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agentsop-per-model-artifacts

Enforce versioned lifecycles for compiled prompt and model-specific configuration artifacts.

287|16|Updated May 20, 2026
Search Tags:#DSPy#LlamaIndex#artifact registry#prompt versioning#model snapshot#CI regression gate#swap testing
One-click install
npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-per-model-artifacts
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentsop-per-model-artifacts
Source: https://github.com/agentsope/SkillAlchemy/tree/main/skills/agentsop-per-model-artifacts
Command: npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-per-model-artifacts

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It solves the risk of shipping degraded behavior when compiled prompts, edit-format settings, or embedding-bound indices silently break after a model snapshot change or a framework/provider deprecation.

Core Features & Use Cases

  • Per-(program × LM snapshot × dataset) artifact lifecycle: encode the identity of compiled prompts and model-specific configuration so behavior is auditable and reproducible.
  • Regression gate + swap-test workflow: require held-out-eval comparisons before approving artifact replacements and before tagging production-ready upgrades.
  • Deprecation-aware maintenance: run deprecation scans and enforce rules that prevent alias pinning and prompt/config drift without verification.

Quick Start

Activate this SOP by telling an AI assistant to help you structure your compiled prompt artifacts with dated model snapshots and dataset hashes, then add a regression gate that fails PRs when held-out scores drop beyond your threshold.

Frequently Asked Questions about agentsop-per-model-artifacts

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

FAQPage Schema
How does a CI regression gate work for compiled prompt versioning?▼

A CI regression gate for prompt versioning fails pull requests when held-out evaluation scores drop beyond a configured threshold during swap-tests, preventing degraded behavior from shipping after a model snapshot change or framework deprecation.

What is the best way to manage multi-LM production deployments without behavior drift?▼

To prevent LlamaIndex embedding-bound indices from breaking during provider deprecations, run deprecation scans and enforce rules that prevent alias pinning and prompt or config drift without explicit verification against snapshot pinning requirements.

What is the best way to manage multi-LM production deployments without behavior drift?▼

To prevent LlamaIndex embedding-bound indices from breaking during provider deprecations, run deprecation scans and enforce rules that prevent alias pinning and prompt or config drift without explicit verification against snapshot pinning requirements.

When should I recompile a compiled prompt versus transfer it to a new model snapshot?▼

To version prompts for safe model swaps in DSPy, enforce a per-(program × LM snapshot × dataset) artifact lifecycle by encoding compiled prompt identity with dated model snapshots and dataset hashes to ensure behavior remains auditable and reproducible across changes.

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