create-architecture▌
Donchitos/Claude-Code-Game-Studios · updated Apr 16, 2026
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### Create Architecture
- ›name: create-architecture
- ›description: "Guided, section-by-section authoring of the master architecture document for the game. Reads all GDDs, the systems index, existing ADRs, and the engine reference library to produce a com
- ›argument-hint: "[focus-area: full | layers | data-flow | api-boundaries | adr-audit] [--review full|lean|solo]"
| name | create-architecture |
| description | "Guided, section-by-section authoring of the master architecture document for the game. Reads all GDDs, the systems index, existing ADRs, and the engine reference library to produce a complete architecture blueprint before any code is written. Engine-version-aware: flags knowledge gaps and validates decisions against the pinned engine version." |
| argument-hint | "[focus-area: full | layers | data-flow | api-boundaries | adr-audit] [--review full|lean|solo]" |
| user-invocable | true |
| allowed-tools | Read, Glob, Grep, Write, Bash, AskUserQuestion, Task |
| agent | technical-director |
Create Architecture
This skill produces docs/architecture/architecture.md — the master architecture
document that translates all approved GDDs into a concrete technical blueprint.
It sits between design and implementation, and must exist before sprint planning begins.
Distinct from /architecture-decision: ADRs record individual point decisions.
This skill creates the whole-system blueprint that gives ADRs their context.
Resolve the review mode (once, store for all gate spawns this run):
- If
--review [full|lean|solo]was passed → use that - Else read
production/review-mode.txt→ use that value - Else → default to
lean
See .claude/docs/director-gates.md for the full check pattern.
Argument modes:
- No argument /
full: Full guided walkthrough — all sections, start to finish layers: Focus on the system layer diagram onlydata-flow: Focus on data flow between modules onlyapi-boundaries: Focus on API boundary definitions onlyadr-audit: Audit existing ADRs for engine compatibility gaps only
Phase 0: Load All Context
Before anything else, load the full project context in this order:
0a. Engine Context (Critical)
Read the engine reference library completely:
docs/engine-reference/[engine]/VERSION.md→ Extract: engine name, version, LLM cutoff, post-cutoff risk levelsdocs/engine-reference/[engine]/breaking-changes.md→ Extract: all HIGH and MEDIUM risk changesdocs/engine-reference/[engine]/deprecated-apis.md→ Extract: APIs to avoiddocs/engine-reference/[engine]/current-best-practices.md→ Extract: post-cutoff best practices that differ from training data- All files in
docs/engine-reference/[engine]/modules/→ Extract: current API patterns per domain
If no engine is configured, stop and prompt:
"No engine is configured. Run
/setup-enginefirst. Architecture cannot be written without knowing which engine and version you are targeting."
0b. Design Context + Technical Requirements Extraction
Read all approved design documents and extract technical requirements from each:
design/gdd/game-concept.md— game pillars, genre, core loopdesign/gdd/systems-index.md— all systems, dependencies, priority tiers.claude/docs/technical-preferences.md— naming conventions, performance budgets, allowed libraries, forbidden patterns- Every GDD in
design/gdd/— for each, extract technical requirements:- Data structures implied by the game rules
- Performance constraints stated or implied
- Engine capabilities the system requires
- Cross-system communication patterns (what talks to what, how)
- State that must persist (save/load implications)
- Threading or timing requirements
Build a Technical Requirements Baseline — a flat list of all extracted
requirements across all GDDs, numbered TR-[gdd-slug]-[NNN]. This is the
complete set of what the architecture must cover. Present it as:
## Technical Requirements Baseline
Extracted from [N] GDDs | [X] total requirements
| Req ID | GDD | System | Requirement | Domain |
|--------|-----|--------|-------------|--------|
| TR-combat-001 | combat.md | Combat | Hitbox detection per-frame | Physics |
| TR-combat-002 | combat.md | Combat | Combo state machine | Core |
| TR-inventory-001 | inventory.md | Inventory | Item persistence | Save/Load |
This baseline feeds into every subsequent phase. No GDD requirement should be left without an architectural decision to support it by the end of this session.
0c. Existing Architecture Decisions
Read all files in docs/architecture/ to understand what has already been decided.
List any ADRs found and their domains.
0d. Generate Knowledge Gap Inventory
Before proceeding, display a structured summary:
## Engine Knowledge Gap Inventory
Engine: [name + version]
LLM Training Covers: up to approximately [version]
Post-Cutoff Versions: [list]
### HIGH RISK Domains (must verify against engine reference before deciding)
- [Domain]: [Key changes]
### MEDIUM RISK Domains (verify key APIs)
- [Domain]: [Key changes]
### LOW RISK Domains (in training data, likely reliable)
- [Domain]: [no significant post-cutoff changes]
### Systems from GDD that touch HIGH/MEDIUM risk domains:
- [GDD system name] → [domain] → [risk level]
Ask: "This inventory identifies [N] systems in HIGH RISK engine domains. Shall I continue building the architecture with these warnings flagged throughout?"
