write-connector

Adicionar um novo conector de fonte OpenWiki integrado. Use quando um usuário pedir para criar ou implementar um conector OpenWiki.

npx skills add https://github.com/langchain-ai/openwiki --skill write-connector

Write An OpenWiki Connector

OpenWiki connectors are built-in TypeScript modules in the OSS repository. Do not create a plugin marketplace, dynamic connector package, or runtime-loaded untrusted connector. Add normal source files and tests.

Prefer Custom MCP For Arbitrary Servers

If the knowledge source already exposes a read-only MCP server (HTTP or stdio), use the built-in custom-mcp connector instead of adding a new ConnectorId:

  • Configure ~/.openwiki/connectors/custom-mcp/config.json with enabled, transport, optional allowedTools, and optional readOnlyOperations.
  • Put secrets in ~/.openwiki/.env and reference them as ${ENV_NAME} in transport headers/env.
  • Agent tools openwiki_list_mcp_tools / openwiki_call_mcp_tool accept custom-mcp.

Add a dedicated built-in connector only when you need provider-specific auth, scoping UI, or deterministic API pulls that MCP cannot express.

Required Shape

  • Add the connector to src/connectors/types.ts and src/connectors/registry.ts.
  • Implement the connector under src/connectors/sources/.ts.
  • The connector must expose a ConnectorRuntime with id, displayName, description, backend, requiredEnv, supportsAgenticDiscovery, and ingest().
  • Ingestion writes raw JSON/manifests under ~/.openwiki/connectors//raw//.
  • State lives in ~/.openwiki/connectors//state.json.
  • Config lives in ~/.openwiki/connectors//config.json.
  • Secrets live in ~/.openwiki/.env and are referenced only by env var name.

Security Rules

  • Never read, print, log, return, or hardcode secret values.
  • Do not store credentials in connector config, raw files, state, logs, or tests.
  • Validate connector IDs and raw file paths so reads and writes stay inside ~/.openwiki/connectors//.
  • Use deterministic ingestion code for credentialed external fetching.
  • If wrapping MCP, treat the MCP server as read-only and call only allowlisted read/dump operations from connector config.
  • Do not let untrusted connector manifests instantiate arbitrary commands or arbitrary network endpoints without explicit built-in code review.
  • For custom-mcp, users configure a reviewed built-in wrapper; still require allowedTools and/or MCP readOnlyHint before agentic tool calls (no mutating-tool heuristics beyond Notion's hosted endpoint).

Ingestion Rules

  • Git/local repos should write compact manifests and let the agent inspect the local repo as the source of truth.
  • Sources with timestamps should store per-stream cursors.
  • Sources with object metadata should store IDs, last edited timestamps, and content hashes.
  • Sources with pagination should store enough state to continue without refetching everything.
  • Raw dumps should preserve source IDs, timestamps, URLs, authors, and enough provenance for citations.

User-Facing Finish

When done, tell the user:

  • which connector files changed,
  • which env vars to set in ~/.openwiki/.env,
  • what config file to create or edit,
  • how to run openwiki personal --update to trigger ingestion,
  • which scopes/permissions the source provider requires.

Mais skills de langchain-ai

deepagents-thread-inspector
langchain-ai
Inspecione e explique conversas no armazenamento de sessões SQLite local do Deep Agents Code. Use como fallback quando a ferramenta de rastreamento LangSmith não estiver disponível, para…
deepagents-python-quickstart
langchain-ai
Estruture um Deep Agent local mínimo em Python seguindo o quickstart oficial, usando busca web nativa do provedor em vez de Tavily. Use quando o usuário quiser…
deepagents-typescript-quickstart
langchain-ai
Estruture um Deep Agent local mínimo em TypeScript seguindo o quickstart oficial, usando busca web nativa do provedor em vez de Tavily. Use quando o usuário…
eval-engineering
langchain-ai
Inspecione iterativamente um repositório de agente e traces opcionais fornecidos pelo usuário, entreviste o usuário e crie, execute e audite evals do Harbor um por vez. Use para…
LangChain RAG Pipeline
langchain-ai
INVOQUE ESTA HABILIDADE ao construir QUALQUER sistema de geração aumentada por recuperação (RAG). Abrange carregadores de documentos, RecursiveCharacterTextSplitter, embeddings (OpenAI),…
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — uma skill instalável para agentes de IA, publicada por langchain-ai/langchain-skills.
LangSmith Datasets
langchain-ai
INVOQUE ESTA HABILIDADE ao criar conjuntos de dados de avaliação a partir de rastreamento OU ao fazer upload de conjuntos de dados para o LangSmith OU ao consultar conjuntos de dados. Abrange tipos de conjuntos de dados (final_response,…
langsmith-evaluator
langchain-ai
INVOQUE ESTA HABILIDADE ao construir pipelines de avaliação para LangSmith. Abrange três componentes principais: (1) Criação de Avaliadores - LLM como Juiz, código personalizado; (2)…