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Automated deep research tool for thorough topic investigation
Passed 17 of 24 test cases with the skill, and 14 of 24 without it.
and 2 more in the full report
3 fixed14 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
When initiating an automated deep literature investigation into room-temperature superconductivity in hydride compounds, should I immediately execute search queries for all downloadable papers, or is there a preliminary setup phase?
| Checked | Without skill | With skill |
|---|---|---|
| Decomposes the main question into core concepts and formulates 5 to 10 seed questions covering topic breadth before running full searches. | Missed | Passed |
| Establishes depth targets for each subtopic prior to in-depth searching. | Missed | Passed |
The instructions your model reads when the skill loads.
A skill for conducting automated, in-depth research investigations that go beyond surface-level searches to produce comprehensive, well-sourced reports on any academic topic. Based on Auto-Deep-Research (1K stars), this skill implements iterative search-analyze-refine cycles that progressively deepen understanding of a research topic.
Deep research differs from simple literature search in its depth and synthesis. Rather than returning a list of papers, deep research produces a structured understanding of a topic: its history, current state, key debates, methodological approaches, open questions, and future directions. This skill automates the iterative process that expert researchers perform manually, cycling through search, reading, analysis, and question refinement until a satisfactory depth of understanding is achieved.
The approach is particularly valuable for researchers entering a new field, preparing comprehensive literature reviews, writing grant proposals that require thorough background knowledge, or advising students on topics adjacent to their own expertise.
The automated deep research process follows a structured methodology:
Phase 1: Topic Decomposition
Phase 2: Breadth-First Exploration
Phase 3: Depth-First Investigation
Phase 4: Iterative Refinement
test cases passed, vs 14 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.