tda-diagnosing-computational-defects
CommunityDiagnose unexpected TDL results and computational defects efficiently.
Authorstephendor
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill is designed to diagnose unexpected results in TDL (Topological Data Analysis) computations, identifying and addressing computational defects.
Core Features & Use Cases
- Unexpected Result Diagnosis: Detects issues like unexpected changes in results, null model inconsistencies, and non-deterministic behavior.
- Defect Lane Classification: Helps classify the type of defect, such as topology, stochastic/null model, statistical issues, or representation problems.
- Command Execution and Reproduction: Executes specific commands to reproduce defects and minimize datasets for easier analysis.
- Hypothesis Generation and Testing: Encourages the creation of falsifiable hypotheses and their testing.
- Post-Mortem Analysis: Provides a framework for post-mortem analysis, including cause, prevention, and impact assessment.
Quick Start
Run the tda-diagnosing-computational-defects skill to diagnose unexpected changes in your TDL computations.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: tda-diagnosing-computational-defects Download link: https://github.com/stephendor/TDL/archive/main.zip#tda-diagnosing-computational-defects Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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