Generate execution-ready research plans with experiments and expected outcomes.
npx skills add https://github.com/TenureAI/PhD-Zero --skill research-plan-tenureai
Please help me install this Agent Skill. Skill: research-plan Source: https://github.com/TenureAI/PhD-Zero/tree/main/.agents/skills/research-plan Command: npx skills add https://github.com/TenureAI/PhD-Zero --skill research-plan-tenureai
This Skill transforms abstract research ideas into concrete, actionable plans with defined experiments, implementation requirements, and expected outcomes, streamlining the research process.
Use the research-plan skill to create a detailed research plan for the objective of improving image classification accuracy using a new attention mechanism.
High-intent search queries and answers about installing and using this skill.
To create an actionable research plan, define core research questions, innovation points, baseline comparisons, and expected outcomes. Structuring experiment designs and codebase requirements ensures the proposal is execution-ready and directly addresses the project objectives.
Experiment design in a research proposal defines specific ablation plans, workloads, and baseline comparisons to validate innovation points. It is needed to transform abstract ideas into structured execution paths that prove algorithmic efficiency or performance gains.
Structure an experiment roadmap by segmenting the project into defined problem statements, core research questions, and specific ablation plans. Include implementation requirements, data workloads, and expected results to ensure the roadmap is execution-ready.
Generating a study design for algorithm efficiency improvements requires defining codebase requirements and data workloads as part of the plan. Without specifying these implementation dependencies, the expected performance gains and baseline comparisons cannot be accurately evaluated.
The best way to outline pre-implementation planning is to document problem definitions, research questions, and innovation points alongside structured experiment plans. Detailing expected results and execution steps ensures the machine learning paper has a solid, actionable foundation.
Limitations in generating a research plan include the necessity of providing explicit core research questions and innovation points. If abstract ideas lack defined experiment designs, baseline comparisons, or data requirements, the resulting study design will lack execution-ready structure.
Ocean326Document product problems and opportunities through structured discovery and stakeholder interviews.
THUFanZdDesign ablation studies with component removals, hyperparameter sweeps, and execution order.
WILLOSCARVerify citations remain within their original subsections after text edits.
robikscubeGenerate CSI MasterFormat outline specifications from unstructured construction material lists.
