deep-research

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Research the problem domain before coding. Web search for techniques, save raw sources, write structured findings, update the index.

AI & Automation 1,019 stars 128 forks Updated 2 weeks ago Apache-2.0

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# Deep Research Research the problem thoroughly before writing code. Understand what's known, what's been tried, and what approaches exist. ## When to Use - Starting a new task or problem - Stuck after multiple evals without improvement - Pivoting to a fundamentally different approach - The problem involves domain-specific knowledge you're unfamiliar with ## Notes Directory Structure ``` notes/ ├── index.md ← table of contents for research/ and experiments/ ├── raw/ ← saved web pages, paper excerpts (immutable, never edit) ├── research/ ← your synthesized findings (link back to raw/) │ └── _coverage.md ← the research coverage ledger (dimensions × covered/partial/missing) └── experiments/ ← eval reflections and results (written by reflect heartbeat) ``` ## Process ### 1. Understand the Problem — and Map the Research Space Read the task description and key files. Identify what's being optimized, what the constraints are, and what makes it hard. Check `coral log` and `{shared_dir}/notes/` for prior work. Then **decompose the problem into 4–8 research dimensions** — the distinct things a team would need to understand to win *this* task. Derive them from the task, don't pull them from a fixed list. Useful starting prompts (not a required set): *prior art / SOTA methods*, *mechanism or theory*, *implementation / libraries*, *the evaluation & grader surface*, *failure modes*, *adjacent fields*. Drop the ones that don...

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Author
Human-Agent-Society
Repository
Human-Agent-Society/CORAL
Created
6 months ago
Last Updated
2 weeks ago
Language
Python
License
Apache-2.0

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