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Found 6,914 Skills
Validate database integrity, test migrations forward and backward, verify schema constraints, manage seed data, detect migration drift, and identify query performance issues. Covers PostgreSQL, MySQL, MongoDB with Prisma, TypeORM, Drizzle, and SQLAlchemy, plus Testcontainers test databases. Use when: "database test," "migration test," "migration rollback," "rollback test," "data integrity," "SQL test," "schema validation," "seed data," "query performance," "Testcontainers." Not for: synthetic data generation/masking at scale — use test-data-management; Docker/IaC test-environment provisioning — use test-environments; SQL injection — use security-testing. Related: test-data-management, test-environments, security-testing, ci-cd-integration.
Provision, connect, and operate Sealos Cloud databases through sealos-cli for local development, Devbox development, and app setup. Use when the user needs a cloud database for a project, asks to create or connect PostgreSQL/MySQL/MongoDB/Redis or another Sealos database, wants DATABASE_URL or similar env vars wired into a dev environment, needs database connection details, backups, logs, public access, or wants to replace local Docker Compose databases with a managed Sealos database.
Load for any work involving Baseten - deploying/operating models on Dedicated Inference (Truss, custom Docker servers, TRT-LLM engines, Chains), calling hosted Model APIs, running Training jobs (SFT/RL/LoRA), or Model Frontier Gateway.
Consolidate Claude Code memory across git worktrees so all branches of the same repo share a single memory directory. Use when memory is isolated per worktree, when switching branches loses context, or when setting up a new machine with worktree-based workflows.
Maps the directory structure of the project to help the AI understand the codebase layout.
Use this skill when designing database schemas for relational (SQL) or document (NoSQL) databases. Provides normalization guidelines, indexing strategies, migration patterns, and performance optimization techniques. Ensures scalable, maintainable, and performant data models.
Advanced database design and administration for PostgreSQL, MongoDB, and Redis. Use when designing schemas, optimizing queries, managing database performance, or implementing data patterns.
Use when entering an unfamiliar codebase, onboarding to a new project, or wanting to assess codebase health before reading code — analyzes git history to reveal hotspots, risk areas, team structure, and development momentum
Use when the user asks to "map what our surfaces say today", "inventory our current messaging", or "find the gap between what we say and what we mean"; produces the narrative baseline — a surface-by-surface inventory of what every owned touchpoint (homepage, pricing, docs, decks, social bios, email footers) claims RIGHT NOW, each line labeled Measured / User-provided / Estimated, plus a per-surface gap read vs the intended message and the drift-baseline snapshot the Evaluate phase measures future drift against. Not for authoring the canon — use message-system-architect; not for scoring the surfaces or running the vetoes — use narrative-quality-auditor. 现状叙事盘点/各触点口径/意图差距/漂移基线
One API and one credential for frontier and open-source LLMs, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Meta, Alibaba, DeepSeek), or avoid juggling separate provider API keys and accounts — especially when they already use Neon and want AI requests to branch with their project. Works with the OpenAI SDK, Anthropic SDK, google-genai, the Vercel AI SDK, and Mastra by changing only the base URL. Triggers include "call an LLM", "add AI to my app", "chat completion", "model routing", "LLM proxy/gateway", "one API for all models", "use Claude/GPT/Gemini", "AI SDK", "Mastra agent", "Neon AI Gateway", and "log/rate-limit AI calls".
Expert in Supabase architecture, SQL optimization (PostgreSQL), and backend security (RLS) for real-time tracking systems.
Ingest a codebase into memory as code pages connected by wiki links. Walks the tree, classifies each code file by role, applies a role template, and writes a code page per meaningful unit under <wiki root>/code/. Resumable: skips files already ingested and current. Not for prose documents; use /loam::adding-to-memory for those.