Grounded Asset Sheets

The description states that Grounded Asset Sheets is:

• An "open agent skill and specification for turning references and approved requirements into evidence-grounded visual asset packages"
• A "dependency-free Python skill package with Markdown specifications, JSON fixtures, an asset-record validator, unit tests, and GitHub Actions validation"
• A system that creates:
  • Camera-neutral asset records
  • Source registers and field-level evidence ledgers
  • Typed identity, structure, state, unknown, and prohibition fields
  • View plans designed to reveal high-risk facts
  • Generator-ready briefs
  • Validation and minimal-repair QA loops

The system includes four adapters covering objects, wearables, vehicles, and scenes. It uses JSON schema for validation and has a consolidated evidence-report command that can output either Markdown or JSON.

The description states the project began with the question: "what if visual continuity were treated like an executable contract instead of a prompt-writing problem?"

It positions itself as:

• An evidence-first approach to AI image production
• A system that turns references into verifiable asset contracts, continuity sheets, and minimal-repair QA
• A solution to problems where AI generators silently change facts that make assets usable (handness, part counts, layer order, attachment points, etc.)

The claim evolution shows a shift from a conceptual problem ("visual continuity as contract") to a technical implementation with concrete components like validators, fixtures, and adapters.

Not evidenced. The description does not identify specific customer segments or target users beyond the general context of AI image generation and visual asset creation.

Not evidenced. The description makes no mention of pricing models, revenue streams, or commercial arrangements. It only states that the project is open source under MPL-2.0.

The description states:

• Built with codex, github-actions, gpt-5.6, json-schema, markdown, python
• Dependency-free Python skill package
• Includes Markdown specifications, JSON fixtures, asset-record validator, unit tests, and GitHub Actions validation
• Four working adapters for objects, wearables, vehicles, and scenes
• Standard-library validator that passes 4 public JSON fixtures
• Consolidated evidence report with Markdown and JSON output
• Twelve passing unit tests including failing cases
• CI validation for package, fixtures, schema, tests, and report
• Synthetic micro-world showcase with no real brand, product, franchise, or third-party reference image

Not evidenced. The description states that the current public fixtures contain:

• 4 registered sources
• 12 evidence-backed fields
• 21 structural components
• 10 explicit unknowns
• 21 explicit prohibitions
• 0 validation errors and 0 warnings

However, there is no evidence of actual customers, revenue, usage metrics, or adoption beyond the author's own synthetic examples.

Not evidenced. The description does not mention competitors, market positioning, or competitive landscape.

• The project is described as a hackathon submission with no evidence of real-world adoption
• All examples are synthetic and lack third-party reference images
• No revenue, customer, or traction data provided beyond self-reporting
• The system appears to be primarily for internal validation rather than production use
• The author states "human judgment remained the approval boundary" which suggests limited automation

1. What specific problems are you trying to solve in real-world AI image generation workflows?
2. Have any organizations actually adopted or integrated this system beyond your synthetic examples?
3. How does this system handle edge cases that aren't covered by your current fixtures?
4. What is the actual validation process for real-world assets vs. synthetic ones?
5. Are there any commercial partnerships or use cases planned for this technology?
6. How do you plan to scale beyond the current four adapters?