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symmetry-discovery-questionnaire

symmetry-discovery-questionnaire

github.com/lyndonklData & databases18780 installsUpdated 9w ago
Repository

About symmetry-discovery-questionnaire

A discovery skill for identifying which transformations should preserve or predictably change a model's outputs. It guides data scientists and ML practitioners through domain, coordinate, physical-constraint, and output-behavior questions, then documents symmetry candidates.

Key features

  1. 01Classify domains and data types
  2. 02Analyze coordinate-system choices
  3. 03Test candidate transformations
  4. 04Account for physical constraints
  5. 05Document invariance and equivariance

Use cases

  1. 01Choosing symmetries for an image model
  2. 02Assessing transformations for point-cloud data
  3. 03Determining whether graph node order matters
  4. 04Reviewing output behavior under input transformations

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lyndonkl/claude --skill symmetry-discovery-questionnaire

After installing, type/symmetry-discovery-questionnairein Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

resources/
domain-examples.md
methodology.md
resources/evaluators/
rubric_symmetry_discovery.json

Details

From the project README

Symmetry Discovery Questionnaire

Workflow

Copy this checklist and track your progress:

Symmetry Discovery Progress:
- [ ] Step 1: Classify your domain and data type
- [ ] Step 2: Analyze coordinate system choices
- [ ] Step 3: Test candidate transformations
- [ ] Step 4: Analyze physical constraints
- [ ] Step 5: Determine output behavior under transformations
- [ ] Step 6: Document symmetry candidates

Step 1: Classify your domain and data type

Ask user what their primary data type is. Use this table to identify likely symmetries and guide further questions. Images (2D grids) → likely translation, rotation, reflection. 3D data (point clouds, meshes) → likely SE(3), E(3). Molecules → E(3) + permutation + point groups. Graphs/Networks → permutation. Sets → permutation. Time series → time-translation, periodicity. Tabular → rarely symmetric. Physical systems → conservation laws imply symmetries. For detailed worked examples by domain, consult Domain Examples.

Step 2: Analyze coordinate system choices

Guide user through coordinate analysis questions: Is there a preferred origin? (NO → translation invariance). Is there a preferred orientation? (NO → rotation invariance). Is there a preferred handedness? (NO → reflection invariance). Is there a preferred scale? (NO → scale invariance). Is element ordering meaningful? (NO → permutation invariance). Document each answer with reasoning.

Step 3: Test candidate transformations

For each candidate transformation T, ask: "If I transform my input by T, should my output change?" If NO → invariance to T. If YES predictably → equivariance to T. If YES unpredictably → no symmetry. Use domain-specific checklists from Domain Transformation Tests. Test all relevant transformations systematically. For the detailed methodology behind this testing approach, see Methodology.

Step 4: Analyze physical constraints

Ask about conservation laws and physical symmetries. Noether's theorem: every conservation law implies a symmetry. Energy conserved → time-translation symmetry. Momentum conserved → space-translation symmetry. Angular momentum conserved → rotation symmetry. Ask: Are there physical conservation laws? Is system isolated from external reference frames? Are there gauge freedoms?

Step 5: Determine output behavior under transformations

Critical question: When input transforms, how should output transform? Classification labels → stay same (invariance). Bounding boxes → move with object (equivariance). Force vectors → rotate with system (equivariance). Scalar properties → stay same (invariance). Segmentation masks → transform with image (equivariance). This determines whether you need invariant or equivariant architecture.

Step 6: Document symmetry candidates

Create summary using Output Template. List identified symmetries with confidence levels. Note uncertain cases that need empirical validation. Identify non-symmetries (transformations that DO matter). Recommend next steps for validation and formalization. Quality criteria for this output are defined in Quality Rubric.

Domain Transformation Tests

Image Symmetries
TransformationTest QuestionIf NO →
TranslationDoes object position matter for label?Translation invariance
Rotation (90°)Would rotated image have same label?C4 symmetry
Rotation (any)Would any rotation preserve label?SO(2) symmetry
Horizontal flipWould mirror image have same label?Reflection
ScaleWould zoomed image have same label?Scale invariance
3D Data Symmetries
TransformationTest QuestionIf NO →
3D TranslationDoes absolute position matter?Translation invariance
3D RotationDoes orientation matter?SO(3) or SE(3)
ReflectionDoes handedness matter?O(3) or E(3)
Point permutationDoes point ordering matter?Permutation invariance
Graph Symmetries
TransformationTest QuestionIf NO →
Node relabelingDoes node ID matter, or just connectivity?Permutation invariance
Molecular Symmetries
TransformationTest QuestionIf NO →
RotationIs property independent of orientation?SO(3)
TranslationIs property independent of position?Translation
ReflectionAre both enantiomers equivalent?Include reflections
Atom permutationDo identical atoms behave identically?Permutation
Temporal Symmetries
TransformationTest QuestionIf NO →
Time shiftCan pattern occur at any time?Time-translation
Time reversalIs forward same as backward?Time-reversal
PeriodicityDo patterns repeat with period T?Cyclic symmetry

Quick Reference

The 5 Key Questions:

  1. Is there a preferred coordinate system? (origin, orientation, scale)
  2. Does element ordering matter?
  3. What transformations leave the label unchanged?
  4. What physical constraints apply?
  5. How should outputs transform when inputs transform?

Common Symmetry → Group Mapping:

  • Rotation (2D, discrete) → Cyclic group Cₙ
  • Rotation + reflection (2D) → Dihedral group Dₙ
  • Rotation (2D, continuous) → SO(2)
  • Rotation (3D) → SO(3)
  • Rotation + translation (3D) → SE(3)
  • Full Euclidean (3D) → E(3)
  • Permutation → Symmetric group Sₙ

Output Template

SYMMETRY CANDIDATE SUMMARY
==========================

Domain: [Data type]
Task: [Classification/Regression/Detection/etc.]

IDENTIFIED SYMMETRIES:
1. [Transformation]: [Invariance/Equivariance]
   - Evidence: [Why you believe this]
   - Confidence: [High/Medium/Low]

2. [Transformation]: [Invariance/Equivariance]
   - Evidence: [Why you believe this]
   - Confidence: [High/Medium/Low]

UNCERTAIN SYMMETRIES (need validation):
- [Transformation]: [Reason for uncertainty]

NON-SYMMETRIES (transformations that DO matter):
- [Transformation]: [Why it matters]

NEXT STEPS:
- Empirically validate uncertain symmetry candidates
- Map confirmed symmetries to mathematical groups
- Design architecture based on validated group structure

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