experiment-report-writer

A collection of agent skills for the full ML research workflow, covering paper and code repo initialization, experiment execution, result syncing, documentation updates, and release preparation. Includes a shared project memory system and a core execution loop for structured research automation.

Part of an ML research skill collection covering the full research workflow: initializing paper and code repos, running experiments, syncing results, updating documentation, checking paper readiness, preparing releases, and tagging milestones.

Part of an ML research skills collection that covers the full machine learning research workflow, including initializing paper and code repos, running experiments, syncing results, and preparing releases.

Agent skills for the full ML research workflow, covering project initialization, experiment execution, result syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging.

Part of a collection of agent skills for the full ML research workflow, covering project initialization, experiment execution, results syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging.

A collection of agent skills for the full ML research workflow, covering paper and code repo initialization, experiment execution, result syncing, documentation updates, paper readiness checks, and release preparation.

A skill from the ml-research-skills collection that covers the full ML research workflow, including initializing paper and code repos, running experiments, syncing results, and checking paper readiness.

A Python project initialization skill from ml-research-skills, a collection of agent skills covering the full ML research workflow including experiment execution, result syncing, paper readiness checks, and release preparation with shared project memory.

Agent skills for the full ML research workflow, including initializing LaTeX paper projects and code repositories, running experiments, syncing results, updating documentation, checking paper readiness, and preparing releases.

A skill for synchronizing ML research project artifacts, part of a collection covering the full research workflow from initializing paper and code repositories to running experiments, updating documentation, and preparing releases.