med-deepscientist

Use when a quest has enough evidence to draft or refine a paper, report, or research summary without inventing missing support.

Use when the quest needs an explicit go, stop, branch, reuse-baseline, write, finalize, reset, or user-decision transition with reasons and evidence.

Use when the quest is ready to consolidate final claims, limitations, recommendations, summary state, and graph exports before stopping or archiving.

Use when a quest needs one or more follow-up runs such as ablations, robustness checks, error analysis, or failure analysis after a main experiment.

Use when a quest is ready for a concrete implementation pass or a main experiment run tied to a selected idea and an accepted baseline.

Use when a quest needs concrete hypotheses, limitation analysis, candidate directions, or a selected idea relative to the active baseline.

Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.

Use when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.

Install, repair, and validate the current `ds` runtime on Windows with WSL2 until Linux-side `codex exec`, `ds doctor`, and the Windows browser can reach the local Web UI. Use when an agent needs to bootstrap or fix a Windows plus WSL2 DeepScientist environment, including WSL pre-flight checks, Linux-side Node or uv or Codex CLI setup, auth or relay configuration, proxy or NAT repair, and common failure recovery.

Use when structured numeric data, arrays, or CSV-like measurements should be turned into a publication-quality figure by adapting a bundled paper-style plotting template instead of improvising a new chart from scratch.

Use when a quest needs a polished milestone chart, paper-facing figure, appendix figure, or a mandatory render-inspect-revise pass before treating a figure as final.

Use when an algorithm-first quest should manage candidate briefs, optimization frontier, branch promotion, or fusion-aware search instead of the paper-oriented default loop.

Use when a quest already has a paper, draft, or review package and the task is to map reviewer feedback into experiments, manuscript deltas, and a durable rebuttal / revision response.

Use when a quest does not start from a blank state and the agent must first audit, trust-rank, and reconcile existing baselines, results, drafts, or review materials before choosing the next anchor.

Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.