve: AGENT_OUT path (TMPDIR key), BRANCH (TMPDIR key), DATE (TMPDIR key), REPORT_OUT target path, topic string from ARGUMENTS. Clear at Step 1 start (stale prior run) and at follow-up gate (terminal action). </compaction> <workflow> <!-- Agent resolution: see _RESEARCH_SHARED/agent-resolution.md --> ## Agent Resolution **A...