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The most comprehensive open-source product development system. 80 AI agents. 36 frameworks. Solo founder Day 0 → IPO.

Avis de repli linguistique :La documentation localisée n'est pas disponible. Affichage de la version originale officielle en anglais.
npm i product-architect

Product Architect

The most comprehensive open-source product development skill for Claude.

80 agents · 36 frameworks · Research-first, AI-native, depth to the Mariana Trench · Solo founder Day 0 → IPO → public company

This is a Claude Skill. Claude reads SKILL.md. This README is for human visitors.


About

Product Architect turns Claude into an entire product organization.

Most AI product advice is one generalist answering everything at surface level. This is the opposite: 80 specialized department heads covering Discovery, PRD, Engineering, Security, Privacy, Pricing, Growth, RevOps, Legal, Finance, Governance and more. Each carries a department-head-depth playbook, routed to your question by a context engine that loads only what's relevant.

Ask "should we build this?" and you don't get an opinion. You get a cited market verdict (does it already exist, who ships it, where's the gap), then a PRD with the edge cases, an architecture, a pricing model, a compliance read, and a launch plan. Each comes from the agent that owns that domain, cross-checked against the others.

What makes it different:

🔍 Research-first Before recommending you build anything, it investigates the market and returns a verdict: "this exists; here are the competitors, refine it" or "white-space, and here's why the niche may be empty." It never fabricates a company, statistic, or URL.
🧠 Reasons, doesn't template Every agent works through frame → options → evidence → quantified trade-offs → recommendation → risks + the condition that would reverse it. Each carries its own decision framework for its hardest calls.
🏢 Enterprise-grade Regulated-industry, 1000+ person, multi-region depth: audit trails, SLAs, procurement gauntlets, change management, 3-year TCO. Not just startup advice.
🤖 AI-native A full LangGraph + RAG engineering stack, plus a concrete LLM/agent application mapped to all 80 departments.
⚖️ Governed A 5-level authority hierarchy (Compliance → Privacy → Security → Finance → Review) resolves conflicts between agents instead of letting them contradict each other.
💾 Remembers Key Decision Records survive chat compaction. Paste one into a new conversation and pick up exactly where you left off.

Who it's for: solo founders who need a whole company's worth of expertise · product managers who want PRDs with every edge case · engineering leads needing architecture, security, and test strategy · teams operating at Fortune-500 caliber without Fortune-500 headcount.

Honest scope: this is an educational and operational framework built through human-AI collaboration, not professional advice. Legal, financial, security, and HR content requires qualified review before real-world use. See references/DISCLAIMER.md.


Install

Claude.ai (Pro, Max, Team, or Enterprise)

Skills require a paid plan. Free tier does not have access to Skills, including "Add from GitHub."

  1. Download this repo as ZIP (Code → Download ZIP)
  2. Go to Claude.ai → Settings → Capabilities → Skills
  3. Upload the ZIP
  4. Test: Ask Claude "Help me build a product" or "Write a PRD for [feature]"

Claude Code

git clone https://github.com/ankitjha67/product-architect.git
# Place in your Claude Code skills directory (~/.claude/skills/)

API

Use the /v1/skills endpoint with the container.skills parameter. Requires Code Execution Tool beta.

Free Tier Workarounds

If you're on the free plan, you can still use everything in this repo:

  1. Guided mode (recommended) - Open START-HERE.md on GitHub, click Raw, copy everything, paste into a new Claude chat. Describe your idea - Claude will ask what you're building, identify the right agents, give you exact GitHub URLs to paste them one at a time, and guide you through the entire product development process step by step.

  2. Projects - Create a Project in Claude.ai, paste SKILL.md + SMART-LOADER.md as project knowledge. Then paste individual agent/framework files as needed per conversation.

  3. Direct paste - Open any file on GitHub (e.g. frameworks/founders-playbook.md), copy the raw content, and paste it into a regular Claude chat. Every file works standalone.

  4. Claude Code - If you have Claude Code access, clone the repo into your skills directory. Skills load automatically.


What's Inside

80 Specialized Agents

Organized by product lifecycle - each operates at department-head depth:

