scientify

Use this when the user needs to choose between multiple ML routes after survey but before committing to implementation. Compares candidate approaches, selects one, records rejected routes, and keeps a fallback.

Use this when the project needs real baseline results before or alongside the main model. Runs classical or literature-aligned baselines under the same protocol and writes a reproducible baseline summary.

Use this when the project needs a dedicated data-quality review before model review. Checks data reality, split correctness, label health, leakage risk, shape consistency, and mock-data disclosure.

Use this when the user wants a draft paper, figure bundle, README, release page, or experiment artifact reviewed before sharing. Checks evidence binding, claim scope, captions, layout clarity, and release readiness.

Use this when the user wants to improve chart quality, standardize plotting style, regenerate release figures, or add captions/protocol notes. Normalizes fonts, colors, legends, units, and scope notes across Scientify figures.

Use this when the user wants to improve README, docs pages, or microsites so a new reader can understand what the project is, how to use it, what artifacts exist, and what the scope boundaries are within one screen.

[Read when prompt contains /research-experiment]

[Read when prompt contains /research-survey]

Use this when the user wants a systems paper, experiment paper, technical report, or extended abstract drafted from existing Scientify artifacts. Builds a claim-bounded paper draft from experiment outputs, figures, and supporting notes.

Use this when the user wants to write a literature review, survey paper, or thesis chapter from existing papers. Guides reading strategy, note-taking, synthesis, and academic writing. NOT for searching new papers (/research-collect) or generating ideas (/idea-generation).

Use this when the user wants research ideas, innovation points, or to find research gaps (找研究方向, 生成创新点). Generates 5 ideas from collected papers, identifies gaps, proposes novel methods with citations. Requires papers in workspace.

Knowledge metabolism: Day 0 init (discuss + baseline survey) or daily cycle (incremental search + update knowledge + generate hypotheses). Triggered by bootstrap and daily cron.

Download academic papers: arXiv source/PDF by ID, DOI papers via Unpaywall open access. Supports batch download.

[Read when prompt contains /research-collect]

[Read when prompt contains /research-implement]

Use this skill when the user wants to research a topic, analyze papers, build ML models, or run experiments. Orchestrates the full pipeline: paper search → analysis → planning → implementation → review → experiments.

[Read when prompt contains /research-plan]

[Read when prompt contains /research-review]