claude-proj-blueprint

agent
SUMMARY

Modular, level-based project template for Claude Code + Obsidian + Figma. Pick your maturity level (L1-L4). Plug in your specs. Ship.

README.md

Claude Proj Blueprint

A modular, level-based project template for teams building software with Claude Code, Obsidian, and Figma.

Pick your maturity level. Plug in your specs. Ship.


Why

Every team reinvents project structure from scratch. This blueprint gives you an opinionated skeleton that scales from solo MVP to autonomous multi-agent systems — without locking you into a specific stack.

Level 1 → You and Claude as copilot on a weekend project
Level 4 → Agent teams with self-healing CI and compliance auditors

Maturity Levels

Level What Who it's for What you get
L1 ReAct loop Solo devs, MVPs CLAUDE.md + docs structure + Obsidian vault
L2 Planner + Executor Small teams + Skills (auto-invoked knowledge) + Slash commands
L3 Multi-agent with critique Teams in production + Hooks (automated gates) + Spec review pipeline
L4 Autonomous system Mature teams + Specialized agents + Agent teams + Self-healing

Each level includes everything from the levels below it.

Where do these levels come from? This framework is a practical synthesis — not invented from scratch. It combines industry frameworks, published research, and hands-on experience building with these tools. See Influences & Attribution for the full picture.

Quick Start

# Clone
git clone https://github.com/rtazima/claude-proj-blueprint.git my-project
cd my-project

# Remove the template's git history and start fresh
rm -rf .git && git init

# Bootstrap at your level
chmod +x scripts/bootstrap.sh
./scripts/bootstrap.sh --level 2

# Fill in your project specifics
grep -r '\[SPEC\]' CLAUDE.md docs/ .claude/ | head -20

# Open docs/ as an Obsidian vault (optional but recommended)
# In Obsidian: Open folder as vault → select docs/

# Start working
claude

Project Structure

your-project/
├── CLAUDE.md                    ← L1+  Hub — Claude reads this first
├── .claude/
│   ├── settings.json            ← L1+  Permissions & safety
│   ├── skills/                  ← L2+  Auto-invoked knowledge packs
│   │   ├── code-review/
│   │   ├── testing/
│   │   ├── implement-prd/
│   │   ├── frontend-agent/      ←      UI generation without Figma
│   │   ├── adr/
│   │   ├── memory/              ←      L4 long-term memory retrieval
│   │   └── _template-skill/     ←      Create your own
│   ├── commands/                ← L2+  Slash commands
│   │   ├── implement.md         ←      /implement <prd-path>
│   │   ├── deploy.md            ←      /deploy
│   │   └── spec-review.md       ←      /spec-review <path>
│   ├── hooks.json               ← L3+  Automated gates
│   └── agents/                  ← L4+  Specialized sub-agents
│       ├── compliance-auditor.md
│       ├── security-auditor.md
│       └── quality-guardian.md
├── docs/                        ← L1+  Obsidian vault
│   ├── product/                 ←      PRDs, vision, roadmap
│   ├── architecture/            ←      ADRs (decision records)
│   ├── specs/                   ←      Modular spec modules
│   ├── design-flow-guide.md     ←      Figma vs Agent vs Hybrid decision
│   └── runbooks/                ←      Deploy, debug, post-mortems
├── src/                         ←      Your code
├── memory/                      ← L4+  Long-term vector memory
│   ├── index.py                 ←      Index project into vector DB
│   ├── query.py                 ←      Semantic search CLI
│   ├── config.yaml              ←      Configuration
│   └── requirements.txt         ←      pip install -r memory/requirements.txt
└── scripts/
    ├── bootstrap.sh             ←      Level-based setup
    ├── lint-check.sh            ←      L3+ post-write hook
    ├── security-check.sh        ←      L3+ pre-bash hook
    └── post-commit-index.sh     ←      L4 auto-index on commit

Spec Modules

Specs are plug-and-play knowledge modules in docs/specs/. Enable only what your project needs.

