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Found 77 Skills
Guide for Convex backend development fundamentals including function types (queries, mutations, actions), layered architecture, HTTP actions, and the core mental model. Use when building Convex backends, creating queries/mutations/actions, implementing HTTP webhooks, or understanding Convex's reactive data model. Activates for Convex project setup, function definition, API design, or backend architecture tasks.
Use when conducting comprehensive code review for pull requests across multiple quality dimensions. Orchestrates 12-15 specialized reviewer agents across 4 phases using star topology coordination. Covers automated checks, parallel specialized reviews (quality, security, performance, architecture, documentation), integration analysis, and final merge recommendation in a 4-hour workflow.
Complete Convex development mastery — functions (queries, mutations, actions, HTTP actions), schema design, index optimization, argument/return validation, authentication, security patterns, error handling, file storage, scheduling, crons, aggregates, OCC handling, denormalization, TypeScript best practices, and production-ready code organization. The definitive Convex skill. Use when building any Convex backend: writing functions, designing schemas, optimizing queries, handling auth, adding real-time features, setting up webhooks, scheduling jobs, managing file uploads, or reviewing/fixing Convex code. Triggers on: convex, query, mutation, action, ctx.db, defineSchema, defineTable, v.id, v.string, v.object, withIndex, ConvexError, internalMutation, httpAction, ctx.scheduler, ctx.storage, OCC, convex best practices, convex functions, convex schema, convex performance, "how do I do X in Convex".
Route Alibaba Cloud Model Studio requests to the right local skill (Qwen Image, Qwen Image Edit, Wan Video, Wan R2V, Qwen TTS and advanced TTS variants). Use when the user asks for Model Studio without specifying a capability.
Generate tiered knowledge-verification questions (quiz/exam) at 3 difficulty levels with grading and diagnostics. For testing UNDERSTANDING of code, concepts, or architecture — NOT for writing software tests (use engineering:testing-strategy for that). Triggers on "문제 만들어", "quiz", "검증 문제", "이해도 확인", "knowledge check", "challenge me", "시험 문제", "면접 문제".
Guides creation and modification of domain feature systems organized under a systems/ directory. Covers directory layout, API service layer patterns, TanStack Query hooks (queries, mutations, optimistic updates), React context and XState store conventions, hook organization, and public API barrel exports. Use when adding a new domain system, extending an existing one, or fixing bugs in a system-layer codebase. Don't use for generic React component work, backend API implementation, or codebases not organized around a systems/ domain pattern.
Generates structured literature survey reports from collected papers using a multi-stage pipeline: outline generation (query-type adaptive) → draft survey → section-by-section expansion → summary section refinement → final assembly. Produces survey-grade output with taxonomy-based method analysis, LaTeX formalizations, comparative tables, and dense citations. Use when: user wants a literature review, research survey, field overview, or systematic synthesis of multiple papers. Do NOT use for finding/searching papers (use paper-navigator), generating research ideas (use research-ideation), or writing a paper's Related Work section (use paper-writing).
Verint Open Platform help — enterprise CX automation with Da Vinci AI bots (Quality Bot 100% QA, Coaching Bot real-time guidance, Wrap Up Bot auto-summaries, CX/EX Scoring, TimeFlex agent scheduling, Exact Transcription 80+ languages), WFM forecasting/scheduling/adherence, knowledge automation, IVA virtual assistants, speech/text analytics, financial compliance, Verint Marketplace 350+ listings. Use when Verint reports loading slowly or showing inconsistent data, Quality Bot not scoring interactions correctly, Coaching Bot recommendations irrelevant, WFM forecasts off vs actual volume, Verint API integration or developer portal questions, comparing Verint vs NICE vs Genesys WEM capabilities, or connecting Verint to your CCaaS or CRM. Do NOT use for choosing between CCaaS platforms (use /sales-ccaas-selection) or for QA tool comparison across vendors (use /sales-coaching).
Analyze, prioritize, and document test cases in TMS (Jira/Xray) -- the bridge between manual QA and test automation. Use when creating Test/ATP/ATR artifacts, calculating ROI to choose which tests to automate, maintaining US-ATP-ATR-TC traceability, or repairing broken TMS links. Supports four scopes: module-driven (exhaustive module exploration), ticket-driven (QA-approved user story), bug-driven (regression TC for a closed bug), and ad-hoc/exploratory. Produces three outcomes per TC: Candidate (feeds test-automation), Manual (terminal), Deferred (terminal). Triggers on: document tests, create test cases in Jira/Xray, prioritize for automation, ROI analysis, which tests to automate, Candidate vs Manual, link ATP to ATR, fix TMS traceability, stage 4, turn this bug into a regression test. Do NOT use for writing test code (test-automation) or running suites (regression-testing).
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
Coordinates 3 specialized audit workers (query efficiency, transaction correctness, runtime performance). Researches DB/ORM/async best practices, delegates parallel audits, aggregates results into single Linear task in Epic 0.
Comprehensive academic writing skill for drafting journal-ready manuscripts. Orchestrates specialized sub-skills for introduction sections (q-intro), descriptive analysis (q-descriptive-analysis), methods sections (q-methods), and results sections (q-results). Use when the user needs end-to-end support for academic manuscript preparation, from initial data exploration through publication-ready prose. Follows APA 7th edition formatting standards.