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 alivirgo/Major-AI-Skills --skill prompt-regression-gategit clone --depth 1 https://github.com/alivirgo/Major-AI-SkillsWrote 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/alivirgo/major-ai-skills/prompt-regression-gate)<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/prompt-regression-gate"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/prompt-regression-gate/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/alivirgo/major-ai-skills/prompt-regression-gate"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/prompt-regression-gate.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 | $0.00029 | $0.00307 |
| Opus 5 | $0.00015 | $0.00153 |
| Sonnet 5 | $0.00006 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
prompt-regression-gate 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 today.
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.
What it actually says
Prompt Regression Gate
Scope
Capture baseline and candidate prompts, model identifiers, tool schemas, retrieval revision, and generation settings. Agree on acceptance thresholds before running the comparison. Reuse a held-out evaluation set rather than tuning against final test failures.
Procedure
Run paired cases with equivalent context. If outputs are stochastic, repeat cases enough to expose variability within the agreed budget. Record missing runs and provider errors rather than treating them as ordinary wrong answers.
Checks
Use deterministic assertions for structured tasks and rubric-based review for subjective ones. Blind human reviewers to candidate identity where practical. Never use the candidate model's self-confidence as the sole quality metric.
Failure Handling
Compare aggregate results and important slices, including refusals, ambiguous requests, and high-cost mistakes. Report uncertainty and sample size; a small average gain must not hide a critical regression.
Deliverable
Deliver a go/no-go recommendation tied to predefined thresholds, failing examples, and rollback instructions. Without executed runs, provide the gate configuration and mark the result untested.
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.
- today First seen · 35 lines · 29 tokens per session scan A b66dcbe3b575
prompt-regression-gate is a skill published in the GitHub repository alivirgo/Major-AI-Skills (1 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 307 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.
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