Writes the trace and report for an analysis whose output a reader will judge, restating the question with its binding clauses, recording data provenance, the method with every parameter that matters, results with raw and adjusted numbers and uncertainty, the checks performed, limitations, a decision log of alternatives considered and why they were rejected, and the exact commands that reproduce every number, using the Markdown skeleton included here. Use when delivering any quantitative analysis, model comparison, data audit or computational experiment to a person who did not watch the work; use paper-writing for manuscripts and ml-paper-writing for machine-learning papers.
Scanned
synthetic-sciences/openscience
a08e0ad
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.