method-design · diff
git:20260506.86420c0 to git:20260529.18bb4b7
52 added, 4 removed. Audit A to A.
---
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', 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, designs concrete network structures (layers, modules, connections), performs mathematical derivations (equations, loss functions, proofs), and always outputs a structured METHOD.md."
+ 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: []
+ 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 |
- | 2. Architecture Design | Define network layers & modules | 02_architecture.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
```bash
# 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
+ ```bash
+ 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: 2026-03-26 00:14 HKT
+ Last Updated At: 2026-05-27 (Tournament Mode added)
Author: chengwang96