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Found 15 Skills
Expert-level quality assurance, testing strategies, automation, and QA processes
Orchestrate the public UGC creative production line from product or research evidence through creator logic, storyboard, image/video/audio generation handoffs, montage planning, and QA. Use this when the user asks for a repeatable UGC-style ad or creator video workflow rather than one isolated media asset.
Use this skill when you need to review test cases for completeness, clarity, maintainability, and missing scenarios; triggers include 'test case review' and 'test case review'.
Review an implemented user story or task (via GitHub Pull Request) for completeness, test coverage, and code quality. Use this when asked to QA, review a PR, verify implementation, or as a follow-up to the user-story-implementer skill.
AI SDLC test-case-driven testing workflow. Use when an AI assistant is asked to derive test cases, create a test plan, expand coverage, or write tests from explicit scenarios before implementing unit, service, transport, or integration tests. 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.
Apply Traceknot's ISTQB-aligned, evidence-bound QA process to repository changes across OMP, Codex, GajaeCode, Claude Code, and OpenCode. Use for implementation verification, bug fixes, release checks, repository audits, defect confirmation, and residual-risk decisions without treating an agent's own completion claim as proof.
This skill should be used when a developer or QA engineer wants to report a bug, create a bug ticket, document a test failure, log a defect, file an issue found during a QA session, or report something that is broken — for example "report a bug", "create a bug ticket", "I found a defect", "something is broken in task
Resolve ambiguities in spec.md through targeted Q&A before planning
Conversational bug discovery → issue draft. Light listening, background exploration, scope assessment. Asks before gh issue create — never auto-files. Use when conducting a QA session, triaging user-reported issues, or filing bugs.
Browser verification, proof screenshots, traces, console and network checks, and reproducible UI evidence for Workbench QA.
Complete cold email system teaching research-driven personalization, "poke the bear" openers, custom signal hunting, and strict QA.
Creates test strategy and ATDD scenarios.