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Ai Prompt Regression Testing

CommunityPopular
sickn33
ai-prompt-regression-testing

Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.

Overview

Publishersickn33
Repositoryagentic-awesome-skills
Skill nameai-prompt-regression-testing
Stars
47.5K
Forks
6.9K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sickn33 on GitHub. Read the source before you install it.

Installation

Install the Ai Prompt Regression Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/ai-prompt-regression-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/ai-prompt-regression-testing .claude/skills/ai-prompt-regression-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Prompt Regression Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Prompt Regression Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Prompt Regression Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI Prompt Regression Test Matrix

What it is: Tracks regression baselines, evaluation rubrics, and automated judge verdicts to prevent output degradation across prompt revisions.

Overview

Provides a standardized, auditable framework and data model for AI Prompt Regression Test Matrix operations across distributed engineering and decentralized application systems.

When to Use This Skill

  • When formalizing architectural contracts, security invariants, or operational limits for AI Prompt Regression Test Matrix.
  • When cross-functional review is required between protocol developers, smart contract auditors, and AI engineering agents.
  • When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.

How It Works

  1. Define the parameters, thresholds, and identity bindings required for the target operational register.
  2. Select appropriate boundary enforcement values from validated enum select sets.
  3. Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.

Field Reference

#Field NameTypeSQL TypeJSON Schema TypeNotion Property TypeExample Value
1Prompt TestCase IDidSERIAL PRIMARY KEYintegerTextPTEST-001
2Prompt IdentifiertextVARCHAR(64)stringTextsoroban_code_refactor_v2
3Target LLM Model FamilyselectVARCHAR(64)stringSelectClaude 3.5 Sonnet
4Evaluation MetricselectVARCHAR(64)stringSelectAST Code Correctness
5Semantic Drift ThresholdnumberNUMERIC(5,2)numberNumber0.05
6Golden Baseline Match %numberNUMERIC(5,2)numberNumber98.50
7Judge Model EvaluatortextVARCHAR(64)stringTextGemini 1.5 Pro
8Zero-Shot Reasoning VerifiedselectVARCHAR(16)stringSelectYes
9Latency Bound SecondsnumberNUMERIC(6,2)numberNumber3.20
10Test Suite VerdictselectVARCHAR(32)stringSelectPassed
11Benchmarking DatedateDATEstring, format: dateDate2026-10-01

Select Options

Target LLM Model Family

Claude 3.5 Sonnet | GPT-4o | Gemini 1.5 Pro | DeepSeek Coder

Evaluation Metric

AST Code Correctness | Semantic Embedding Cosine | Exact Match | Rubric Scoring

Zero-Shot Reasoning Verified

Yes | No

Test Suite Verdict

Passed | Degraded | Failed Regression

Relations

  • Audit Reference -> links to the formal review documentation or test repository.
  • Target Architecture -> links to the deployed contract or autonomous agent runtime component.

Examples

Prompt

How do I configure and track AI Prompt Regression Test Matrix for our production environment?

Recommended Next Step

Generate the unified field schema, SQL DDL migration, and JSON validation schema to register into your system catalog.

Workflow: Define criteria -> Run automated verification -> Record baseline -> Monitor invariants.

Best Practices

  • Enforce strict typing on numerical bounds and currency amounts; avoid unstructured free-text fields for critical states.
  • Re-run validation test suites on every state-altering commit or parameter change.
  • Keep example data synthetic and isolated from production cryptographic keys or private endpoints.

Limitations

  • Provides architectural specifications, data models, and verification schemas; does not execute direct transaction signing without authorized external tooling.
  • Requires network connectivity and valid RPC credentials when querying on-chain states.

Security & Safety Notes

  • All parameters declare risk: safe. No unauthorized state modification or privileged credential access is performed.
  • Use synthetic dummy keys and mock addresses in test suites and local verification scripts.

Common Pitfalls

  • Problem: Mismatched decimal precision between contract runtime and database register. Solution: Always verify decimals using the explicit field mapping in this reference.
  • Problem: Missing authorization checks prior to state update. Solution: Cross-validate against the Security Audit register before deployment.

Related Skills

  • @ai-agent-tool-routing - covers tool schema registration and retry policy.
  • @cross-chain-relayer-audit - covers message hashes, nonces and quorum proofs.
  • @smart-contract-formal-verification - verifies state invariants mathematically.

Reusable Prompt

I want to establish a verified AI Prompt Regression Test Matrix register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.

Frequently asked questions

What does the Ai Prompt Regression Testing AI skill do?

Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.

Why use Ai Prompt Regression Testing on TypingMind?

Because you install it once and use it with any model. Ai Prompt Regression Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Prompt Regression Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/ai-prompt-regression-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ai Prompt Regression Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Prompt Regression Testing?

As many as you like. As long as a model supports skills, you can use Ai Prompt Regression Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Prompt Regression Testing AI skill free?

Yes. It is published on GitHub by sickn33 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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