sensory-bridges

Convert multimodal artifacts into canonical perception responses for agent reasoning.

Updated Mar 26, 2026

Search Tags:#automation #caching #schema #multimodal #perception #bridge

Ready to run

npx skills add https://github.com/jlenrique/course-DEV-IDE-with-AGENTS --skill sensory-bridges

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sensory-bridges
Source: https://github.com/jlenrique/course-DEV-IDE-with-AGENTS/tree/main/skills/sensory-bridges
Command: npx skills add https://github.com/jlenrique/course-DEV-IDE-with-AGENTS --skill sensory-bridges

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, python-dotenv, python-pptx, pypdf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Shared infrastructure for converting multimodal artifacts into structured, agent-interpretable representations, enabling agents to perceive images, audio, PDF, PPTX, and video consistently.

Core Features & Use Cases

Modality-specific perception bridges that produce canonical perception schema outputs.
Optional run-scoped caching and dynamic bridge loading for performance and resilience.
Use Case: An AI agent validates content across formats (slides, docs, media) for fidelity, auditing, and narration planning.

Quick Start

Call perceive on an artifact with its modality and gate to obtain a canonical perception response.

Frequently Asked Questions about sensory-bridges

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert multimodal assets like PDFs and PPTX into structured agent perception responses?▼

To convert multimodal assets into agent perception, call the perceive function on an artifact with its modality and gate. This generates deterministic canonical perception schema outputs for images, audio, PDFs, PPTX, and video to support agent reasoning.

What is a canonical perception schema and how does it help AI agents process multimodal artifacts?▼

A canonical perception schema is a structured, agent-interpretable representation of multimodal artifacts. It allows AI agents to consistently perceive images, audio, PDFs, PPTX, and video across gates G0–G6 by providing deterministic outputs for content validation and reasoning.

Do I need a specific Python environment to use perception bridges for multimodal artifact processing?▼

Yes, multimodal artifact processing requires a Python environment with the sensory-bridges codebase, the canonical perception schema, and dependencies like requests, python-dotenv, python-pptx, and pypdf. Optional references can be added for runtime extension.

Can I cache multimodal perception responses to improve agent performance across multiple runs?▼

Yes, you can cache multimodal perception responses. The system supports optional run-scoped caching and dynamic bridge loading to enhance performance and resilience when agents process images, audio, PDFs, PPTX, and video across gates G0–G6.

What is the best way to validate content fidelity across slides, documents, and media formats for an AI agent?▼

The best way to validate content fidelity across formats is using modality-specific perception bridges. They produce canonical perception schema outputs for slides, docs, and media, enabling agents to consistently audit, validate, and plan narration across multimodal artifacts.