Phase 1: System Layer Mapping
Map every system from systems-index.md into an architecture layer. The standard
game architecture layers are:
┌─────────────────────────────────────────────┐
│ PRESENTATION LAYER │ ← UI, HUD, menus, VFX, audio
├─────────────────────────────────────────────┤
│ FEATURE LAYER │ ← gameplay systems, AI, quests
├─────────────────────────────────────────────┤
│ CORE LAYER │ ← physics, input, combat, movement
├─────────────────────────────────────────────┤
│ FOUNDATION LAYER │ ← engine integration, save/load,
│ │ scene management, event bus
├─────────────────────────────────────────────┤
│ PLATFORM LAYER │ ← OS, hardware, engine API surface
└─────────────────────────────────────────────┘
For each GDD system, ask:
- Which layer does it belong to?
- What are its module boundaries?
- What does it own exclusively? (data, state, behaviour)
Present the proposed layer assignment and ask for approval before proceeding to the next section. Write the approved layer map immediately to the skeleton file.
Engine awareness check: For each system assigned to the Core and Foundation layers, flag if it touches a HIGH or MEDIUM risk engine domain. Show the relevant engine reference excerpt inline.
Phase 2: Module Ownership Map
For each module defined in Phase 1, define ownership:
- Owns: what data and state this module is solely responsible for
- Exposes: what other modules may read or call
- Consumes: what it reads from other modules
- Engine APIs used: which specific engine classes/nodes/signals this module calls directly (with version and risk level noted)
Format as a table per layer, then as an ASCII dependency diagram.
Engine awareness check: For every engine API listed, verify against the relevant module reference doc. If an API is post-cutoff, flag it:
⚠️ [ClassName.method()] — Godot 4.6 (post-cutoff, HIGH risk)
Verified against: docs/engine-reference/godot/modules/[domain].md
Behaviour confirmed: [yes / NEEDS VERIFICATION]
Get user approval on the ownership map before writing.
Phase 3: Data Flow
Define how data moves between modules during key game scenarios. Cover at minimum:
- Frame update path: Input → Core systems → State → Rendering
- Event/signal path: How systems communicate without tight coupling
- Save/load path: What state is serialised, which module owns serialisation
- Initialisation order: Which modules must boot before others
Use ASCII sequence diagrams where helpful. For each data flow:
- Name the data being transferred
- Identify the producer and consumer
- State whether this is synchronous call, signal/event, or shared state
- Flag any data flows that cross thread boundaries
Get user approval per scenario before writing.
Phase 4: API Boundaries
Define the public contracts between modules. For each boundary:
- What is the interface a module exposes to the rest of the system?
- What are the entry points (functions/signals/properties)?
- What invariants must callers respect?
- What must the module guarantee to callers?
Write in pseudocode or the project's actual language (from technical preferences). These become the contracts programmers implement against.
Engine awareness check: If any interface uses engine-specific types (e.g.
Node, Resource, Signal in Godot), flag the version and verify the type
exists and has not changed signature in the target engine version.
Phase 5: ADR Audit + Traceability Check
Review all existing ADRs from Phase 0c against both the architecture built in Phases 1-4 AND the Technical Requirements Baseline from Phase 0b.
ADR Quality Check
For each ADR:
- Does it have an Engine Compatibility section?
- Is the engine version recorded?
- Are post-cutoff APIs flagged?
- Does it have a "GDD Requirements Addressed" section?
- Does it conflict with the layer/ownership decisions made in this session?
- Is it still valid for the pinned engine version?
| ADR | Engine Compat | Version | GDD Linkage | Conflicts | Valid |
|---|---|---|---|---|---|
| ADR-0001: [title] | ✅/❌ | ✅/❌ | ✅/❌ | None/[conflict] | ✅/⚠️ |
Traceability Coverage Check
Map every requirement from the Technical Requirements Baseline to existing ADRs. For each requirement, check if any ADR's "GDD Requirements Addressed" section or decision text covers it:
| Req ID | Requirement | ADR Coverage | Status |
|---|---|---|---|
| TR-combat-001 | Hitbox detection per-frame | ADR-0003 | ✅ |
| TR-combat-002 | Combo state machine | — | ❌ GAP |
Count: X covered, Y gaps. For each gap, it becomes a Required New ADR.
Required New ADRs
List all decisions made during this architecture session (Phases 1-4) that do not yet have a corresponding ADR, PLUS all uncovered Technical Requirements. Group by layer — Foundation first:
Foundation Layer (must create before any coding):
/architecture-decision [title]→ covers: TR-[id], TR-[id]
Core Layer:
/architecture-decision [title]→ covers: TR-[id]
Phase 6: Missing ADR List
Based on the full architecture, produce a complete list of ADRs that should exist but don't yet. Group by priority:
Must have before coding starts (Foundation & Core decisions):
- [e.g. "Scene management and scene loading strategy"]
- [e.g. "Event bus vs direct signal architecture"]
Should have before the relevant system is built:
- [e.g. "Inventory serialisation format"]
Can defer to implementation:
- [e.g. "Specific shader technique for water"]
Phase 7: Write the Master Architecture Document
Once all sections are approved, write the complete document to
docs/architecture/architecture.md.
