new-product

name	new-product
description	Deep research and architecture design for new product development
args	{"input":{"type":"string","description":"Path to documentation files or product description","required":true}}

New Product - Deep Research & Architecture Design

Given documentation or a product description, perform deep research and cross-reference technologies to determine the optimal architecture for building a new application.

Your Task

You are orchestrating a 6-stage deep research workflow to design product architecture. Run the stages in order, use 4 parallel agents for the deep-research stages, and present a final comprehensive review at the end.

All document and agent-prompt templates live in this skill's references/templates/ — read the listed template at each stage; do not improvise document structures.

---

Stage 0: Initialize Product Workspace

• Parse the input argument: if it is a file path (contains / or ends in .md, .txt, etc.), read the documentation; otherwise treat it as a product description.
• Extract a short kebab-case product name (from the doc's project/product title, or derived from the description, e.g. "real-time chat app" → realtime-chat). Store it for later stages.
• mkdir -p /job-queue/product-{name}/research-notes
• Display: "🚀 Stage 0/6: Workspace created at /job-queue/product-{name}/"

---

Stage 1: Big Idea & High-Level Research

1. Analyze input. From documentation: extract features, user flows, requirements, technical hints/constraints. From a description: core functionality, target users, key features, scale expectations.
2. Ask 3 initial clarifying questions via AskUserQuestion — Product Vision, Scale & Audience, Deployment Context. Exact question/option text: references/templates/stage1-big-idea.md.
3. High-level technology research via WebSearch: "[product type] architecture patterns 2026", "[product type] technology stack comparison 2026", "[product type] best practices 2026", "[scale level] architecture considerations 2026". WebFetch the top 2-3 articles per search; extract common patterns, recommended technologies, pitfalls. Save notes to /job-queue/product-{name}/research-notes/stage1-research.md.
4. Write /job-queue/product-{name}/big-idea.md using the content structure in references/templates/stage1-big-idea.md (overview, value proposition, architecture approach, frontend/backend/database/deployment options with pros/cons, key challenges, next steps, research sources).
5. Display: "✅ Stage 1/6: Big idea generated at product-{name}/big-idea.md"

---

Stages 2-5: Parallel Deep Research for Architectural Documents

Launch all 4 research agents at once via the Task tool with subagent_type="general-purpose". Each agent researches one architectural document independently.

Stage	Task description	Output document	Prompt template
2	"Runtime execution research" — how the system executes work	runtime-execution.md	references/templates/stage2-runtime-execution.md
3	"Abstraction layer research" — how user intent becomes executable logic	abstraction-layer.md	references/templates/stage3-abstraction-layer.md
4	"Integration layer research" — how the system connects to external resources	integration-layer.md	references/templates/stage4-integration-layer.md
5	"Output rendering research" — how results are delivered to consumers	output-rendering.md	references/templates/stage5-output-rendering.md

Build each agent prompt from its template:

1. Read the stage's template plus references/templates/common-closing-sections.md — the single deduplicated tail (Technology Comparison / Implementation Considerations / Research Sources) shared by all four documents; insert it at the template's [COMMON CLOSING SECTIONS] marker with that stage's variants.
2. Substitute [Product Name] and {name}, and paste the full big-idea.md content into the CONTEXT block (every agent needs it for cross-referencing).

Each template already instructs the agent to WebSearch/WebFetch, ask its own AskUserQuestion clarifications, and write both its document and raw notes under /job-queue/product-{name}/.

Wait for all 4 agents to complete, then display "✅ Stage N/6: [topic] research complete" for stages 2-5.

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Stage 6: Final Review & Presentation

1. Read all 5 generated documents (big-idea.md + the 4 architecture docs).
2. Write /job-queue/product-{name}/ARCHITECTURE-SUMMARY.md (stack, style, key decisions, trade-offs, risks, next steps) and print the completion banner — both templates in references/templates/stage6-summary-and-review.md.
3. Ask for feedback via AskUserQuestion (exact text in the same template): Approve, or Revise runtime execution / abstraction layer / integration layer / output rendering.
   • Approve → display "✅ Architecture approved! Ready for implementation."
   • Revise → re-run that stage's research agent with the feedback, regenerate the document, re-present for approval.

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Error Handling

On any stage failure: display which stage failed, list completed vs failed stages, show the error, note partial work saved at /job-queue/product-{name}/, and give recovery instructions (resume with /new-product [input] or continue manually). Example failure display: references/templates/stage6-summary-and-review.md.

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Notes

• Tools: WebSearch + WebFetch (deep research — use extensively), Task (parallel agents), AskUserQuestion (iterative clarification), Read/Write (documents).
• Ask clarifying questions during each stage, not all upfront; each agent reads big-idea.md for context. Documents must be comprehensive, well-structured, actionable.
• Duration ~20-40 min: Stage 0 seconds; Stage 1 ~5-10 min; Stages 2-5 ~15-25 min (parallel); Stage 6 ~2-3 min.
• Done = 6 documents in /job-queue/product-{name}/ (big-idea, 4 architecture docs, ARCHITECTURE-SUMMARY), trade-offs and risks identified, user approval obtained.