research-planning

Design research plans and paper architectures. Given a research topic or idea, generate structured plans with methodology outlines, paper structure,…

INSTALLATION
npx skills add https://github.com/lingzhi227/agent-research-skills --skill research-planning
Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

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):

  • Overall Plan — Strategic overview: methodology, key experiments, evaluation metrics
  • Architecture Design — File structure, system design, Mermaid class/sequence diagrams
  • Logic Design — Task breakdown with dependencies, required packages, shared knowledge
  • 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

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