project-stage-detect▌
Donchitos/Claude-Code-Game-Studios · updated Apr 16, 2026
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### Project Stage Detect
- ›name: project-stage-detect
- ›description: "Automatically analyze project state, detect stage, identify gaps, and recommend next steps based on existing artifacts. Use when user asks 'where are we in development', 'what stage are
- ›argument-hint: "[optional: role filter like 'programmer' or 'designer']"
| name | project-stage-detect |
| description | "Automatically analyze project state, detect stage, identify gaps, and recommend next steps based on existing artifacts. Use when user asks 'where are we in development', 'what stage are we in', 'full project audit'." |
| argument-hint | "[optional: role filter like 'programmer' or 'designer']" |
| user-invocable | true |
| allowed-tools | Read, Glob, Grep, Bash, Write |
| model | haiku # Read-only diagnostic skill — no specialist agent delegation needed |
Project Stage Detection
This skill scans your project to determine its current development stage, completeness of artifacts, and gaps that need attention. It's especially useful when:
- Starting with an existing project
- Onboarding to a codebase
- Checking what's missing before a milestone
- Understanding "where are we?"
Workflow
1. Scan Key Directories
Analyze project structure and content:
Design Documentation (design/):
- Count GDD files in
design/gdd/*.md - Check for game-concept.md, game-pillars.md, systems-index.md
- If systems-index.md exists, count total systems vs. designed systems
- Analyze completeness (Overview, Detailed Design, Edge Cases, etc.)
- Count narrative docs in
design/narrative/ - Count level designs in
design/levels/
Source Code (src/):
- Count source files (language-agnostic)
- Identify major systems (directories with 5+ files)
- Check for core/, gameplay/, ai/, networking/, ui/ directories
- Estimate lines of code (rough scale)
Production Artifacts (production/):
- Check for active sprint plans
- Look for milestone definitions
- Find roadmap documents
Prototypes (prototypes/):
- Count prototype directories
- Check for READMEs (documented vs undocumented)
- Assess if prototypes are archived or active
Architecture Docs (docs/architecture/):
- Count ADRs (Architecture Decision Records)
- Check for overview/index documents
Tests (tests/):
- Count test files
- Estimate test coverage (rough heuristic)
2. Classify Project Stage
Based on scanned artifacts, determine stage. Check production/stage.txt first —
if it exists, use its value (explicit override from /gate-check). Otherwise,
auto-detect using these heuristics (check from most-advanced backward):
| Stage | Indicators |
|---|---|
| Concept | No game concept doc, brainstorming phase |
| Systems Design | Game concept exists, systems index missing or incomplete |
| Technical Setup | Systems index exists, engine not configured |
| Pre-Production | Engine configured, src/ has <10 source files |
| Production | src/ has 10+ source files, active development |
| Polish | Explicit only (set by /gate-check Production → Polish gate) |
| Release | Explicit only (set by /gate-check Polish → Release gate) |
3. Collaborative Gap Identification
DO NOT just list missing files. Instead, ask clarifying questions:
- "I see combat code (
src/gameplay/combat/) but nodesign/gdd/combat-system.md. Was this prototyped first, or should we reverse-document?" - "You have 15 ADRs but no architecture overview. Should I create one to help new contributors?"
- "No sprint plans in
production/. Are you tracking work elsewhere (Jira, Trello, etc.)?" - "I found a game concept but no systems index. Have you decomposed the concept into individual systems yet, or should we run
/map-systems?" - "Prototypes directory has 3 projects with no READMEs. Were these experiments, or do they need documentation?"
4. Generate Stage Report
Use template: .claude/docs/templates/project-stage-report.md
Report structure:
# Project Stage Analysis
**Date**: [date]
**Stage**: [Concept/Systems Design/Technical Setup/Pre-Production/Production/Polish/Release]
**Stage Confidence**: [PASS — clearly detected / CONCERNS — ambiguous signals / FAIL — critical gaps block progress]
## Completeness Overview
- Design: [X%] ([N] docs, [gaps])
- Code: [X%] ([N] files, [systems])
- Architecture: [X%] ([N] ADRs, [gaps])
- Production: [X%] ([status])
- Tests: [X%] ([coverage estimate])
## Gaps Identified
1. [Gap description + clarifying question]
2. [Gap description + clarifying question]
## Recommended Next Steps
[Priority-ordered list based on stage and role]
5. Role-Filtered Recommendations (Optional)
If user provided a role argument (e.g., /project-stage-detect programmer):
Programmer:
- Focus on architecture docs, test coverage, missing ADRs
- Code-to-docs gaps
Designer:
- Focus on GDD completeness, missing design sections
- Prototype documentation
Producer:
- Focus on sprint plans, milestone tracking, roadmap
- Cross-team coordination docs
General (no role):
- Holistic view of all gaps
- Highest-priority items across domains
6. Request Approval Before Writing
Collaborative protocol:
I've analyzed your project. Here's what I found:
[Show summary]
Gaps identified:
1. [Gap 1 + question]
2. [Gap 2 + question]
Recommended next steps:
- [Priority 1]
- [Priority 2]
- [Priority 3]
May I write the full stage analysis to production/project-stage-report.md?
Wait for user approval before creating the file.
Example Usage
# General project analysis
/project-stage-detect
# Programmer-focused analysis
/project-stage-detect programmer
# Designer-focused analysis
/project-stage-detect designer
Follow-Up Actions
After generating the report, suggest relevant next steps:
- Concept exists but no systems index? →
/map-systemsto decompose into systems - Missing design docs? →
/reverse-document design src/[system] - Missing architecture docs? →
/architecture-decisionor/reverse-document architecture - Prototypes need documentation? →
/reverse-document concept prototypes/[name] - No sprint plan? →
/sprint-plan - Approaching milestone? →
/milestone-review
Collaborative Protocol
This skill follows the collaborative design principle:
- Question First: Ask about gaps, don't assume
- Present Options: "Should I create X, or is it tracked elsewhere?"
- User Decides: Wait for direction
- Show Draft: Display report summary
- Get Approval: "May I write to production/project-stage-report.md?"
Never silently write files. Always show findings and ask before creating artifacts.
How to use project-stage-detect 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 project-stage-detect
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches project-stage-detect 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 project-stage-detect. Access the skill through slash commands (e.g., /project-stage-detect) 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
Submit your Claude Code skill and start earning
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
4.7★★★★★31 reviews- ★★★★★Shikha Mishra· Dec 20, 2024
Solid pick for teams standardizing on skills: project-stage-detect is focused, and the summary matches what you get after install.
- ★★★★★Kaira Okafor· Dec 16, 2024
project-stage-detect has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ganesh Mohane· Dec 12, 2024
Useful defaults in project-stage-detect — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ishan Gonzalez· Nov 11, 2024
project-stage-detect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 3, 2024
project-stage-detect is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Oct 22, 2024
Keeps context tight: project-stage-detect is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ishan Abebe· Sep 21, 2024
Useful defaults in project-stage-detect — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Rahul Santra· Sep 13, 2024
We added project-stage-detect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Lucas Smith· Sep 13, 2024
project-stage-detect has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Yuki Gonzalez· Sep 5, 2024
I recommend project-stage-detect for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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