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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'.
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.
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.
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
Browser verification, proof screenshots, traces, console and network checks, and reproducible UI evidence for Workbench QA.
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.
Complete cold email system teaching research-driven personalization, "poke the bear" openers, custom signal hunting, and strict QA.
Creates test strategy and ATDD scenarios.