How is the skill executed?

It is executed via a Node.js script wrapper using the Codex skill framework with a JSON-based request.

What is the primary storage target?

Data is persisted into the MOVA lab's specialized genetic file store, designed for episodic data tracking.

Is the ingestion process deterministic?

Yes, this skill is marked as deterministic, ensuring consistent storage behavior across repeated automated runs.

What data format does this skill handle?

This skill specifically handles the persistence of 'ds.episode_skill_ingest_run_v1' data structures.

MOVA Episode Ingest & Store

Name: MOVA Episode Ingest & Store
Author: Leryk1981

byLeryk1981

データベース管理

Persists ingested episode data into the MOVA lab's genetic file store for automated workflow cycles.

概要

The MOVA Episode Ingest & Store skill is a specialized automation utility designed to handle the persistence of data within the MOVA workflow ecosystem. It specifically manages the storage of 'ds.episode_skill_ingest_run_v1' data, ensuring that results from Skill Seeker ingestion runs are reliably archived in the lab's genetic file store. This skill is essential for developers using Codex automation who need to maintain a consistent record of episodic data runs for research, debugging, or continuous data pipeline operations.

主な機能

Direct integration with the MOVA genetic file store
Codex-optimized wrapper for CLI-based execution
0 GitHub stars
Automated persistence of episodic ingestion data
Deterministic execution for reliable workflow integration
Standardized handling of ingest run schemas

ユースケース

Managing data persistence in MOVA workflow cycles
Standardizing storage for lab-scale data ingestion runs
Archiving automated Skill Seeker ingestion results

概要

主な機能

Direct integration with the MOVA genetic file store
Codex-optimized wrapper for CLI-based execution
0 GitHub stars
Automated persistence of episodic ingestion data
Deterministic execution for reliable workflow integration
Standardized handling of ingest run schemas

ユースケース

Managing data persistence in MOVA workflow cycles
Standardizing storage for lab-scale data ingestion runs
Archiving automated Skill Seeker ingestion results