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 patricio0312rev/skillset --skill prompt-regression-testergit clone --depth 1 https://github.com/patricio0312rev/skillsetWrote 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/patricio0312rev/skillset/prompt-regression-tester)<a href="https://agentmods.dev/skills/patricio0312rev/skillset/prompt-regression-tester"><img src="https://agentmods.dev/badge/skills/patricio0312rev/skillset/prompt-regression-tester/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/patricio0312rev/skillset/prompt-regression-tester"><img src="https://agentmods.dev/badge/skills/patricio0312rev/skillset/prompt-regression-tester.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 | $0.00052 | $0.01381 |
| Opus 5 | $0.00026 | $0.00691 |
| Sonnet 5 | $0.00010 | $0.00276 |
| Haiku 4.5 | $0.00005 | $0.00138 |
Grade A, and why
prompt-regression-tester 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 12d 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 prompt-regression-tester — 0 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Regression Tester
Systematically test prompt changes to prevent regressions.
Test Case Format
{
"test_cases": [
{
"id": "test_001",
"input": "Summarize this article",
"context": "Article text here...",
"expected_behavior": "Concise 2-3 sentence summary",
"baseline_output": "Output from v1.0 prompt",
"must_include": ["main point", "conclusion"],
"must_not_include": ["opinion", "speculation"]
}
]
}
Comparison Framework
def compare_prompts(old_prompt, new_prompt, test_cases):
results = {
"test_cases": [],
"summary": {
"total": len(test_cases),
"improvements": 0,
"regressions": 0,
"unchanged": 0,
},
"breakages": []
}
for test in test_cases:
old_output = llm(old_prompt.format(**test))
new_output = llm(new_prompt.format(**test))
comparison = {
"test_id": test["id"],
"old_output": old_output,
"new_output": new_output,
"diff": compute_diff(old_output, new_output),
"scores": {
"old": score_output(old_output, test),
"new": score_output(new_output, test),
},
"verdict": classify_change(old_output, new_output, test)
}
results["test_cases"].append(comparison)
results["summary"][comparison["verdict"]] += 1
if comparison["verdict"] == "regressions":
results["breakages"].append(analyze_breakage(comparison, test))
return results
Stability Metrics
def calculate_stability_metrics(results):
return {
"output_stability": measure_output_consistency(results),
"format_stability": check_format_preservation(results),
"constraint_adherence": check_constraints(results),
"behavioral_consistency": measure_behavior_delta(results),
}
def measure_output_consistency(results):
"""How similar are outputs between versions?"""
similarities = []
for result in results["test_cases"]:
sim = semantic_similarity(
result["old_output"],
result["new_output"]
)
similarities.append(sim)
return sum(similarities) / len(similarities)
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.
- 12d ago First seen · 217 lines · 52 tokens per session scan A 718287a23607
prompt-regression-tester is a skill published in the GitHub repository patricio0312rev/skillset (6 stars, last pushed 8mo ago), licensed MIT. It adds 52 tokens to every session and 1,381 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prompt-regression-tester, differing in 0 lines, and is treated as a copy.
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