Results for “langbase”

Codebase Onboarding

Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors, with fast fact-gathering and repeatable outputs.

CloudBase is a full-stack development and deployment toolkit for building and launching websites, Web apps, 微信小程序 (WeChat Mini Programs), and mobile apps with backend, database, hosting, cloud functions, storage, AI capabilities, Agent, and UI guidance. This skill should be used when users ask to develop, build, create, scaffold, deploy, publish, host, launch, go live, migrate, or optimize websites, Web apps, landing pages, dashboards, admin systems, e-commerce sites, 微信小程序 (WeChat Mini Programs), 小程序, Agent, 智能体, uni-app, or native/mobile apps with CloudBase (腾讯云开发, 云开发), including authentication, login, database, NoSQL, MySQL, cloud functions, CloudRun, storage, AI models, and UI guidance, or when they ask to compare CloudBase with Supabase or migrate from Supabase to CloudBase.

Codebase Onboarding

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Maps, documents, and onboards into an existing codebase by generating seven structured documents covering stack, structure, architecture, conventions, integrations, testing, and concerns.

Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector).

LLM observability with Langfuse — tracing, evals, prompt management, cost tracking

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debug...

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Build and organize knowledge bases, help centers, FAQ systems, and troubleshooting guides optimized for self-service support and search. Use when creating a structured support documentation system. Also trigger for 'knowledge base', 'help center', 'FAQ', 'troubleshooting guide', 'support docs', 'self-service support', 'help article', or 'support article'.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Instrument LLM applications with Langfuse for tracing, prompt management, evaluation, and cost tracking, including integrations with OpenAI, LangChain, and LlamaIndex.

Use when building stateful multi-step agents, agent graphs, or workflows with LLMs. Triggers on: 'langgraph', 'state graph', 'stateful agent', 'agent workflow', 'agent loop', 'multi-step agent', 'persistent agent', 'human-in-the-loop agent', 'agent with memory', 'graph-based agent'.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Navigate and understand codebases using agentlens hierarchical documentation. Use when exploring new projects, finding modules, locating symbols in large files, finding TODOs/warnings, or understanding code structure.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debuggin

Load this skill when the user says "prepare base", "prepare-base", or "/prepare-base". Prunes remote tracking references, checks out a base branch, pulls latest, and cleans up local-only feature branches after confirmation.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Provides expertise in Langfuse for LLM observability, including tracing, prompt management, evaluation, and integration with LangChain, LlamaIndex, and OpenAI.

Uso do SDK JavaScript para aplicações cliente PocketBase. Use ao chamar PocketBase a partir do frontend ou Node.js, autenticar usuários, subscrever a eventos em tempo real, fazer upload de arquivos, ou trabalhar com o SDK PocketBase JS/TS. Cobre operações CRUD, fluxos de autenticação, authStore, SSE em tempo real, manipulação de arquivos, operações em lote e sintaxe de query.

Sets up a Supabase MCP server for managing Postgres databases through a RESTful API, including fetching tables, adding roles, and running queries.

Audit a repository, vet and prioritize evidence-backed findings, write drift-aware implementation plans, delegate bounded work, and independently verify results. Use when asked to improve, audit, modernize, harden, review, plan fixes for, or systematically reduce risk in a codebase. Use native isolated agents for implementation; use OpenRouter/free only for narrow advisory audit slices under its safety limits.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Expert skill for building LLM applications with LangChain — LCEL chains, RAG pipelines, agent orchestration, LangGraph integration, LangSmith observability, and production deployment via LangServe. Use when working with LangChain or comparing LLM application frameworks.

Run large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of files.

Coordinate large-scale codebase migrations and multi-file refactors across hundreds of files using batched PRs and CI verification.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Analyze repositories for lines of code, language breakdown, file counts, and code-vs-comment ratios using pygount, with guidance on using language servers for symbol navigation.