Module What it covers When to enable
compliance/ Laws, regulations, ISOs Regulated data, certifications
security/ OWASP, access control, crypto Every production project
observability/ Logs, metrics, traces, alerts Every production project
scalability/ Caching, queues, performance When scale matters
versioning/ API versions, migrations, semver Public APIs, multiple clients
design-system/ Design tokens, component patterns, UI strategy Projects with UI — makes Figma optional
accessibility/ WCAG, a11y User-facing products
i18n/ Multi-language, localization International products
testing-strategy/ Test pyramid, QA process Teams with 3+ devs
devops/ CI/CD, IaC, environments Every production project
data-architecture/ Modeling, pipelines, analytics Data-intensive products
ai-ml/ Models, prompts, evals, guardrails AI/ML products
long-term-memory/ Vector DB, semantic search L4 autonomous systems

Adding a custom module

mkdir docs/specs/my-module
# Use any existing module as reference
# Optionally create .claude/skills/my-module/SKILL.md for auto-invocation

The [SPEC] Convention

Every [SPEC] marker is an extension point. The blueprint tells you where things go; you fill in what goes there.

## Tech Stack
[SPEC] List the actual project stack:
- Backend: [linguagem + framework]

Becomes:

## Tech Stack
- Backend: Python + FastAPI
- Frontend: React + TypeScript
- Database: PostgreSQL

How the 4 Layers Work

L1 — CLAUDE.md — Claude reads this automatically at every session. Your stack, architecture, conventions, gotchas. Keep it under 200 lines.

L2 — Skills — Markdown files that Claude loads based on natural language triggers. Say "write tests" and the testing skill activates automatically.

L3 — Hooks — Scripts that run before/after Claude uses tools. Lint on every file write. Security check before every bash command. Exit 0 = allow, exit 2 = block.

L4 — Agents — Sub-agents with their own context. Run individually or as a coordinated team with the author-critic loop.

L4 — Memory — Vector database indexes docs/ and src/ for semantic search. Ask "how did we handle rate limiting?" and get the relevant ADR, code, and PRD. Supports Chroma (local, default) or pgvector (shared, team-wide).

pip install -r memory/requirements.txt
python memory/index.py              # Index everything
python memory/query.py "auth"       # Semantic search
python memory/query.py --stats      # What's indexed

Daily Workflow

cd your-project && claude

# L2+
# Shift+Tab+Tab → Plan Mode
# Describe feature intent
# Shift+Tab → Auto Accept
# /compact to compress context
# Commit frequently, new session per feature

# L3+
# /spec-review src/ → run compliance + security + quality audit

# L4+
# /memory search "how did we handle rate limiting"
# "Create an agent team to implement auth with OAuth2"

Integrations

Obsidian — Open docs/ as a vault. PRDs, ADRs, specs are interconnected with [[wiki-links]].

Figma (optional) — For teams with a designer. Add Figma links to PRDs, use the Figma MCP server for design-to-code. See the Design Flow Guide to decide if you need it.

Frontend Agent (alternative to Figma) — For dev-only teams. Define design tokens in docs/specs/design-system/, choose a component library, and the /implement command generates UI from PRD requirements. No designer needed.

GitHub — Issue templates and CI workflow included.

Influences & Attribution

The 4 maturity levels in this blueprint are a practical synthesis. They weren't invented in isolation — they combine industry frameworks, published research, and direct experience building products and technology with AI at the center.

What's original

  • The integration of Obsidian + Claude Code + Figma Make as a unified workflow
  • The specific mapping of Claude Code features (CLAUDE.md, skills, hooks, agent teams) to maturity levels
  • The [SPEC] convention and modular spec system
  • The blueprint structure itself — opinionated on where things go, flexible on what goes there

