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Found 484 Skills
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)
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.
This skill should be used when a QA engineer wants to test or verify a completed task, run through acceptance criteria, check Gherkin scenarios against the implementation, record pass/fail results, or sign off on a ticket before merge. Triggers on phrases like "verify task
Convin platform help — AI-powered contact center QA, coaching, and conversation intelligence. Use when setting up Convin automated QA scoring, Convin Real-Time Assist not surfacing prompts, Convin transcription missing speakers or inaccurate with accents, Convin audits hanging or calls delayed on dashboard, Convin AI Phone Call agent for outbound, Convin LMS agent training, or evaluating Convin vs Observe.AI vs Cresta vs Balto vs Enthu.AI for contact center QA. Do NOT use for CCaaS platform selection (use /sales-ccaas-selection) or building a coaching program (use /sales-coaching).
Use when requirements are testable enough and you need to define QA scope, coverage priorities, test strategy, suite intent, test data needs, environment dependencies, and risk-based execution focus. 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.