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JustinChaney2023
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inference-orchestrator

Orchestrate local OCR, STT, embeddings, and extraction jobs with caching and model verification.

Updated Aug 27, 2026
Search Tags:#pipeline#OCR#inference#orchestrate#local-ML#STT#model-integrity
One-click install
npx skills add https://github.com/JustinChaney2023/orate --skill inference-orchestrator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: inference-orchestrator
Source: https://github.com/JustinChaney2023/orate/tree/main/roles/inference-orchestrator
Command: npx skills add https://github.com/JustinChaney2023/orate --skill inference-orchestrator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of unreliable, fragmented local ML processing by providing a unified orchestration layer for OCR, STT, embeddings, and extraction.

Core Features & Use Cases

  • Manage processing jobs with a structured model, including job_id, patient_id, visit_id, job_type, inputs, outputs, status, and retry policies.
  • Enforce resource limits, timeouts, and concurrency controls to prevent overuse of local hardware.
  • Provide deterministic caching keyed by input hashes and preprocessing config to ensure reproducible results.
  • Maintain a model_registry.json with versioned models and sha256 verification to enforce model integrity.
  • Offer progress updates to UI and deterministic retry strategies across job types.

Quick Start

Use the inference-orchestrator to initialize and run a batch of tasks (OCR, STT, embeddings) on the folder 'input_docs/' with caching and model verification.

Frequently Asked Questions about inference-orchestrator

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

FAQPage Schema
How do I orchestrate local ML pipelines for OCR and STT with reliable retries?▼

Local ML pipelines for OCR and STT are orchestrated using a unified layer that manages job queues, enforces timeouts, and applies deterministic retry strategies across processing tasks.

Can I enforce model integrity and version pinning for local inference workflows?▼

Model integrity for local inference workflows is enforced by maintaining a versioned model registry with sha256 verification, ensuring artifacts are pinned and verified before execution.

Does deterministic caching work for local machine learning extraction tasks?▼

Deterministic caching for local ML extraction tasks works by keying on input hashes and preprocessing configurations, ensuring reproducible results without redundant model execution.

What is the best way to manage resource limits for edge ML processing?▼

Resource limits for edge ML processing are managed by enforcing concurrency controls and strict hardware usage caps, preventing local system overuse during batch extraction tasks.

How do I run a batch of embeddings and OCR tasks on a local input folder?▼

Batch embeddings and OCR tasks on a local folder are run by initializing the orchestrator with job inputs, enabling caching and model verification for structured pipeline execution.

Why do I need an orchestration layer for desktop machine learning workflows?▼

An orchestration layer for desktop ML workflows is needed to solve unreliable, fragmented processing by unifying job management, caching, and model verification across isolated tasks.

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