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Robustness and Reproducibility Stress Testing

jse-tju-robustness-reproducibility

brycewang-stanford/Awesome-Journal-Skills

Stress-test core claims for Journal of Systems Engineering submissions: parameter sensitivity, initial conditions, alternative models and metrics, extreme scenarios, Monte Carlo repetitions, random seeds, software environments, data/code statements, and explicit failure boundaries.

Systems Engineering Topic Selection

jse-tju-topic-selection

brycewang-stanford/Awesome-Journal-Skills

This skill guides researchers in selecting and refining topics for the Journal of Systems Engineering (Tianjin University). It helps define system boundaries, identify feedback loops, and ensure testable questions. It prevents vague topics by requiring specific system structures and verifiable outcomes. Suitable for academic research in systems engineering, operations research, and complex systems.

MCP Server Release Quality Assurance

github/awesome-copilot

This skill verifies an MCP server before release by exercising a real protocol session. It compares runtime capabilities with source code and documentation, tests failure paths, and records reproducible evidence. Use this when shipping or reviewing an MCP server, tool, resource, prompt, catalog, or install path to ensure protocol behavior and transport correctness.

OpenSquilla Skill Hub Canary Fixture

opensquilla-live-skill-hub-canary

TokenRhythm/opensquilla

This is a synthetic, instructional fixture designed solely for verifying the delivery and access of community skills within the OpenSquilla live Skill Hub canary environment. It is crucial for testing platform capability (e.g., resource access validation) and should not be executed for actual functionality or command processing.

Roslyn Analyzer Development

roslyn-analyzers

github/awesome-copilot

Build, review, debug, package, and test Roslyn diagnostic analyzers, code fix providers, and incremental source generators. Covers IOperation analysis, dependency pinning, test harnesses, and NuGet packaging.

Automated Test Gap Audit

github/awesome-copilot

This skill performs a read-only audit to identify missing, weak, or stale test coverage in codebases. It analyzes full repositories or specific scopes like PRs, features, and APIs to recommend concrete test cases. It prioritizes high-risk areas and ensures behavior is properly covered without executing tests or performing security reviews.

WCAG Accessibility Audit and Testing

accessibility-compliance-accessibility-audit

sickn33/agentic-awesome-skills

This expert conducts comprehensive audits to ensure digital products comply with WCAG standards. It specializes in identifying accessibility barriers, testing user experiences for users with disabilities, and providing step-by-step remediation guidance. Use it for auditing web or mobile platforms, establishing compliance, and improving overall inclusivity.

Active Directory Attack Techniques

active-directory-attacks

sickn33/agentic-awesome-skills

Comprehensive offensive techniques for Microsoft Active Directory environments including reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for authorized red team operations and penetration testing.

AI Agent Evaluation Suites

sickn33/agentic-awesome-skills

Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Covers prompt-level unit evals, tool-calling checks, end-to-end tasks, safety/adversarial tests, and LLM-as-judge scoring to gate deployments on quality.

AI Agent Behavior Evaluation

agent-evaluation

sickn33/agentic-awesome-skills

Evaluate AI agent behavior using versioned cases and explicit verifiers. This skill helps compare agent or prompt changes, reproduce failures, and run regression tests. It ensures reliability on declared task distributions without inferring product readiness from generic scores. Includes methods for uncertainty reporting and safety verification.

AI Agent Evaluation Reporting

agent-evaluation-reporting

sickn33/agentic-awesome-skills

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

Agent Harness Fault Injection

agent-harness-fault-injection

sickn33/agentic-awesome-skills

This skill enables deterministic fault injection testing for agent workflows to verify recovery mechanisms, state preservation, and safety boundaries. It generates fault matrices and event timelines to distinguish between recovered, contained, and unrecoverable failures without impacting production systems or real user data.

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