/research-flow
Workflow for deep project research with grounded references, parallel exploration, etc.
$ npx -y skills add griddynamics/rosetta --skill research-flow --agent claude-codeHow 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-flow
Context preview
The summary Claude sees to decide when to auto-load this skill.
Workflow for deep project research with grounded references, parallel exploration, etc.
SKILL.md
research-flow.SKILL.mdname: research-flow
description: "Workflow for deep project research with grounded references, parallel exploration, etc."
<research_flow>
<description_and_purpose> Orchestrates deep research via meta-prompting: craft an optimized research prompt, then execute it in a dedicated subagent. </description_and_purpose>
<workflow_phases>
<prerequisites phase="0", applies="ALL">
1. All Rosetta prep steps MUST be FULLY completed 2. USE SKILL `load-project-context`, `orchestration`, `hitl` 3. MUST ALWAYS use todo tasks ledger, ASAP. Phases are sequential. Independent tasks can run in parallel. 4. Orchestrator trusts the system and skills; coordinates sequence, artifacts, state, and approvals only. 5. Workflow state MUST be saved to `agents/TEMP/<FEATURE>/research-flow-state.md` file.
6. If `/goal` is set repeat phases 3-4 until goal is met.
</prerequisites>
<context_load phase="1" subagent="researcher" role="Context gatherer for research scope" subagent_required_model="inherit">
1. Read all lines from CONTEXT.md, ARCHITECTURE.md, and IMPLEMENTATION.md. 2. Input: user research request. Output: loaded project context. 3. Update `research-flow-state.md`.
</context_load>
<prompt_craft phase="2" subagent="researcher" role="Research prompt architect" subagent_required_model="inherit">
1. Create an optimized research prompt for the user request. 2. Save as `research-prompt.md` in FEATURE PLAN folder. Output ONLY the optimized prompt. 3. Input: user request + project context. Output: `research-prompt.md`. 4. Required skills: `reasoning` 5. Update `research-flow-state.md`. 6. HITL approval of research prompt before execution.
</prompt_craft>
<execute_research phase="3" subagent="researcher" role="Deep research executor" subagent_required_model="inherit">
1. Execute the approved research prompt as a separate subagent. 2. Input: approved `research-prompt.md`. Output: `docs/<feature>-research.md`. 3. Required skills: `research` 4. Update `research-flow-state.md`.
</execute_research>
<finalize phase="4" subagent="researcher" role="Research finalizer" subagent_required_model="inherit">
1. Finalize `docs/<feature>-research.md`. 2. Input: completed research document. Output: finalized research document. 3. Update `research-flow-state.md` and mark complete.
</finalize>
</workflow_phases>
</research_flow>
Read more
name: research-flow description: "Workflow for deep project research with grounded references, parallel exploration, etc."
<research_flow>
<description_and_purpose> Orchestrates deep research via meta-prompting: craft an optimized research prompt, then execute it in a dedicated subagent. </description_and_purpose>
<workflow_phases>
<prerequisites phase="0", applies="ALL">
1. All Rosetta prep steps MUST be FULLY completed 2. USE SKILL `load-project-context`, `orchestration`, `hitl` 3. MUST ALWAYS use todo tasks ledger, ASAP. Phases are sequential. Independent tasks can run in parallel. 4. Orchestrator trusts the system and skills; coordinates sequence, artifacts, state, and approvals only. 5. Workflow state MUST be saved to `agents/TEMP/<FEATURE>/research-flow-state.md` file.
6. If `/goal` is set repeat phases 3-4 until goal is met.
</prerequisites>
<context_load phase="1" subagent="researcher" role="Context gatherer for research scope" subagent_required_model="inherit">
1. Read all lines from CONTEXT.md, ARCHITECTURE.md, and IMPLEMENTATION.md. 2. Input: user research request. Output: loaded project context. 3. Update `research-flow-state.md`.
</context_load>
<prompt_craft phase="2" subagent="researcher" role="Research prompt architect" subagent_required_model="inherit">
1. Create an optimized research prompt for the user request. 2. Save as `research-prompt.md` in FEATURE PLAN folder. Output ONLY the optimized prompt. 3. Input: user request + project context. Output: `research-prompt.md`. 4. Required skills: `reasoning` 5. Update `research-flow-state.md`. 6. HITL approval of research prompt before execution.
</prompt_craft>
<execute_research phase="3" subagent="researcher" role="Deep research executor" subagent_required_model="inherit">
1. Execute the approved research prompt as a separate subagent. 2. Input: approved `research-prompt.md`. Output: `docs/<feature>-research.md`. 3. Required skills: `research` 4. Update `research-flow-state.md`.
</execute_research>
<finalize phase="4" subagent="researcher" role="Research finalizer" subagent_required_model="inherit">
1. Finalize `docs/<feature>-research.md`. 2. Input: completed research document. Output: finalized research document. 3. Update `research-flow-state.md` and mark complete.
</finalize>
</workflow_phases>
</research_flow>
Repo: griddynamics/rosetta
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