Loading...
Loading...
Found 6,249 Skills
Resolve implementation ambiguities before planning begins. Two modes: Discussion mode surfaces gray areas with concrete options for greenfield work. Assumptions mode reads the codebase, forms evidence-based opinions, and asks the user to correct only what's wrong (brownfield work). Use for "discuss ambiguities", "resolve gray areas", "clarify before planning", "assumptions mode", "what are the gray areas", "before we plan". Do NOT use for broad design exploration (use feature-design) or for planning itself (use feature-plan).
Generate EliteForge frontend projects with the same logic as cisdigital-generator-app. Reuse the exact project type to template mapping for frontend_app/frontend_ui/frontend_sdk, naming rules for company/product/service, and onebase-cli command assembly. Use when users ask to scaffold EliteForge frontend app projects, Vue3 component monorepo projects, JS SDK/lib projects, or request a dry-run command preview aligned with this generator. Always require user-provided required parameters and never infer missing required fields.
Configure AI Config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.
Orchestrate subagent workflows for complex tasks that benefit from decomposition, role-based delegation, and parallel execution. Use when Codex should assemble a temporary team of subagents, choose roles from a reusable role library, create a controlled fallback role when no preset role fits, coordinate read-heavy work in parallel, or handle write-heavy work with ownership boundaries, staged execution, and an integrator-led merge path.
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
Diagnose and manage Alibaba Cloud databases through natural language. Use when users need to troubleshoot database performance issues (high CPU, slow queries, abnormal connections, lock waits), check instance status, analyze disk space, optimize SQL, run health inspections, or detect security baseline violations. Supports RDS (MySQL/PostgreSQL/SQL Server), PolarDB, MongoDB, Redis (Tair), and Lindorm. Trigger this skill even for casual descriptions like "my database is slow", "can't connect to the database", "help me check this SQL", or "database disk is almost full". Also suitable for consulting Alibaba Cloud-specific database features (e.g., PolarDB Serverless, DAS autonomy capabilities) and comparing product differences (RDS vs PolarDB). Do NOT use this skill for general SQL tutorials, non-Alibaba Cloud databases, or local database administration.
Extract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (lightweight scan) or derives from an existing /understand knowledge graph.
Expert guide for Drizzle ORM best practices, including schema definitions, queries, mutations, transactions, migrations, and performance optimization. Use when working with Drizzle ORM, database schemas, queries, or migrations.
Alibaba Cloud PolarDB Database AI Assistant. For PolarDB MySQL/PostgreSQL cluster management, performance diagnostics, parameter tuning, slow SQL analysis, backup recovery, connection session analysis, primary-standby switchover diagnostics, security configuration audit, and other O&M operations. Use when user questions involve PolarDB, cluster IDs starting with pc-, kernel parameters, primary-standby switchover, IMCI columnar storage, etc.
Progressively gather requirements through automated codebase discovery and yes/no questions, then generate a comprehensive requirements spec. Use when starting a new feature, planning a build, or when you need structured requirements before implementation.
Systematic evidence-based debugging using runtime logs. Generates hypotheses, instruments code with NDJSON logs, guides reproduction, analyzes log evidence, and iterates until root cause is proven with cited log lines. Use when the user reports a bug, unexpected behavior, or asks to debug an issue.
This skill should be used when a developer is ready to implement a GitHub Task issue and needs to read the full spec hierarchy (Task + Feature + Epic), explore the codebase, produce a concrete Technical Approach with real file paths, and drive TDD implementation against Gherkin scenarios. Triggers on phrases like "implement task