skippy-correctness

Validate staged Skippy execution parity against full-model results across deployments.

3.1k|376|Updated Feb 11, 2026
One-click install
npx skills add https://github.com/Mesh-LLM/mesh-llm --skill skippy-correctness
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skippy-correctness
Source: https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/skippy-correctness
Command: npx skills add https://github.com/Mesh-LLM/mesh-llm --skill skippy-correctness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validates that staged Skippy execution yields results consistent with full-model runs, enabling reliable model deployment and troubleshooting.

Core Features & Use Cases

  • Parity validation across staged and full-model executions for multiple model families.
  • Boundary and activation-dtype mismatch diagnosis with deterministic test runs.
  • Integrated harness guidance to reproduce and fix observed mismatches in CI and local environments.

Quick Start

Run the parity harness to compare staged Skippy results with full-model execution for your current model deployment.

Frequently Asked Questions about skippy-correctness

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

FAQPage Schema
How do I validate staged execution parity against full-model results?

To validate staged execution parity, run a deterministic test harness that compares staged Skippy execution results against full-model runs and reports exact differences. This ensures reliable model deployment and troubleshooting.

What is staged vs full-model execution parity in model deployment?

Staged vs full-model execution parity means staged Skippy execution yields results consistent with full-model runs. Validating this parity ensures reliable model deployment and helps troubleshoot mismatches across split boundaries and activation wire types.

Why does my staged model execution produce different results than the full-model run?

Mismatches between staged and full-model execution often stem from split boundaries or activation wire types. A parity check harness can diagnose these exact differences deterministically across model families to identify the root cause.

Can I diagnose activation dtype mismatches across different model families?

Yes, you can diagnose activation dtype mismatches across multiple model families by applying parity checks. The harness examines split boundaries and activation wire types to report exact differences and guide reproduction in CI environments.

How do I reproduce execution parity mismatches in my CI environment?

You can reproduce execution parity mismatches in CI using the integrated harness guidance provided by the deterministic test framework. It applies parity checks across model families and split boundaries to consistently reproduce and fix observed mismatches.

What are the limitations of using a deterministic test harness for parity validation?

The deterministic test harness focuses specifically on diagnosing split boundaries and activation wire type mismatches across model families. It requires both staged and full-model execution environments available to run parity checks and report differences.