Ask: "May I write the master architecture document to docs/architecture/architecture.md?"
The document structure:
# [Game Name] — Master Architecture
## Document Status
- Version: [N]
- Last Updated: [date]
- Engine: [name + version]
- GDDs Covered: [list]
- ADRs Referenced: [list]
## Engine Knowledge Gap Summary
[Condensed from Phase 0d inventory — HIGH/MEDIUM risk domains and their implications]
## System Layer Map
[From Phase 1]
## Module Ownership
[From Phase 2]
## Data Flow
[From Phase 3]
## API Boundaries
[From Phase 4]
## ADR Audit
[From Phase 5]
## Required ADRs
[From Phase 6]
## Architecture Principles
[3-5 key principles that govern all technical decisions for this project,
derived from the game concept, GDDs, and technical preferences]
## Open Questions
[Decisions deferred — must be resolved before the relevant layer is built]
Phase 7b: Technical Director Sign-Off + Lead Programmer Feasibility Review
After writing the master architecture document, perform an explicit sign-off before handoff.
Step 1 — Technical Director self-review (this skill runs as technical-director):
Apply gate TD-ARCHITECTURE (.claude/docs/director-gates.md) as a self-review. Check all four criteria from that gate definition against the completed document.
Review mode check — apply before spawning LP-FEASIBILITY:
solo→ skip. Note: "LP-FEASIBILITY skipped — Solo mode." Proceed to Phase 8 handoff.lean→ skip (not a PHASE-GATE). Note: "LP-FEASIBILITY skipped — Lean mode." Proceed to Phase 8 handoff.full→ spawn as normal.
Step 2 — Spawn lead-programmer via Task using gate LP-FEASIBILITY (.claude/docs/director-gates.md):
Pass: architecture document path, technical requirements baseline summary, ADR list.
Step 3 — Present both assessments to the user:
Show the Technical Director assessment and Lead Programmer verdict side by side.
Use AskUserQuestion — "Technical Director and Lead Programmer have reviewed the architecture. How would you like to proceed?"
Options: Accept — proceed to handoff / Revise flagged items first / Discuss specific concerns
Step 4 — Record sign-off in the architecture document:
Update the Document Status section:
- Technical Director Sign-Off: [date] — APPROVED / APPROVED WITH CONDITIONS
- Lead Programmer Feasibility: FEASIBLE / CONCERNS ACCEPTED / REVISED
Ask: "May I update the Document Status section in docs/architecture/architecture.md with the sign-off?"
Phase 8: Handoff
After writing the document, provide a clear handoff:
- Run these ADRs next (from Phase 6, prioritised): list the top 3
- Gate check: "The master architecture document is complete. Run
/gate-check pre-productionwhen all required ADRs are also written." - Update session state: Write a summary to
production/session-state/active.md
Collaborative Protocol
This skill follows the collaborative design principle at every phase:
- Load context silently — do not narrate file reads
- Present findings — show the knowledge gap inventory and layer proposals
- Ask before deciding — present options for each architectural choice
- Get approval before writing — each phase section is written only after user approves the content
- Incremental writing — write each approved section immediately; do not accumulate everything and write at the end. This survives session crashes.
Never make a binding architectural decision without user input. If the user is unsure, present 2-4 options with pros/cons before asking them to decide.
Recommended Next Steps
- Run
/architecture-decision [title]for each required ADR listed in Phase 6 — Foundation layer ADRs first - Run
/create-control-manifestonce the required ADRs are written to produce the layer rules manifest - Run
/gate-check pre-productionwhen all required ADRs are written and the architecture is signed off
How to use create-architecture on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add create-architecture
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches create-architecture from GitHub repository Donchitos/Claude-Code-Game-Studios and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate create-architecture. Access the skill through slash commands (e.g., /create-architecture) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★52 reviews- ★★★★★Arjun Verma· Dec 24, 2024
I recommend create-architecture for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ama Sanchez· Dec 16, 2024
Solid pick for teams standardizing on skills: create-architecture is focused, and the summary matches what you get after install.
- ★★★★★Arjun Menon· Nov 15, 2024
create-architecture reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakshi Patil· Nov 7, 2024
create-architecture is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aisha Dixit· Nov 7, 2024
We added create-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Arya Singh· Nov 3, 2024
create-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Oct 26, 2024
Keeps context tight: create-architecture is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Liam Tandon· Oct 26, 2024
create-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Arya Ghosh· Oct 22, 2024
We added create-architecture from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Li Torres· Oct 6, 2024
Registry listing for create-architecture matched our evaluation — installs cleanly and behaves as described in the markdown.
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