Requirement Ambiguity Analysis Skill: Turn Vague Wording into Closeable Questions
A bilingual guide to identifying ambiguous actors, scope, quantities, conditions, timing, states, and acceptance criteria without choosing an interpretation.
#AI Testing
Requirement Quality Review Skill: Prepare a Requirement for Design and Testing
A bilingual guide to reviewing requirement completeness, clarity, verifiability, feasibility, scope, and evidence quality before design or testing.
LLM Evaluation Design: From task sets and scoring criteria to model regression gates
Design LLM evaluations with representative task sets, calibrated rubrics, judge audits, and regression thresholds tied to real tasks.
LLM Testing: Systematically validate factual accuracy, refusal behavior, and safety boundaries
Build LLM tests for factual accuracy, citations, refusal, privacy, latency and cost; pin model settings and repeat runs to assess stochastic output.
AI-Assisted Testing: Improve testing efficiency while retaining test evidence
Assess AI-generated test ideas against real interfaces, missing boundaries, and traceable evidence before adding them to a test suite.
Prompt Testing: Systematically validate prompt accuracy, boundaries, and multi-turn consistency
Evaluate prompt changes across varied inputs and turns with pinned model settings, rubric-based scoring, boundary cases and version-regression checks.
Security Testing: Systematically identify security risk from assets, permissions, and attack surfaces
Prioritize security risks across identity, input, data, and dependency boundaries by exploitability and impact; record reproduction, fixes, and retests.
Multi-Role Quality Synthesis: Preserve different perspectives and reach actionable quality decisions
Synthesize product, QA, engineering and operations views while preserving evidence conflicts, assumptions and unresolved risks for release decisions.
Project Delivery Perspective: Include scope, dependencies, and delivery risk in quality decisions
Assess delivery readiness from scope, dependencies, milestones and evidence; make trade-offs and owners clear when plans shift.
Awesome QA Skills Update: From a QA Skill Collection to a Governed AI QA Skills Ecosystem
How Awesome QA Skills grew into a governed AI QA ecosystem spanning delivery lifecycle, CLI distribution, evaluation, routing, composition, and regression.
Technical Quality Perspective: Include architecture, reliability, and maintainability in quality decisions
Connect architecture, availability, maintainability, recovery and dependencies to operational outcomes for a grounded system quality review.
UX Quality Perspective: Bring user task flows and usability issues into quality decisions
Assess task flow, waiting, feedback, accessibility and recovery to find experience risks even when the interface reports no errors.