ritual-dapp-precompiles

Encode and decode Ritual precompile payloads using ABI reference layouts.

62|56|Updated Apr 24, 2026
One-click install
npx skills add https://github.com/ritual-foundation/ritual-dapp-skills --skill ritual-dapp-precompiles
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ritual-dapp-precompiles
Source: https://github.com/ritual-foundation/ritual-dapp-skills/tree/main/skills/ritual-dapp-precompiles
Command: npx skills add https://github.com/ritual-foundation/ritual-dapp-skills --skill ritual-dapp-precompiles

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a definitive, developer-focused reference for Ritual precompile ABIs, field layouts, and decoding/encoding conventions to accelerate secure on-chain interactions.

Core Features & Use Cases

  • Includes comprehensive address maps and base executor schemas for all precompiles (HTTP, LLM, JQ, ONNX, Long-Running HTTP, ZK, FHE, Sovereign Agent, Image/Audio/Video, DKMS, and more).
  • Enables precise payload construction, result unwrapping, and debugging across synchronous and asynchronous precompiles, with phase-based delivery semantics.
  • Used in contract testing, dApp integration, and tooling to ensure consistent interpretation of precompile inputs/outputs and error handling.

Quick Start

Run through the Ritual precompile ABI reference to learn how to encode, decode, and test Ritual on-chain calls.

Frequently Asked Questions about ritual-dapp-precompiles

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

FAQPage Schema
How do I encode and decode payloads for Ritual precompile on-chain calls?

To encode and decode Ritual precompile payloads, apply the consistent address map and per-precompile field layouts. This ensures accurate construction of on-chain calls and correct unwrapping of results across all supported precompile types.

What Ritual precompiles are available for on-chain inference and agent interactions?

Ritual precompiles include ONNX, HTTP, LLM, JQ, long-running HTTP, ZK long-running, FHE inference, Sovereign Agent, image, audio, video, DKMS, persistent agent, and TX hash. Each has a specific ABI for building and decoding payloads.

How do I debug inconsistent precompile inputs and outputs in my dApp integration?

Debug inconsistent precompile inputs and outputs by referencing the definitive Ritual precompile ABI schemas. Enforcing per-precompile field layouts and phase-based delivery semantics ensures correct interpretation of responses and error handling.

How does phase-based delivery work for long-running HTTP and ZK precompile responses?

Phase-based delivery for long-running HTTP and ZK precompiles manages asynchronous on-chain interactions by defining specific output formats for each phase. This handles result delivery and response unwrapping across synchronous and asynchronous calls.

Can I use the same ABI encoding conventions for FHE inference and ONNX precompiles?

FHE inference and ONNX precompiles share a consistent base executor schema but require per-precompile field layouts. Applying the specific Ritual precompile ABI reference ensures accurate encoding and decoding for each distinct type.