machine-learning
304
qcsd-refinement-swarm
QCSD Refinement phase swarm for Sprint Refinement sessions using SFDIPOT product factors, BDD scenario generation, and requirements validation.
proffesor-for-testing
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
304
qe-debug-loop
Hypothesis-driven autonomous debugging with real command validation
proffesor-for-testing
data-ai
machine-learning
304
qe-defect-intelligence
AI-powered defect prediction, pattern learning, and root cause analysis for proactive quality management.
proffesor-for-testing
data-ai
machine-learning
304
qe-enterprise-integration-testing
Orchestration skill for enterprise integration testing across SAP, middleware, WMS, and backend systems. Covers E2E enterprise flows, SAP-specific patterns (RFC, BAPI, IDoc, OData, Fiori), cross-system data validation, and enterprise quality gates.
proffesor-for-testing
data-ai
machine-learning
304
qe-holistic-testing-pact
Apply the Holistic Testing Model evolved with PACT (Proactive, Autonomous, Collaborative, Targeted) principles. Use when designing comprehensive test strategies for Classical, AI-assisted, Agent based, or Agentic Systems building quality into the team, or implementing whole-team quality practices.
proffesor-for-testing
data-ai
machine-learning
304
qe-learning-optimization
Transfer learning, metrics optimization, and continuous improvement for AI-powered QE agents.
proffesor-for-testing
data-ai
machine-learning
304
qe-middleware-testing-patterns
Enterprise middleware testing patterns for message routing, transformation, DLQ, protocol mediation, ESB error handling, and EIP patterns. Use when testing middleware layers, message brokers, ESBs, or integration buses.
proffesor-for-testing
data-ai
machine-learning
304
qe-observability-testing-patterns
Observability and monitoring validation patterns for dashboards, alerting, log aggregation, APM traces, and SLA/SLO verification. Use when testing monitoring infrastructure, dashboard accuracy, alert rules, or metric pipelines.
proffesor-for-testing
data-ai
machine-learning
304
qe-pentest-validation
Orchestrate security finding validation through graduated exploitation. 4-phase pipeline: recon (SAST/DAST), analysis (code review), validation (exploit proof), report (No Exploit, No Report gate). Eliminates false positives by proving exploitability.
proffesor-for-testing
data-ai
machine-learning
304
qe-pr-review
Scope-aware GitHub PR review with user-friendly tone and trust tier validation
proffesor-for-testing
data-ai
machine-learning
304
qe-release
End-to-end npm release workflow with verification gates and hardcoded-version protection
proffesor-for-testing
data-ai
machine-learning
304
qe-sfdipot-product-factors
James Bach's HTSM Product Factors (SFDIPOT) analysis for comprehensive test strategy generation. Use when analyzing requirements, epics, or user stories to generate prioritized test ideas across Structure, Function, Data, Interfaces, Platform, Operations, and Time dimensions.
proffesor-for-testing
data-ai
machine-learning
304
qe-test-automation-strategy
Design and implement effective test automation with proper pyramid, patterns, and CI/CD integration. Use when building automation frameworks or improving test efficiency.
proffesor-for-testing
data-ai
machine-learning
304
qe-test-idea-rewriting
Transform passive 'Verify X' test descriptions into active, observable test actions. Use when test ideas lack specificity, use vague language, or fail quality validation. Converts to action-verb format for clearer, more testable descriptions.
proffesor-for-testing
data-ai
machine-learning
304
scikit-learn-pipeline-with-ner-and-vader-feature-engineering
Constructs a scikit-learn text classification pipeline that integrates custom feature engineering steps: one-hot encoding of spaCy NER labels for a predefined set of 18 classes and VADER sentiment analysis.
ECNU-ICALK
data-ai
machine-learning
304
sequential-ml-problem-formulation
Formulate a machine learning problem statement that utilizes a sequential scheme involving two distinct ML approaches, where the output of the first subtask serves as the input for the second.
ECNU-ICALK
data-ai
machine-learning
304
generate-inference-code-for-image-to-html-keras-model
Generates Python code to perform inference on a pre-trained Keras Image-to-HTML model, utilizing specific image preprocessing (aspect-ratio preserving resize and padding) and a greedy decoding loop to predict HTML sequences from images.
ECNU-ICALK
data-ai
machine-learning
304
gnn-ppo-continuous-stability-entropy
Implements a PPO agent utilizing a Graph Neural Network (GNN) for state embeddings and continuous action spaces. The policy update integrates a custom stability loss based on node features and an entropy regularization term, ensuring efficient computation and stable training.
ECNU-ICALK
data-ai
machine-learning
304
gpt2-jsonl-finetuning-optimization
Fine-tune GPT-2 on JSONL datasets (supporting both generic text and Q&A formats) using Hugging Face Transformers, with a focus on memory-efficient training strategies like mixed precision and gradient accumulation.
ECNU-ICALK
data-ai
machine-learning
304
keras-iterative-training-and-prediction-wrapper
Generates Keras implementations for an iterative training loop (finding the best model over multiple attempts) and a prediction wrapper, based on a provided sklearn MLP logic.
ECNU-ICALK
data-ai
machine-learning
304
matlab-regression-model-comparison-and-visualization
Implements a MATLAB function to compare linear polynomial models (orders 1 to m) and a non-linear exponential model (y=ce^bx) using RMSE. Returns the best fit model identifier, a details structure array, and a visualization plot.
ECNU-ICALK
data-ai
machine-learning
304
minesweeper-prediction-and-solver-development
Develop a Python-based Minesweeper prediction tool for a 5x5 grid using historical data to identify safe spots and mine locations. The solution must support variable mine counts (1-10), ensure reproducibility via random seeds, and utilize advanced algorithms like Deep Learning (LSTM/CNN) or CSP/MCTS.
ECNU-ICALK
data-ai
machine-learning
304
numpy-vectorized-stitching
Implements a high-performance, robust image stitching pipeline using NumPy for geometric transformations (DLT, RANSAC, vectorized warping) and OpenCV for feature extraction. Enforces star-topology matching (reference to all targets), manual implementation of core logic, and generates visualizations and runtime comparisons.
ECNU-ICALK
data-ai
machine-learning
304
polars-mstl-decomposition-data-preparation
Prepare Polars DataFrames for MSTL time series decomposition by splitting data into train and validation sets, specifically resolving list aggregation type mismatches during anti-joins.
ECNU-ICALK
data-ai