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Skill

/research

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

From plugin
rosetta
330200 skills24 agents63 commands
Install
$ npx -y skills add griddynamics/rosetta --skill research --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/research

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

research.SKILL.md
name: research
description: "To run systematic deep research via meta-prompting — grounded references, incremental tracking, self-validation."

<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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