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Learning Resource Curator

Example prompt: "Find the 15 best free resources for learning Python data analysis — tutorials, videos, and articles. Rate each by difficulty and compile them into a Notion reading list."

How to automate learning resource curation with GloriaMundo

The Problem

When you want to learn a new skill or put together a reading list for students, the research phase is surprisingly time-consuming. You open dozens of browser tabs, skim articles to judge quality, check whether content is up to date, and try to arrange resources in a sensible learning order. For a topic like "Python data analysis," a thorough search and curation effort can easily take 2-3 hours, and the result is often a loose collection of bookmarks with no structure or difficulty rating. Repeating this for multiple topics across a curriculum multiplies the effort considerably.

How GloriaMundo Solves It

We build a workflow that takes a topic and resource count as input. A web search step finds relevant tutorials, articles, video courses, and documentation across the web. A sub-agent evaluates each candidate resource — checking whether it is free, assessing its depth and recency, and assigning a difficulty level (beginner, intermediate, or advanced). An LLM step then ranks the resources, removes duplicates, and organises them into a structured reading list with a brief description of each. Finally, an integration step creates the curated list in Notion as a database, with columns for title, URL, format (article, video, tutorial), difficulty, and a one-sentence summary. Glass Box preview lets you review the full reading list and adjust rankings or remove entries before anything is saved.

Example Workflow Steps

  1. Trigger (manual): You provide the topic, desired number of resources, and any preferences (e.g. "free only," "beginner-friendly").
  2. Step 1 (web_search): Search the web for tutorials, articles, videos, and courses on the specified topic.
  3. Step 2 (sub_agent): For each candidate resource, fetch the page, assess quality and recency, determine difficulty level, and confirm it matches the criteria.
  4. Step 3 (LLM): Rank the vetted resources, remove duplicates, and organise them into a structured reading list with descriptions and difficulty ratings.
  5. Step 4 (integration): Create a Notion database with the curated reading list — one row per resource with title, URL, format, difficulty, and summary.

Integrations Used

  • Notion — destination for the curated reading list, structured as a filterable database
  • Google Docs — optional alternative destination if you prefer a formatted document

Who This Is For

Educators building course reading lists, self-learners mapping out a study plan for a new skill, and team leads compiling onboarding resources for new hires who need to get up to speed on a technical topic.

Time & Cost Saved

Manually researching and curating 15 quality resources on a topic takes roughly 2-3 hours of searching, reading, and organising. This workflow reduces that to about 15-20 minutes of reviewing the curated list in the Glass Box preview. For an instructor preparing reading lists across 5-10 course topics, that represents 10-25 hours saved. The workflow uses web search, sub-agent, LLM, and integration steps, costing a moderate number of credits per run.