machine-learning
304
roleplay-as-remote-viewing-bot
Roleplay as a Remote Viewing AI trained on coordinate/target pairings to describe targets, answer questions, and provide recommendations.
ECNU-ICALK
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
304
qcsd-refinement-swarm
Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.
proffesor-for-testing
data-ai
machine-learning
304
qe-defect-intelligence
Predicts defect-prone code using change frequency, complexity metrics, and historical bug patterns. Use when predicting defects before they escape, analyzing root causes of test failures, learning from past defect patterns, or implementing proactive quality management.
proffesor-for-testing
data-ai
machine-learning
304
qe-learning-optimization
Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.
proffesor-for-testing
data-ai
machine-learning
304
qcsd-cicd-swarm
QCSD Verification phase swarm for CI/CD pipeline quality gates using regression analysis, flaky test detection, quality gate enforcement, and deployment readiness assessment. Consumes Development outputs (SHIP/CONDITIONAL/HOLD decisions, quality metrics) and produces signals for Production monitoring.
proffesor-for-testing
data-ai
machine-learning
304
qcsd-development-swarm
QCSD Development phase swarm for in-sprint code quality assurance using TDD adherence, code complexity analysis, coverage gap detection, and defect prediction. Consumes Refinement outputs (BDD scenarios, SFDIPOT priorities) and produces signals for Verification.
proffesor-for-testing
data-ai
machine-learning
304
qcsd-production-swarm
QCSD Production Telemetry phase swarm for post-release production health assessment using DORA metrics, root cause analysis, defect prediction, and cross-phase feedback loops. Consumes CI/CD outputs (RELEASE/REMEDIATE/BLOCK decisions, release readiness metrics) and produces feedback signals to Ideation and Refinement.
proffesor-for-testing
data-ai
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
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