Phase Agents
Research & Audit 47 Deep Research (exists-vs-novel verdict, research-first gate) · 00 Chief Reviewer (6-pass audit, veto power) · 01 Proactive Advisor (blind spots)
Product 02 Discovery · 03 Strategy · 04 PRD · 05 Design · 06 Engineering · 35 User Research
Build & Test 07 Testing & QA · 08 DevOps & SRE · 41 Technical Program Management
Protect 09 Security · 10 Legal & IP · 11 Compliance (14 policies) · 12 Trust & Safety · 13 Fraud · 39 Privacy & Data Protection (DPO)
Launch & Grow 14 Launch & GTM · 15 Marketing & Sales · 16 Analytics · 17 Customer Success · 31 Product Marketing · 37 Growth
Commercial 32 Sales & RevOps · 33 Partnerships & BizDev · 36 Pricing & Monetization
Operate & Scale 18 Finance · 19 Operations (24 SOPs) · 20 BAU · 21 Innovation · 46 Procurement & Supply Chain
People 22 People & HR · 23 L&D · 24 Wellness & Performance
Corporate 25 PR & Comms · 26 Governance & IPO · 27 ESG · 28 Govt Relations · 44 Investor Relations · 45 Corporate Development & M&A
Data & Platform 29 Data & AI Strategy · 30 Platform & Ecosystem · 34 Developer Relations · 38 Data Engineering
Enablement 40 IT & Corporate Engineering · 42 Content & Docs · 43 Localization & i18n
Engineering Specialisms 48 Mobile Engineering · 49 ML Engineering (MLOps) · 50 Frontend & Web Platform
Customer-Facing Delivery 51 Solutions Engineering (Pre-Sales) · 52 Professional Services · 53 Customer Education · 54 Community
Revenue Systems 55 Billing & Monetization Engineering
Finance Specialisms 56 Revenue Accounting & Controller · 57 Tax · 58 Treasury
Risk & Talent 59 Internal Audit & Enterprise Risk · 60 Talent Acquisition · 61 Total Rewards
Executive & AI Assurance 62 Chief of Staff & BizOps · 63 AI Evaluation & Red-Teaming
Support & Platform Engineering 64 Customer Support · 65 Backend & Distributed Systems · 66 Enterprise Architecture · 67 Developer Productivity & Internal Platform
Infra Economics & Resilience 68 FinOps & Cloud Economics · 69 Business Continuity & Enterprise Resilience
Corporate Security & Workplace 70 Corporate & Physical Security · 71 Workplace, Facilities & Real Estate
Regulated Product & Hardware 72 Regulatory Affairs & Quality Management · 73 Hardware Engineering & Manufacturing
Comms & Vendor Risk 74 Internal Communications · 75 Third-Party & Vendor Risk
Expansion, Ops & Insight 76 Market Expansion & Country Launch · 77 Design Ops & Research Ops · 78 Accessibility & Inclusive Design · 79 Data Science & Experimentation

36 Frameworks

Framework What It Does
AI Engineering Stack LangGraph agents, RAG (hybrid retrieval, rerank, GraphRAG), evals, guardrails, observability, and the maturity ladder - with Anthropic-native options
AI Department Playbooks How every one of the 64 departments applies LLMs/RAG/agents - a specific use case, pattern, stack, and guardrail per function
Enterprise Edge Cases What actually derails work in a large org: sponsor departure, reorg, budget cut, approval deadlock, change freeze, legal hold, vendor EOL, conflicting mandates, M&A freeze, scale pathologies. Each with trigger, first move, and structural fix, plus a pre-mortem sweep
Deep Research Protocol End-to-end market existence/novelty engine: exists-vs-novel verdict, citation hygiene, anti-hallucination gate, per-agent depth map
Founder's Playbook Week-by-week from Day 0 with costs, fundraising, IP, legal
30-Day Launch Engine Positioning, channel selection, day-by-day execution
Scenario Playbooks 54 tactical guides across 31 departments (breach response, pivot, MEDDICC discovery call, DSAR runbook, secure offboarding, price increase, launch readiness, investor update, etc.)
SOP & Process Maps 24 SOPs across 10 departments with automation opportunities
Compensation Bands Salary by role/level/geography + equity + maintenance process
Consulting Frameworks McKinsey 7S, Porter's, Blue Ocean, BCG, JTBD, PESTEL
Stress-Test Framework Product-level failure: 165 checklist items across 18 failure classes, from idempotency and clock skew to money rounding, encoding, illegal state transitions and adversarial abuse, plus how to run the session and a severity triage rubric
OKRs & Goal-Setting North Star metric, metrics tree, OKR cascade & cadence
Pricing & Packaging Price-metric selection, Van Westendorp, discount governance
Growth Model AARRR, growth loops, activation/retention, experiment engine
Sales Playbook Sales stages, MEDDICC, discovery scripts, objection handling
Data Governance Data contracts, quality SLAs, classification, lineage, catalog
Incident Management Severity matrix, on-call, comms, blameless postmortems
Partnership Framework Partner tiers, deal structures, co-sell, partner economics
Brand & Messaging Positioning statement, messaging house, voice & tone, narrative
Customer Journey Lifecycle map, moments of truth, retention/expansion loops
Institutional Memory Decision archaeology, knowledge maps, context handoffs
Corporate Scaling Solo founder → IPO with org charts per stage
Global Compliance Regulatory overview for 30+ countries
+ 13 more PRD, MVP, roadmap, user-flows, risk-matrix, A/B testing, accessibility, product lifecycle, competitive intel, continuous improvement, physical ops, coverage audit, universal checklists

