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research

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

Quellinformationen

Repository
griddynamics/rosetta
Letzte Quellaktivität
15. September 2026 um 01:56
Erkannte Sprache von SKILL.md
Englisch
Sterne
354
Forks
79

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
research
description
To run systematic deep research via meta-prompting — grounded references, incremental tracking, self-validation.
license
Apache-2.0
argument-hint
feature, request, scope?
baseSchema
docs/schemas/skill.md
<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> Systematic research: grounded references, multiple-options analysis, self-validation. Skip: simple lookups, single-source questions. </when_to_use_skill> <core_concepts> - All Rosetta prep steps MUST be FULLY completed, load-project-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 references, but call this out EXPLICITLY! - Search documentation for libraries, versions, and issues not in built-in knowledge - MUST be cautious of LLM context: use grep, search, and similar techniques and tools - Ask user questions during research to resolve unknowns and validate direction - Spawn parallel subagents to go over individual ideas or areas - Use synthesis and comparison approach Enforcement rules for the generated research prompt: 1. MUST use todo tasks 2. MUST use DeepWiki and Context7 3. MUST create and update state md file after each task 4. MUST output result file section by section as soon as each section becomes available 5. MUST think about and align consequences and consequences of consequences to prevent oversight (example: doing X leads to Y, which affects Z, thus it should be done ABC way) </process> </research>
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