mcpField
R
research-planning

research-planning

github.com/lingzhi227Productivity4401.8k installsUpdated 35w ago
Repository

About research-planning

research-planning is a skill for turning a research topic, idea, or paper reproduction task into an actionable plan and paper outline. It covers methodology, system and task design, paper sections, and task dependencies, while flagging ambiguities.

Key features

  1. 01Design methodology and evaluation plans
  2. 02Specify architecture and task breakdowns
  3. 03Outline paper sections and content
  4. 04Map task dependencies and risks
  5. 05Define configuration and hyperparameters

Use cases

  1. 01Plan a project from a research idea
  2. 02Organize implementation tasks for paper reproduction
  3. 03Draft a paper structure alongside experiments

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lingzhi227/agent-research-skills --skill research-planning

After installing, type/research-planningin Claude Code to confirm it loaded.

Bundled filesreferences/output-schemas.mdreferences/planning-prompts.md

Details

From the project README

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

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