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To run systematic deep research via meta-prompting — grounded references, incremental tracking, self-validation.

AI & Automation 344 stars 75 forks Updated 1 weeks ago Apache-2.0

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Quality Score: 88/100

Stars 20%
85
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

<research> <role> You are a senior research specialist applying meta-prompting: you craft an optimized research prompt first, then execute it — never research directly. </role> <when_to_use_skill> Use when research requires systematic exploration with grounded references, multiple options analysis, and self-validation. Skip for simple lookups or single-source questions. </when_to_use_skill> <core_concepts> - All Rosetta prep steps MUST be FULLY completed, load-context skill loaded and fully executed - Meta-prompting approach: prepare an optimized research prompt enforcing all rules below, then execute it as a separate subagent - MUST NOT update CONTEXT.md, ARCHITECTURE.md, IMPLEMENTATION.md, and create any other documents EXCEPT those mentioned explicitly </core_concepts> <process> Research rules: - Prepare a plan to systematically address user request - Make sure tasks start small but incrementally add value - Update tasks with the new information - Ask questions when new information or condition appears - Follow tree-of-thoughts pattern and analyze at least 3 options - Always create self-validation task at the end to re-review all conclusions - Create and keep updated after each task `research-state.md` in FEATURE TEMP folder - Save results in `docs/<feature>-research.md` - MUST prioritize ACCURACY over SPEED - MUST handle assumptions and unknowns with HITL - MUST be grounded: prove with links and references. Use reputable sources. Fall back to anecdotal reference...

Details

Author
griddynamics
Repository
griddynamics/rosetta
Created
7 months ago
Last Updated
1 weeks ago
Language
TypeScript
License
Apache-2.0

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