Loading...
Loading...
Found 495 Skills
Run UX walkthroughs and QA sweeps on live web apps using browser automation. Walks through apps as a real user, flags friction points and usability issues, tests CRUD operations, and produces ranked audit reports. Trigger with 'ux audit', 'ux walkthrough', 'qa test', 'test the app', or 'check all pages'.
Generates structured Given/When/Then acceptance criteria for a user story or feature slice. Use when translating product requirements into testable scenarios that cover the happy path, edge cases, error states, and non-functional expectations for engineering handoff and QA.
Qt Model/View architecture — QAbstractItemModel, table/list/tree views, item delegates, and proxy models. Use when displaying tabular data, building a list with custom items, implementing a tree, creating a sortable/filterable table, or writing a custom item delegate. Trigger phrases: "QAbstractItemModel", "table view", "list model", "QTableView", "QListView", "tree view", "item delegate", "sort table", "filter model", "QSortFilterProxyModel", "custom model", "model data"
Operating system for intake, approvals, QA, and training across brand stakeholders.
Use when QA scope and strategy are defined and you need to generate detailed, executable test cases plus smoke, regression, and user acceptance suites tied to requirements, roles, workflows, and risks. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Enrich a Phase Sepc/PRD with Quality Requirements (Q-nnn) and Acceptance Criteria (AC-nnnn). Use when user wants to add QA perspective, define test criteria, identify non-functional requirements, add verification steps, or prepare a Phase PRD for test planning.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)
Create comprehensive test scenarios from user stories with test objectives, starting conditions, user roles, step-by-step actions, and expected outcomes. Use when writing QA test cases, creating test plans, defining acceptance tests, or preparing for feature validation.
You are **EvidenceQA**, a skeptical QA specialist who requires visual proof for everything. You have persistent memory and HATE fantasy reporting.
Test coverage, code quality, defect metrics, and QA KPIs
Enables continuous self-improvement through learning from failures, user corrections, and capability gaps. Integrates with QAVR for learned memory ranking.