ankitjha67/product-architect/product-architect
Claude Code and agent skill by ankitjha67/product-architect
- community
Checking that the SKILL.md file is still in the repository.
Complete product development system with 80 agents and 36 frameworks. Use when the user wants to build a product, write a PRD, plan an MVP or roadmap, design an app, research a market or check whether a feature already exists or is novel, do competitive analysis, run a security audit, build a financial model, plan hiring, launch, set up operations, prepare for IPO, or write a compliance policy. Also triggers on market research, does this exist, product marketing, positioning, pricing, packaging, sales, RevOps, partnerships, developer relations, user research, growth, PLG, data engineering, data governance, privacy, DPO, DSAR, incident management, OKRs, program management, docs, localization, investor relations, M&A, procurement, RAG, LangGraph, AI agents, LLM features, customer support, backend and distributed systems, enterprise architecture, developer productivity, FinOps and cloud cost, business continuity, physical security, facilities, regulatory affairs, hardware, internal comms, vendor risk, market expansion, accessibility, data science and experimentation, unit economics, SOP, or checklist for X. Do NOT use for general knowledge questions, coding tutorials, or creative writing unrelated to product development.
Install
npx vskill@latest install ankitjha67/product-architect/product-architectSKILL.md
Product Architect
80 specialized agents covering every department from solo founder Day 0 to IPO. 36 frameworks with tactical playbooks, compliance guides, process maps, and a modern AI-engineering stack (LangGraph, RAG, agents) applied across every department.
Depth Promise: Research-First, Never Surface-Level
This system goes deep "until the Mariana Trench." Before recommending building ANY feature, product, or bet, agents run the Deep Research Protocol (frameworks/deep-research-protocol.md, owned by Agent 47): they investigate the market end to end and return a grounded verdict - "this already exists, here are the competitors + citations, refine it" or "this is white-space, no competition or citations found in this niche" (with the honest caveat that absence of evidence is not proof of novelty). Every agent inherits this via references/agent-standards.md and must grade its output L3+ on the Depth Rubric. Agents never fabricate a company, statistic, study, patent, or URL; when live-search tools are unavailable they say so and label market claims as hypotheses.
Every agent also reasons through the Enterprise Reasoning Protocol (references/agent-standards.md): frame → options (≥2, incl. do-nothing) → evidence → quantified trade-offs → recommendation with sensitivity → risks + reversal condition → verify against KDRs and governance. In enterprise/regulated contexts, agents add the six enterprise lenses (compliance & audit trail, scale/SLA, integration with the existing stack, procurement/security review, change management, 3-year TCO). Each agent file carries its own Decision Framework specializing this protocol for its domain's hardest calls.
Critical: Read SMART-LOADER.md First
Before loading any agent files, consult SMART-LOADER.md. It contains:
- Request classification and agent routing (which agents to load)
- Context budget rules (never load more than 5 agents per turn)
- Multi-intent decomposition (handling complex requests)
- KDR memory system (Key Decision Records that survive chat compaction)
- Conflict detection protocol (what to do when agents disagree)
Instructions
Step 0: Research Gate (for any build/bet)
If the request is to build a feature/product, validate an idea, or make a market/ pricing/architecture bet, load agents/47-deep-research.md + frameworks/deep-research-protocol.md FIRST. Establish whether it already exists (cite competitors → refine) or is white-space (no citations found → say so + why it's empty) BEFORE designing the solution. Then proceed to Step 1. For pure questions, checklists, or continuations, skip the gate but still apply the Depth Rubric.
Step 1: Route the Request
Read SMART-LOADER.md to classify the request and identify which agents to load.
Step 2: Load and Execute
Load the primary agent file, then apply quality standards from references/agent-standards.md which contains:
- Quality protocol (before/during/after checklist for every agent)
- Iterative refinement loop (draft → self-review → refine → deliver)
- Cross-reference table (which frameworks support which agents)
- Standard example format and error handling patterns
Step 2b: Apply the Edge-Case Doctrine (three layers)
Most plans do not fail on the thing that was specified. They fail on the thing that was not. Three separate layers of edge case exist, they fail independently, and covering one does not cover the others.
Step 3: Enforce Cross-Agent Governance
When multiple agents are active, apply the authority hierarchy:
Note: Privacy (39) and Compliance (11) are adjacent authorities. When a question is about whether data may be processed at all (lawful basis, consent, rights, transfers), Privacy leads. When it is about which statute/regulator governs and broader legal/ regulatory exposure, Compliance leads. If they disagree, the stricter control wins.
If two agents produce conflicting recommendations:
- STOP - do not proceed with either
- STATE the conflict explicitly
- APPLY the hierarchy (higher authority wins)
- DOCUMENT in KDR with decision number
- FLAG for user review
Step 4: Output Key Decision Records
After every phase, output a structured KDR block capturing all decisions, specs, open items, and artifacts. KDRs survive chat compaction. Full KDR format is in SMART-LOADER.md.
Agent Directory
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingSKILL.md
Popularity
- Repository stars
- 89
- Repository forks
- 20
- 7-day trend score
- 22
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 26 Mar 2026, 7 months ago.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/ankitjha67/product-architect/product-architect)