Use cases · fetch
Fetch Document
Fetching documents programmatically is a common need when you want to ingest content from the web or internal sources into an AI agent. Instead of manually downloading files, an agent can retrieve documents on demand, parse them, and hand off structured data for further processing. This is especially useful for research, data pipelines, or knowledge base updates. Below are 2 skills we evaluated for this task.
2 skills for this task
qmd
Fast local search for markdown files, notes, and docs using qmd CLI. Combines BM25 full-text search, vector semantic search, and LLM reranking — all running locally. No API keys needed.
hm-fetch-skill
Fetch Huawei HarmonyOS developer document正文 by calling documentPortal getDocumentById API with slug objectId, convert HTML content to Markdown, and maintain local-first migration references.
Common questions
- How can I fetch a PDF using an AI agent?
- Use a skill that accepts a URL and returns the document content. The agent can then extract text or metadata from the PDF for downstream tasks.
- What if the document is behind a login?
- Some skills support authentication headers or cookies. You'll need to configure the skill with your credentials or session token to access protected resources.
- Can I fetch multiple documents at once?
- Yes, you can loop through URLs in a list and call the fetch skill for each. Some skills also support batch processing with a single request.