Used for high-quality experiment reports, analysis reports, and technical summaries. Evidence must be enough, charts must be real, structure must be clear, and empty talk is forbidden.
Open several fundamentally different routes for one problem fast, so the flow does not keep doing small tweaks on one path only.
Split a complex problem into a multi-step reasoning chain, and make key assumptions, branch points, validation order, and rollback points explicit.
Manual start skill: read `.opencode/` first, then read the task files at the project root and start execution. It is like replacing the first prompt line.
Built-in Python tool priors covering time series, statistics, machine learning, and related ecosystems. It reduces repeated search cost and gives the first coverage of method families and tool families.
In time-series, deep-learning, and LLM projects, use ztxexp to unify experiment config, batch runs, result tracking, ablation analysis, and failure diagnosis.
Align project background, initialize runtime trace / memory files, and output the minimum startup summary for the current task.
Review gaps and risks in requirements, plans, implementations, and experiment results. When the flow spins in place, has weak evidence, or tries to close passively, force a stricter rollback and evidence-completion path.