Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lingxling/awesome-skills-cn --skill ai-prompt-regression-testinggit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lingxling/awesome-skills-cn/ai-prompt-regression-testing)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/ai-prompt-regression-testing"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/ai-prompt-regression-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/ai-prompt-regression-testing"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/ai-prompt-regression-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.01220 |
| Opus 5.5 | $0.00011 | $0.00488 |
| Sonnet 5.5 | $0.00006 | $0.00244 |
| Haiku 4.5 | $0.00003 | $0.00122 |
Grade A, and why
ai-prompt-regression-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-prompt-regression-testing — 188 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Define the parameters, thresholds, and identity bindings required for the target operational register.
- Select appropriate boundary enforcement values from validated enum select sets.
- Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.
Field Reference
| # | Field Name | Type | SQL Type | JSON Schema Type | Notion Property Type | Example Value |
|---|---|---|---|---|---|---|
| 1 | Prompt TestCase ID | id |
SERIAL PRIMARY KEY |
integer |
Text | PTEST-001 |
| 2 | Prompt Identifier | text |
VARCHAR(64) |
string |
Text | soroban_code_refactor_v2 |
| 3 | Target LLM Model Family | select |
VARCHAR(64) |
string |
Select | Claude 3.5 Sonnet |
| 4 | Evaluation Metric | select |
VARCHAR(64) |
string |
Select | AST Code Correctness |
| 5 | Semantic Drift Threshold | number |
NUMERIC(5,2) |
number |
Number | 0.05 |
| 6 | Golden Baseline Match % | number |
NUMERIC(5,2) |
number |
Number | 98.50 |
| 7 | Judge Model Evaluator | text |
VARCHAR(64) |
string |
Text | Gemini 1.5 Pro |
| 8 | Zero-Shot Reasoning Verified | select |
VARCHAR(16) |
string |
Select | Yes |
| 9 | Latency Bound Seconds | number |
NUMERIC(6,2) |
number |
Number | 3.20 |
| 10 | Test Suite Verdict | select |
VARCHAR(32) |
string |
Select | Passed |
| 11 | Benchmarking Date | date |
DATE |
string, format: date |
Date | 2026-10-01 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 131 lines · 28 tokens per session scan A bd48f8169228
ai-prompt-regression-testing is a skill published in the GitHub repository lingxling/awesome-skills-cn (310 stars, last pushed 9d ago), licensed MIT. It adds 28 tokens to every session and 1,220 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ai-prompt-regression-testing, differing in 188 lines, and is treated as a copy.
Other skills, from other repositories
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.
llm-evaluator
Evaluate LLM outputs systematically using LLM-as-judge, human evaluation frameworks, and regression testing. Use when assessing model quality, comparing models, or preventing quality regression.
rag-evaluator
Evaluate RAG pipeline quality across faithfulness, relevance, and hallucination metrics. Use when user asks to test, benchmark, or improve a RAG system, or when RAG outputs look wrong.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
llm-engineering-expert
Build reliable applications on large language models: prompt design, structured output, evaluation, guardrails, and cost and latency control. Use when the user mentions LLMs, prompts, prompt engineering, few-shot examples, structured or JSON output, function calling, hallucination, model evaluation, token costs…