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 agentsope/SkillAlchemy --skill agentsop-regression-gategit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-regression-gate)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-regression-gate"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-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/agentsope/skillalchemy/agentsop-regression-gate"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-regression-gate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00062 | $0.06510 |
| Opus 5.5 | $0.00025 | $0.02604 |
| Sonnet 5.5 | $0.00012 | $0.01302 |
| Haiku 4.5 | $0.00006 | $0.00651 |
Grade A, and why
agentsop-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
regression-gate — Eval Set + Metric + Threshold, Wired Into CI
"Every subsequent change must be gated on these numbers." — Synthesized from [[llamaindex]] Stage 2 (eval loop before optimizing) [llamaindex-sop-skill/SKILL.md:114-126]
"Compiled program beats baseline on a held-out test set (not the val set used in optimization)." — [[dspy]] Stage 3 exit criterion [dspy-sop-skill/SKILL.md:101]
This is an enhancement overlay. The regression-gate SOP exists only as fragments scattered across base skills — [[llamaindex]] OP-10 EvalLoop ("gate every change"), [[dspy]] train/dev/test split + metric — and is never assembled as a standalone cross-framework discipline. It is the discipline that turns a one-off eval into a gate: a test suite that runs in CI on every prompt/model/retriever change and fails the build on regression. It consumes a metric from [[agentsop-metric-design]] and, for domain-specific held-out sets, hands off to [[agentsop-domain-eval-set]].
1. 何时激活 (When to Activate)
Activate when any of these is true:
- Any prompt change you want to ship safely: a prompt edit, a system-message tweak, a few-shot-demo swap is about to merge and you have no automated way to know if it made things worse.
- Any model change: swapping GPT-4o → a cheaper/newer model, a temperature change, a provider migration. An LM change silently shifts the whole output distribution. [[dspy]] Case B: "If you optimize a complex pipeline for GPT-4, it usually breaks on a smaller model" [dspy-sop-skill/SKILL.md:184].
- Any retriever/chunking/reranker change in a RAG pipeline: every such change needs a quantitative gate. [[llamaindex]]
OP-10: "Quantitative regression test for every chunking / embedding / retriever / prompt change" [llamaindex-sop-skill/SKILL.md:235]. - Recurring "it got worse" surprises: the team keeps shipping changes that users report as regressions after the fact. The fix is a gate, not more careful review.
- Setting up CI for an LLM app and there is no eval job in the pipeline.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 279 lines · 62 tokens per session scan A 00216a76dd9a
agentsop-regression-gate is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 62 tokens to every session and 6,510 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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github-actions-dataops
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Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
quality-scan
Run a scoped, read-only quality scan and report exact-candidate evidence, failures and frozen debt. Use for an ad-hoc gate sweep or release preparation; a static scan is not full release acceptance.
e2e-testing
Use when reviewing CI coverage, automated checks, or test strategy related to Implement end-to-end testing. Focus on whether the rule is continuously verified, not just documented.
test-setup
Scaffold the test framework and CI — tests/ directory, engine test runner, GitHub Actions workflow. Once, before the first sprint.