method-design

name	method-design
description	Use this skill whenever the user wants to formalize a network architecture and derive theoretical components from a research idea. Triggers include: 'method design', 'design method', 'network architecture', 'formula derivation', 'method-design', 'theoretical framework', 'derive equations', 'compare alternatives', 'architecture tournament', 'rank designs', 'design space exploration', or any request to transform IDEA.md into a detailed METHOD.md. This skill is the **mandatory interface-layer method formalizer** in NeuroClaw: it reads IDEA.md, optionally drafts N alternative architectures and ranks them via a Bradley-Terry pairwise tournament (Tournament Mode), designs concrete network structures (layers, modules, connections), performs mathematical derivations (equations, loss functions, proofs), and always outputs a structured METHOD.md.
license	MIT License (NeuroClaw custom skill – freely modifiable within the project)
layer	interface
skill_type	workflow
dependencies	["academic-research-hub"]

Method Design

Overview

This skill implements the Framework Formalization & Theoretical Derivation process for the NeuroClaw method-design phase.

It acts as the Method Architect within the multi-agent framework:

• Reads the latest IDEA.md from the workspace.
• Designs the specific neural network structure (e.g., CNN backbone, attention modules, MRI-specific layers) tailored to the neuroscience task.
• Derives all necessary formulas (loss functions, gradients, convergence proofs, etc.) using symbolic or step-by-step reasoning.
• Produces a clean, publication-ready METHOD.md with sections: Architecture Diagram (text description), Detailed Layers, Mathematical Formulation, Implementation Notes, and Pseudocode.

If any part is ambiguous, it asks the user for clarification before proceeding.
Research use only — the output is a mathematically rigorous METHOD.md ready for experiment-controller and paper-writing.

Quick Reference (Method Flow)

Step	Description	Output File
1. Read & Parse	Load and analyze IDEA.md	01_idea_summary.md
2a. Draft Alternatives (Tournament Mode)	Sketch N candidate architectures	02a_candidates.json
2b. Lit Probe (Tournament Mode)	Per-candidate literature check via academic-research-hub	02b_lit_check.md
2c. Tournament (Tournament Mode)	Pairwise LLM-judge + Bradley-Terry ranking	02c_ranking.csv
2. Architecture Design	Define network layers & modules (top-1 from tournament if used)	02_architecture.md
3. Formula Derivation	Derive equations & proofs	03_formulas.md
4. Pseudocode & Notes	Generate implementation details	04_pseudocode.md
5. Finalize	Compile and polish	METHOD.md

Installation

# Place files in: skills/method-design/

Tournament Mode (Optional Step 2 Expansion)

When the user asks to "compare alternatives", "rank designs", "explore the design space", or provides multiple architecture sketches, run a Bradley-Terry pairwise tournament instead of single-shot architecture selection.

Inputs

• N >= 2 architecture candidates, each with name and rationale fields
• Saved as 02a_candidates.json (JSON array)

Mechanism

1. Generate all C(N,2) unordered pairs (capped at 300 for cost)
2. For each pair, prompt an LLM judge with the criteria in scripts/tournament.py::ARCHITECTURE_JUDGE_SYSTEM_PROMPT (soundness > novelty > tractability > NeuroClaw compatibility > falsifiability)
3. Aggregate verdicts into a Bradley-Terry strength score via choix.ilsr_pairwise (alpha=0.1)
4. Pick top-1 for downstream Step 3 (formula derivation)

Invocation

python skills/method-design/scripts/tournament.py \
    --candidates 02a_candidates.json \
    --output 02c_ranking.csv \
    --backend openai --model gpt-4o-mini

When to skip Tournament Mode

• User has already named a single specific architecture
• N < 2 candidates available
• User explicitly asks for single-shot design

Cost notes

• Each pair costs ~1 LLM call; total cost = min(C(N,2), 300) calls
• For N=5: 10 calls (~$0.01 with gpt-4o-mini)
• For N=10: 45 calls
• Concurrency capped at 16 by default

Provenance

Pairwise tournament approach adapted from FutureHouse/Robin (arXiv:2505.13400, Nature 2026), MIT License. NeuroClaw adaptation: LLM-backend-agnostic via injectable llm_call, neuroimaging-specific judge criteria, no Edison platform dependency.

Important Notes & Limitations

• Always starts from the latest IDEA.md; stops and prompts if missing.
• Every step saved as numbered .md files for transparency and resumption.
• Uses symbolic derivation (no hallucinated math); can call claw-shell or code tools internally for verification.
• Final output always saved as METHOD.md in workspace root.
• Diagrams are described in text (PlantUML/Mermaid ready); no image generation here.

When to Call This Skill

• Immediately after research-idea skill completes IDEA.md
• When the user wants to specify network details or mathematical foundation
• Before experiment-controller or paper-writing

Complementary / Related Skills

• research-idea → provides IDEA.md (input)
• experiment-controller → consumes METHOD.md
• paper-writing → consumes METHOD.md

Reference

NeuroClaw architecture (section 1.5 method-design skill).
Flow: IDEA.md parsing → architecture design → equation derivation → pseudocode → METHOD.md

Created At: 2026-03-24 00:00 HKT Last Updated At: 2026-05-27 (Tournament Mode added)
Author: chengwang96