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 dayfinggg/openai-codex-agent-skills --skill regression-verificationgit clone --depth 1 https://github.com/dayfinggg/openai-codex-agent-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/dayfinggg/openai-codex-agent-skills/regression-verification)<a href="https://agentmods.dev/skills/dayfinggg/openai-codex-agent-skills/regression-verification"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/regression-verification/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/dayfinggg/openai-codex-agent-skills/regression-verification"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/regression-verification.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 | $0.00033 | $0.00402 |
| Opus 5.5 | $0.00013 | $0.00161 |
| Sonnet 5.5 | $0.00007 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
regression-verification 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
Verify the requested behavior without creating redundant tests or claiming unperformed checks.
- Recover the requirement, changed contract, affected consumers, and original defect evidence. Read the diff and existing checks. Preserve unrelated user changes.
- Identify the smallest checks capable of failing when the requested behavior is wrong. Choose unit, integration, runtime, configuration, or inspection according to the actual risk. A test that mirrors implementation is not independent evidence.
- For a defect, verify that the reproduction exercises the reported failure. If an old revision can be tested safely, observe it fail for the right reason. Do not alter the user's worktree to obtain a baseline.
- Run existing relevant checks with the project's configured tools and versions. Confirm target selection, executed counts, meaningful assertions, and nonzero failures. An empty test run or linter-only pass does not prove runtime behavior.
- Exercise changed failure, cancellation, concurrency, or data-compatibility paths only when the change affects them. Do not add a broad hypothetical test matrix.
- Fix regressions introduced by the change. Distinguish unrelated pre-existing failures and do not modify them solely to obtain a green summary.
- After passing checks, broaden or repeat only for new edits, failures, or a specific unresolved concern. Inspect the final diff for scope, generated files, secrets, and temporary materials.
- Remove verified task-owned temporary fixtures and helpers when no process needs them. Retain requested deliverables and permanent tests that add meaningful coverage.
Finish once the requirements and relevant checks are satisfied. Report what changed, the performed validation, and only material limitations. Keep explanatory reports in chat unless a standalone document is requested.
For skill development, structural validity and host discovery are different from measured improvement on real engineering tasks. Preserve that distinction in completion claims.
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 · 21 lines · 33 tokens per session scan A faf9e851007c
regression-verification is a skill published in the GitHub repository dayfinggg/openai-codex-agent-skills (8 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 402 once invoked, about $0.0001 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 2026-10-30.
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