Results for “test-decay-risks”

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Audits test code in any language using the academic testsmells.org 19-smell catalog, producing a severity-ranked report with specific locations and actionable fixes.

A flaky test is one that produces different results on unchanged code and an

Identify what to measure, test, or track to de-risk a product or AI idea across internal and external risk dimensions.

Debugging is the scientific method under time pressure.

Guides adding characterization tests before modifying or refactoring untested legacy code, using the RGR workflow and strangler fig pattern to reduce risk.

Audits Rust codebases for safe/unsafe boundary issues, memory safety in unsafe blocks, concurrency hazards, panic-induced DoS, FFI safety, and async runtime mistakes.

Use when planning how to test any deliverable or system. This skill provides a framework for selecting the right testing approach based on what is being delivered, ensuring comprehensive coverage without unnecessary overhead.

Provides a structured triage process for debugging test failures, build errors, runtime bugs, and production incidents, including steps to reproduce, localize, fix, and guard against recurrence.

Generate tests, design test strategies, and ensure code quality through testing. Use when writing tests, improving test coverage, designing test infrastructure, or evaluating test quality.

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates.

Simulates BGP hijacking attacks in isolated lab environments to test RPKI deployment, route origin validation, and BGP monitoring defenses against prefix hijacking and route leak attacks.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Checklist of empirical robustness tests for finance/economics papers

Rehearse

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Guides systematic root-cause investigation before proposing fixes for any technical issue, with a four-phase process and red flags to avoid guessing.

Provides expert guidance for systematic backtesting of trading strategies, covering methodology, stress testing, bias prevention, and result interpretation.

Delivers a daily DeFi regime verdict (RISK-ON/NEUTRAL/RISK-OFF) from five named inputs, top TVL movers with causal reasoning, and a sustainable-vs-incentive yield split to distinguish real product-market fit from emissions-pumped APY.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Run mutation testing against a project's test suite to verify the tests actually catch failures when source code is broken. Use after major test changes, before declaring a suite "good", or when coverage % looks healthy but you suspect tests aren't catching real bugs.

Reproduces failures, gathers evidence, tests hypotheses, fixes root causes, and adds regression coverage.