Project Delivery Perspective: Include scope, dependencies, and delivery risk in quality decisions
Project Delivery Perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Blog
Awesome QA Skills Update: From a QA Skill Collection to a Governed AI QA Skills Ecosystem
A look at how Awesome QA Skills has evolved from a QA Skill collection into a broader system covering the software delivery lifecycle, AI Native QA, Skills CLI distribution, Skill Eval, routing, composition, regression, and governance.
Technical Quality Perspective: Include architecture, reliability, and maintainability in quality decisions
Technical quality perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
UX Quality Perspective: Bring user task flows and usability issues into quality decisions
UX Quality Perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
dsh-qa v0.5.3: From Harness Compatibility to a Native QA Workbench
A release-series overview of dsh-qa from v0.4.1 to v0.5.3: Harness contracts, native Panels, Profile Bundles, version awareness, and the Quiet Studio Workbench.
Product Quality Perspective: Bring user value and business impact into quality decisions
Product Quality Perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
QA Quality Perspective: Use coverage, risk, and evidence to support quality conclusions
QA quality perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Production Incident Analysis: From incident timeline to containment, validation, and retrospective actions
Production Incident Analysis field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
Production Verification: Verify business flows, metrics, logs, and rollback conditions after release
Production Verification field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
Root Cause Analysis: Move from symptoms to hypotheses and find a verifiable root cause
Root Cause Analysis field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
Distributed Trace Analysis: Use a trace to locate cross-service latency and error propagation
Distributed Trace Analysis field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
Log Analysis: Cluster errors at scale and locate post-release issues
Log Analysis field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.