agent-module-idea-generation

name	agent-module-idea-generation
description	Module: Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty.

Module: Idea Generation

1. Module Metadata

• Module ID: agent.module.idea-generation
• Version: 1.0.0
• Maturity: migrated
• Scope: Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty.
• Primary outcomes:
  • Facilitates operations for Idea Generation.

2. Mission and Applicability

Use this module when you need to execute tasks related to Idea Generation.

Apply when:

• Executing the legacy idea-generation skill workflow.

Do not apply when:

• Functionality falls outside the scope of Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty..

3. Architecture Pattern

• Pattern: Legacy Claude Code Skill Execution Pattern
• Core design rules:
  • This module was automatically migrated from Claude-Research-Paper-OS.
  • Rely on provided shell scripts and reference files in the local directory.

4. Implementation Workflow

Idea Generation

Generate and refine novel research ideas with literature-backed novelty assessment.

Input

• $0 — Research area, task description, or existing codebase context
• $1 — Optional: additional context (e.g., "for NeurIPS", constraints)

Scripts

Novelty check against Semantic Scholar

python /home/lebi/SKILLS/agent-module-claudeos-idea-generation/scripts/novelty_check.py \
  --idea "Adaptive attention head pruning via gradient-guided importance" \
  --max-rounds 5

Performs iterative literature search to assess if an idea is novel.

References

• Ideation prompts (generation, reflection, novelty): /home/lebi/SKILLS/agent-module-claudeos-idea-generation/references/ideation-prompts.md

Workflow

Step 1: Generate Ideas

Given a research area and optional code/paper context:

1. Generate 3-5 diverse research ideas
2. For each idea, provide: Name, Title, Experiment plan, and ratings
3. Use the ideation prompt templates from references

Step 2: Iterative Refinement (up to 5 rounds per idea)

For each idea:

1. Critically evaluate quality, novelty, and feasibility
2. Refine the idea while preserving its core spirit
3. Stop when converged ("I am done") or max rounds reached

Step 3: Novelty Assessment

For each promising idea:

1. Run novelty_check.py or manually search Semantic Scholar / arXiv
2. Use the novelty checking prompts from references
3. Multi-round search: generate queries, review results, decide
4. Binary decision: Novel / Not Novel with justification

Step 4: Rank and Select

• Score each idea on three dimensions (1-10): Interestingness, Feasibility, Novelty
• Be cautious and realistic on ratings
• Select the top idea(s) for development

Output Format

{
  "Name": "adaptive_attention_pruning",
  "Title": "Adaptive Attention Head Pruning via Gradient-Guided Importance Scoring",
  "Experiment": "Detailed implementation plan...",
  "Interestingness": 8,
  "Feasibility": 7,
  "Novelty": 9,
  "novel": true,
  "most_similar_papers": ["paper1", "paper2"]
}

Rules

• Ideas must be feasible with available resources (no requiring new datasets or massive compute)
• Do not overfit ideas to a specific dataset or model — aim for wider significance
• Be a harsh critic for novelty — ensure sufficient contribution for a conference paper
• Each idea should stem from a simple, elegant question or hypothesis
• Always check novelty before committing to an idea

Related Skills

• Upstream: literature-search, deep-research
• Downstream: research-planning, experiment-design
• See also: novelty-assessment

5. Decision Framework

Decision Area	Preferred Option	Alternative	Selection Rule
Skill Usage	Utilize bundled scripts	Manual execution	Prefer automated scripts if available.

6. Validation Strategy

Functional Validation

• Ensure bundled scripts execute without runtime errors.
• Validate that the output matches the expected JSON or Markdown schema.

7. Benchmarks and SLO Targets

• Execution Latency: < 30s for standard local script execution.
• Quality: Output adheres to constraints specified in the prompt references.

8. Risks and Controls

• Risk: Bundled scripts rely on missing absolute paths (/home/lebi/SKILLS/agent-module-claudeos-...).
  • Control: The paths should be mapped or modified to reflect current working paths.

9. Agent Execution Checklist

• Determine required inputs for idea-generation.
• Execute primary workflows or bundled scripts.
• Parse Output Format.

10. Reuse Notes

Adapt only:

• Command-line arguments for the bundled shell/python scripts.