AI Context Inbox

The problem

LLMs often jump to conclusions, offer premature advice, or start generating code before you have finished providing all the necessary context. This leads to wasted tokens, fragmented logic, and the need to repeatedly correct the assistant as you upload files or explain complex requirements piece by piece.

What it does

• Enforces a strict intake phase that prevents the AI from processing or reacting until you explicitly signal completion.
• Manages a staged context state that tracks requirements, constraints, background info, and multiple file uploads.
• Automatically resolves contradictions by prioritizing later corrections and superseding outdated information.
• Consolidates duplicates and organizes incoming material into logical groups like goals, artifacts, and preferences.
• Triggers substantive work only upon specific natural language cues or explicit commands like READY or PROCESS.

Why this beats prompting it yourself

Manually telling an AI to wait is unreliable, as context windows grow and earlier instructions lose focus. This skill implements a dedicated lifecycle for information collection, ensuring that corrections actually overwrite previous statements rather than just adding to the confusion.

Use cases

• Dumping multiple logs, code snippets, and environment details before starting a complex debugging session.
• Providing a long series of brand guidelines, examples, and tone requirements for a creative brief.
• Uploading several versions of a document to ensure only the final authoritative version is used for analysis.
• Explaining a multi-step business logic problem gradually without being interrupted by premature suggestions.

Known limitations

The skill is subject to the underlying model's context window limits. For extremely large datasets, it may suggest splitting tasks if memory saturation is reached.