PlayReceipt

The description states that PlayReceipt audits eight explicit gates across reliability, balance, accessibility, and human calibration. It returns a deterministic receipt ID derived from SHA-256, based on input evidence.

It supports multiple interfaces:

• CLI
• HTTP API
• Responsive dashboard
• Repository-local Codex skill
• GitHub Action

The system is described as having no runtime npm dependencies, and all components are built using Node.js, JavaScript, and tools like Codex and GPT-5.6.

Inference It appears to be a developer-facing tool for validating game builds through automated checks, with an emphasis on transparency and auditability rather than automation of subjective elements like fun.

The author states that AI coding tools can make a build "green" but do not prove player choices matter, accessibility controls exist, or a game is fun. PlayReceipt aims to provide an “evidence-based” release gate instead of synthetic scores.

It positions itself as a tool that makes missing claims visible and provides a forensic ledger of audit results.

Inference The positioning evolved from a general-purpose AI-assisted development tool toward a more specific, trust-building mechanism for game quality assurance — especially in areas where automation cannot fully replace human judgment.

The description does not name specific customers or personas. However, it implies use by:

• Game developers working with AI tools
• CI/CD engineers looking to enforce quality gates
• Judges or reviewers who want to audit game builds

It also mentions a “judge” role that can paste evidence JSON into the app without an account.

Inference The target ICP likely includes indie and professional game developers using AI-assisted workflows, particularly those concerned with automated testing and compliance with accessibility or reliability standards.

There is no mention of pricing, monetization strategy, or business model in the description. The tool is described as open-source, deployed publicly on Vercel, and available via GitHub Action.

Not evidenced

The system uses:

• Codex and GPT-5.6
• Node.js and JavaScript
• SHA-256 for content-addressed receipts
• No database or submission-history write path
• Deterministic engine that produces identical outputs from identical inputs
• CLI, HTTP API, dashboard, GitHub Action, and repository-local skill

It includes:

• Eleven regression tests
• Public Vercel deployment
• Independent review trail
• No runtime npm dependencies

Inference The architecture is lightweight and deterministic. It emphasizes reproducibility and auditability over scalability or user management features.

There is no evidence of revenue, customers, or adoption beyond the author's own development efforts. The project was submitted to a hackathon and has no archived history or usage data.

Not evidenced

The description does not reference competitors or similar tools in the market. It focuses on its unique approach to combining AI with deterministic auditing and human boundaries.

Not evidenced

• The tool is described as a hackathon project with no prior traction or commercial history.
• No evidence of real-world usage or feedback from external users.
• Relies heavily on self-reported claims about functionality, not independent validation.
• The system explicitly excludes automation of "fun" — which may limit its utility in broader game development workflows.

Inference There is a risk that the tool lacks market relevance or adoption unless it evolves beyond its current prototype state.

1. Has PlayReceipt been used outside of this hackathon context?
2. Are there any real-world test cases or feedback from developers using it?
3. How does PlayReceipt handle edge cases in evidence input that might not be covered by the current regression tests?
4. What is the plan for expanding beyond the current four assessment types and eight gates?
5. Is there a roadmap for integrating with popular game engines or development platforms?