Country Compliance Deep-Dives

India · US · EU · UK · Southeast Asia (Singapore, Indonesia, Thailand, Vietnam, Philippines, Malaysia)


Key Architecture

AI-Native - A modern AI-engineering layer runs through the whole system: the AI Engineering Stack framework (LangGraph orchestration, RAG with hybrid retrieval + reranking + GraphRAG, evals-in-CI, guardrails, observability, and the L0→L5 maturity ladder), plus AI Department Playbooks giving all 80 departments a concrete LLM/RAG/agent application. Defaults to the latest Claude models (Opus 4.8, Sonnet 5, Haiku 4.5) with adaptive thinking, MCP for integrations, and Security (09) + Privacy (39) sign-off on any AI feature touching untrusted input or personal data.

Research-First Depth - Before recommending building anything, agents run the Deep Research Protocol (Agent 47): an end-to-end market investigation that returns a grounded verdict - "this already exists, here are the competitors + citations, refine it" or "this is white-space, no competition/citations in this niche" (with the honest caveat that absence of evidence isn't proof of novelty, plus a "why is it empty?" analysis). Every agent inherits a Depth Rubric (L0 surface → L4 Mariana Trench), must grade itself L3+, and never fabricates a company, statistic, study, patent, or URL.

Smart Loading - SMART-LOADER.md routes each request to only the relevant agents. Scores agents 0-10 on relevance, handles multi-intent requests, supports dynamic mid-conversation loading. Never loads everything at once. Free tier: 3 agents/turn. Pro: 5 agents/turn.

Cross-Agent Governance - 5-level authority hierarchy prevents contradictions: Compliance (override) → Privacy/DPO (override) → Security (override) → Finance (veto) → Chief Reviewer (veto). 5-step conflict protocol: stop, state, apply hierarchy, document, flag.

KDR Memory - Key Decision Records with sequential numbering, conflict detection (scan-before-record), and SUPERSEDED mechanism. Survives chat compaction. Works on free tier. Paste MASTER KDR into new conversations to resume.

Agent Standards - Shared quality protocol inherited by all agents: before/during/after checklists, iterative refinement loops, performance rules, cross-reference table mapping every agent to related frameworks.


Numbers

Metric Value
Agents 80
Frameworks 36
Total files 135
Total lines 66,000+
Country compliance deep-dives 5 (covering 11 countries)
Complete policies drafted 14
SOPs with process maps 24
Tactical scenario playbooks 54 (across 31 departments)
Product edge cases (stress-test) 165 items across 18 failure classes
Organisational edge cases 1,135 across all 80 agents, plus a 54-entry shared catalogue
Routing & session edge cases 7 groups in SMART-LOADER (ambiguity, routing, governance deadlock, research, memory, budget, output integrity)
Salary bands 5 functions × 6 levels × 2 geographies

Repository Structure

product-architect/
├── SKILL.md                     ← Claude reads this (skill entry point)
├── SMART-LOADER.md              ← Context router + memory engine
├── START-HERE.md                ← Free tier entry point (paste into Claude)
├── README.md                    ← You are here (for humans)
├── ENHANCEMENTS.md              ← Enhancement roadmap (what was added & why)
├── CONTRIBUTING.md              ← Conventions, quality bar, wiring checklist
├── CHANGELOG.md                 ← Version history (semver, matches SKILL.md)
├── LICENSE                      ← MIT
├── .github/                     ← CI (validator on every PR), issue & PR templates
├── agents/                      ← 80 agent files (00-79)
├── frameworks/                  ← 36 framework files
├── references/
│   ├── compliance/              ← Country deep-dives (5 files)
│   ├── agent-standards.md       ← Quality protocol for all agents
│   ├── DISCLAIMER.md            ← Professional review requirements
│   ├── github-readme.md         ← Extended documentation
│   └── industry-references.md   ← Best-in-class by function
└── tools/
    ├── navigator.jsx            ← Interactive web UI for founders
    └── validate_repo.py         ← Validator (counts, numbering, links, fences, doc-consistency)

Disclaimer

This is an educational framework created through human-AI collaboration, not professional advice. All legal, financial, security, and HR content requires qualified professional review before real-world use. See references/DISCLAIMER.md for full details including AI authorship considerations and human involvement requirements.

License

MIT - see LICENSE.

Contributing

Contributions welcome - new agents, deeper frameworks, industry-specific extensions, country compliance additions, bug fixes. See CONTRIBUTING.md for the conventions, quality bar, and wiring checklist, and CHANGELOG.md for version history.


Built through iterative human-AI collaboration using Claude (Anthropic). Compliant with Anthropic's official skill guide. 25/25 skill compliance checks passed.

Miroir archivé depuis le dépôt GitHub officiel · Voir sur GitHub
Date du snapshot : 2026-11-09