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Found 198 Skills
Use this skill when you need to create high-quality test cases with normal, exception, and boundary scenarios; triggers include test case writing and test design.
Checks email quality before sending via Mailgun Inspect API. Use when previewing emails across clients, checking accessibility (WCAG), validating links, validating images, or analyzing email HTML/CSS compatibility.
Evaluate the output of a journey-builder run, identify instruction gaps, and edit the project root AGENTS.md (or add pitfalls to the gist) to fix those gaps. Does NOT modify the journey-builder skill itself.
QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear
Structured visual QA verdict for screenshot-to-reference comparisons
Ann — Master Orchestrator for MEL/SRHR work. Use when Ane brings any analytical, evaluation, SRHR, or structured-output task. Ann classifies task complexity, queries the MEL Wiki, retrieves knowledge, creates an implementation plan (verifies with user for complex tasks), delegates to Vi for execution, runs a 5-point quality gate, and delivers. General-purpose — not tied to any specific project.
Check BIM model consistency: naming conventions, parameter completeness, spatial relationships, and data integrity across model elements.
Perform SGLang code review in the style of human maintainers by consulting the 2024-2025 non-agent PR review corpus, including inline code snippets, original multilingual comments, and discussion threads. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Four LLM reviewers challenge changes from independent angles.
Invoke when the user asks to review, check, audit, or look over Qt6 C++ code — or suggest before committing. Runs deterministic linting (60+ rules) then six parallel deep- analysis agents covering model contracts, ownership, threading, API correctness, error handling, and performance. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only — never modifies code.
PR review with parallel specialized agents. Use when reviewing pull requests or code.