research-planning

Research Planning

Create comprehensive research plans and paper architectures from a research topic or idea.

Input

• $0 — Research topic, idea description, or paper to reproduce

References

• Planning prompts from Paper2Code, AI-Researcher, AgentLaboratory: ~/.claude/skills/research-planning/references/planning-prompts.md
• Output schemas and templates: ~/.claude/skills/research-planning/references/output-schemas.md

Workflow

Step 1: Understand the Research Context

• Read any provided papers, code, or references
• Identify the core research question and its significance
• Assess available resources (datasets, compute, existing code)

Step 2: Generate Research Plan

Use the 4-stage planning approach (adapted from Paper2Code):

1. Overall Plan — Strategic overview: methodology, key experiments, evaluation metrics
2. Architecture Design — File structure, system design, Mermaid class/sequence diagrams
3. Logic Design — Task breakdown with dependencies, required packages, shared knowledge
4. Configuration — Extract or specify hyperparameters, training details, config.yaml

Step 3: Structure the Paper

Design the paper structure with section-by-section plan:

• Abstract, Introduction, Background, Related Work, Methods, Experiments, Results, Discussion/Conclusion
• For each section: key points to cover, required figures/tables, target word count

Step 4: Create Task Dependency Graph

• Order tasks by dependency (data → model → training → evaluation → writing)
• Identify parallelizable tasks
• Flag risks and potential failure modes

Output Format

{
  "research_question": "...",
  "methodology": "...",
  "paper_structure": {
    "sections": ["Abstract", "Introduction", ...],
    "section_plans": { "Introduction": "..." }
  },
  "task_list": [
    {"task": "...", "depends_on": [], "priority": 1}
  ],
  "baselines": ["..."],
  "datasets": ["..."],
  "evaluation_metrics": ["..."],
  "risks": ["..."]
}

Rules

• Each plan component must be detailed and actionable
• Include specific implementation references when available
• Ensure all components work together coherently
• Always include a testing/evaluation plan
• Flag ambiguities explicitly rather than making assumptions

Related Skills

• Upstream: idea-generation, literature-review
• Downstream: experiment-design, paper-assembly
• See also: atomic-decomposition