networkx

21.8k

davila7

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

pypict-skill

18.0k

sickn33

Pairwise test generation

advanced-evaluation

13.0k

muratcankoylan

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

networkx

10.8k

K-Dense-AI

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

ahp-calculator

376

a5c-ai

Analytic Hierarchy Process (AHP) calculation skill for pairwise comparison matrices, consistency checking, and weight derivation

advanced-evaluation

354

guanyang

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

bio-alignment-pairwise

293

GPTomics

Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.

bio-multi-omics-mixomics-analysis

293

GPTomics

Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.

qe-test-design-techniques

215

proffesor-for-testing

Systematic test design with boundary value analysis, equivalence partitioning, decision tables, state transition testing, and combinatorial testing. Use when designing comprehensive test cases, reducing redundant tests, or ensuring systematic coverage.

test-design-techniques

215

proffesor-for-testing

Systematic test design with boundary value analysis, equivalence partitioning, decision tables, state transition testing, and combinatorial testing. Use when designing comprehensive test cases, reducing redundant tests, or ensuring systematic coverage.

Pairwise Test Generator

71

PramodDutta

Generate optimized test combinations using pairwise (all-pairs) testing algorithms to achieve maximum coverage with minimum test cases across multiple input parameters

Test Parameterization Patterns

71

PramodDutta

Data-driven testing with parameterized tests, test matrices, combinatorial testing, and pairwise parameter generation techniques.

pict-test-designer

37

omkamal

Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT models with parameters, values, and constraints for valid scenarios using pairwise testing. Outputs the PICT model, markdown table of test cases, and expected results.

networkx

15

oimiragieo

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

networkx

9

jackspace

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

infinity-operads

7

plurigrid

∞-Operads for pairwise/tritwise Cat# interactions with lazy ACSet materialization unifying effective, realizability, and Grothendieck topoi via dendroidal Segal spaces.

pict-test-designer

7

Jst-Well-Dan

Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT models with parameters, values, and constraints for valid scenarios using pairwise testing. Outputs the PICT model, markdown table of test cases, and expected results.

pypict-skill

5

agent-skills-hub

Pairwise test generation

pypict-skill

4

ngxtm

Pairwise test generation

advanced-evaluation

4

rohunvora

Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.

pairwise-test-coverage

4

apankov1

Combinatorial testing with a greedy pairwise matrix generator. Covers all factor pairs in near-minimal test cases.

advanced-evaluation

2

itsAR-VR

Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.

networkx

1

iamseungpil

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

advanced-evaluation

1

Kalyanikhandare29

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

pypict-skill

1

rootcastleco

Pairwise test generation

Advanced Evaluation

oki3505F

This skill should be used when the user asks to 'implement LLM-as-judge', 'compare model outputs', 'create evaluation rubrics', 'mitigate evaluation bias', or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

pypict-skill

reikiplanet

Pairwise test generation

networkx

reikiplanet

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

pypict-skill

haniakrim21

Pairwise test generation

Advanced Evaluation

crinkj

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

rerank

ExoPriors

LLM-powered multi-attribute reranking of candidate sets via pairwise comparison. Supports canonical attributes (clarity, technical_depth, insight), custom prompts, model tier selection, and TopK configuration. Use when the task involves: rerank, rank by clarity, rank by insight, rank by depth, best items, quality tier, LLM judge, pairwise comparison, multi-attribute rank, rerank from sql or list. NOT for: simple SQL sorting (ORDER BY date/upvotes/score -- use scry), semantic search or embedding algebra (use vector-composition), or people identity resolution (use people-graph).

pypict-skill

oki3505F

Pairwise test generation