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symmetry-discovery-questionnaire对称发现问卷

Agent Skill

symmetry-discovery-questionnaire 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:symmetry-discovery-questionnaire(对称发现问卷)
来源仓库:https://github.com/lyndonkl/claude
仓库路径:skills/symmetry-discovery-questionnaire
安装命令:
npx skills add https://github.com/lyndonkl/claude --skill symmetry-discovery-questionnaire
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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

简介

symmetry-discovery-questionnaire 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于需求调研、用户画像构建或产品方向探索等研究检索类任务场景。
  • 通过关键词、任务描述或来源线索触发检索,返回结构化候选信息供进一步核验。
  • 安装命令为 npx skills add https://github.com/lyndonkl/claude --skill symmetry-discovery-questionnaire。
  • 使用前建议确认权限范围、维护状态,以及是否会触发联网或文件读写操作。

SKILL.md

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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