🎯Skills55
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.
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.
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.
Agent skills for the full ML research workflow, covering paper and code repo initialization, experiment execution, result syncing, documentation updates, release preparation, and milestone tagging.
A skill from the ML research skills collection that helps craft rebuttals for academic paper reviews. The collection covers the full ML research workflow including experiment management, paper writing, LaTeX project setup, result syncing, and release preparation.
Part of ml-research-skills, 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, release preparation, and milestone tagging.
A comprehensive set of agent skills for the full ML research workflow, including LaTeX project initialization, experiment execution, results syncing, paper submission readiness checks, and milestone tagging.
A paper positioning planner from ml-research-skills, a collection of agent skills for the full ML research workflow covering paper and code repo initialization, experiment execution, result syncing, document updates, and release preparation.
Agent skills for the full ML research workflow, including initializing LaTeX paper and code repositories, running experiments, syncing results, checking paper readiness, and preparing releases. Features a durable project memory system and structured tool-calling loop for coordinated research tasks.
Agent skills for the full ML research workflow, covering LaTeX project setup, experiment execution, result syncing, paper readiness checks, and release preparation.
Agent skills for the complete ML research workflow, covering experiment initialization, paper and code repo management, results syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging.
Part of ml-research-skills, a collection of agent skills for the full ML research workflow including initializing paper and code repos, running experiments, syncing results, updating documentation, checking paper readiness, and preparing releases.
Initializes a new ML research workspace by scaffolding paper and code repositories, setting up experiment tracking, and configuring the shared project memory layer for coordinated agent-driven research workflows.
Agent skills covering the full ML research lifecycle: initializing paper and code repositories, running experiments, syncing results, updating documentation, checking paper readiness, preparing releases, and tagging milestones.
Part of a comprehensive ML research skills suite, this skill designs and writes research slide decks for advisor meetings, lab presentations, progress reports, and conference talks using structured templates.
Mines existing CSV results, logs, reports, and assets to fill claim-evidence gaps in ML research papers before planning new compute. Part of the ml-research-skills workflow covering the full research lifecycle from experiment initialization to camera-ready paper submission.
A collection of agent skills for the full ML research workflow, covering LaTeX paper initialization, experiment execution, result syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging.
Agent skills for the full ML research workflow, covering paper and code repo initialization, experiment execution, result syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging. Includes a shared project memory system for durable coordination across sessions.
Agent skills for the full ML research workflow, including initializing paper and code repos, running experiments, syncing results, updating documentation, checking paper readiness, and preparing releases.
A skill from the ml-research-skills collection that finalizes accepted academic papers by checking rebuttal promises, de-anonymization, claims and evidence alignment, supplement consistency, and submission package completeness.
Agent skills for the full ML research lifecycle: initializing LaTeX paper and code repos, running experiments, syncing results, updating documentation, checking paper readiness, preparing releases, and tagging milestones.
Part of a comprehensive ML research skills collection covering the full workflow from initializing paper and code repos to running experiments, syncing results, preparing releases, and tagging milestones. Supports both Codex and Claude Code agents.
An agent skill for the full ML research workflow, covering experiment initialization, paper and code repo management, result syncing, documentation updates, and milestone tracking.
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.
Agent skills for the full machine learning research workflow, covering LaTeX project initialization, experiment execution, result syncing, documentation updates, paper readiness checks, release preparation, and remote project management.
Part of ml-research-skills, a collection of agent skills for the full ML research workflow. Covers initializing paper and code repositories, running experiments, syncing results, updating documentation, checking paper readiness, preparing releases, and tagging milestones.
Part of the ml-research-skills collection, this skill diagnoses and analyzes experimental results within the ML research workflow, supporting structured debugging alongside experiment and auditing tools.
Agent skills for the full ML research workflow, including initializing paper and code repos, running experiments, syncing results, updating documentation, checking paper readiness, preparing releases, and tagging milestones.
Part of a comprehensive ML research workflow skill set that covers initializing paper and code repositories, running experiments, syncing results, updating documentation, checking paper readiness, and preparing releases.
Part of the ml-research-skills collection, this skill audits the skill system for inventory, lifecycle, routing, memory-writeback, documentation, and validation consistency within the ML research workflow.
Agent skills for the full ML research workflow, covering LaTeX project initialization, experiment execution, remote project control, result syncing, paper submission preparation, and milestone tagging.
Agent skills for the full ML research lifecycle, from initializing paper and code repositories to running experiments, syncing results, checking paper readiness, and preparing releases with durable memory-driven coordination.
A collection of agent skills for the full ML research workflow, covering LaTeX project initialization, experiment execution with remote GPU control, result syncing, paper readiness checks, and release preparation.
Agent skills for the full ML research workflow, covering paper and code repo initialization, experiment execution, result syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging.
Part of ml-research-skills, a collection of agent skills for the full machine learning research workflow, covering paper and code repository initialization, experiment execution, result syncing, documentation updates, paper readiness checks, release preparation, and milestone tagging.
Agent skills for the full ML research workflow, covering paper and code repository initialization, experiment execution, result syncing, documentation updates, readiness checks, release preparation, and milestone tagging.
Part of a skill collection for the full ML research workflow, including experiment execution, result syncing, paper readiness checks, and release preparation. Uses a shared project memory system for durable coordination across tasks.
Agent skills for the full ML research workflow, covering initialization of paper and code repos, running experiments, syncing results, updating docs, checking paper readiness, and preparing releases. Uses a shared project memory system and a core execution loop for durable state across sessions.
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.
Part of the ml-research-skills library for the full ML research workflow, covering paper and code repo initialization, experiment execution, result syncing, documentation updates, and release preparation for academic submissions.
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