What came from the industry

Source What it contributed How it shaped the blueprint
Rushika Rai — AI Agent vs Agentic AI framework 5 technical maturity levels (Level 0→4) measuring what the agent can do alone Inspired the level-based structure, but we reframed it: our levels measure how the human changes their way of working, not just the agent's capability
OpenAI — Harness Engineering The concept that engineers should design environments, constraints, and feedback loops — not write code. AGENTS.md as a map to deeper docs. Architectural guardrails as agent multipliers Directly influenced L3 (hooks as automated gates) and the CLAUDE.md → docs/ structure. Our Obsidian vault is the equivalent of their structured docs/ directory
DX Research — Q4/2025 Impact Report Data from 135k+ developers across 435 companies on AI adoption, time savings, and quality impact Validated the progression: most teams are at L1 (copilot). Structured enablement is what moves teams forward, not just tool access
Steven Choi Execution vs judgment distinction. Data showing Staff Engineers gain disproportionately more from AI agents. The "agentic context problem" warning Reinforced why L3 and L4 exist: without governance and memory, L2 generates invisible tech debt at scale
Anthropic — Claude Code architecture Skills, hooks, agent teams, CLAUDE.md convention, context compaction The 4 levels map directly to Claude Code's feature progression: CLAUDE.md (L1) → skills + commands (L2) → hooks (L3) → agent teams + memory (L4)
Mitchell Hashimoto The "harness" metaphor — every time an agent makes a mistake, engineer a solution so it never happens again Shaped the L3 philosophy: hooks and gates aren't bureaucracy, they're compounding improvements
Ruben Hassid Playbook for team adoption of Claude in 5 days using Projects and shared context Informed how we think about L1→L2 transition for teams: adoption is an enablement problem, not a technology problem

The synthesis

The key insight that ties it all together: most frameworks measure what AI can do. This one measures what humans need to change. An autonomous agent (L4) without the right human governance is just a faster way to generate tech debt. The levels exist to ensure that as AI capability increases, human oversight and context-keeping scale with it.

This is a living framework. As tools evolve and the community contributes, the levels will too. If you've found patterns that should be here, open an issue or PR.

Recommended Learning

This blueprint gives you the structure, but you need the fundamentals to use it well. All links below come directly from Anthropic's official documentation.

Start here (all levels)

What Link Why
Claude Code overview code.claude.com/docs/en/overview Understand what Claude Code is and choose your environment (CLI, VS Code, Desktop, Web)
Quickstart code.claude.com/docs/en/quickstart Walk through your first real task: explore a codebase, make changes, commit
Best practices code.claude.com/docs/en/best-practices Patterns that work, anti-patterns that don't. Read this before writing your first CLAUDE.md

Level 1 — Copilot

What Link Why
CLAUDE.md and memory code.claude.com/docs/en/memory How to give Claude persistent instructions. The foundation of everything else
Common workflows code.claude.com/docs/en/common-workflows Plan mode, auto-accept, /compact, commit patterns
Settings code.claude.com/docs/en/settings Permissions, safety, environment variables

Level 2 — Planner + Executor

What Link Why
Skills code.claude.com/docs/en/skills Auto-invoked knowledge packs. How to create, trigger, and scope them
Extend Claude Code code.claude.com/docs/en/features-overview Map of all extension points: skills, subagents, hooks, MCP, plugins. Know when to use what
MCP servers code.claude.com/docs/en/mcp Connect Claude to external tools (Figma, GitHub, databases). Essential for the Figma Make flow

Level 3 — Governance + Quality

What Link Why
Hooks guide code.claude.com/docs/en/hooks-guide Automate gates: lint on write, security check on bash, audit on completion
Hooks reference code.claude.com/docs/en/hooks Full event schemas, JSON input/output, PreToolUse decision control
Plugins code.claude.com/docs/en/plugins Package skills + hooks + agents into shareable, installable units

Level 4 — Autonomous System

What Link Why
Subagents code.claude.com/docs/en/sub-agents Create specialized agents with custom prompts, tools, permissions, and persistent memory
Agent teams code.claude.com/docs/en/agent-teams Multi-agent coordination: lead + teammates, shared tasks, peer-to-peer messaging. Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
Claude Code SDK code.claude.com/docs/en/sdk Run Claude Code programmatically for CI/CD, automation, and custom orchestration
L4 Setup Guide docs/l4-setup-guide.md Step-by-step setup for Agent Teams, memory, and L4 hooks in this blueprint

Going deeper

What Link Why
How Claude Code works code.claude.com/docs/en/how-it-works Understand the agentic loop under the hood
Claude Code changelog code.claude.com/docs/en/changelog Stay current. Features evolve fast
Anthropic Academy docs.claude.com/en/docs/anthropic-academy Free interactive courses from Anthropic

Note: Claude Code evolves fast. These links were verified against Claude Code v2.1.79 (March 2026). If a link breaks, check the Claude Code docs homepage for the updated path.

Contributing

See CONTRIBUTING.md.

License

MIT

Author

Created by @rtazima.


"Opinionated on structure, flexible on content."

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