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 verifygit 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/verify)<a href="https://agentmods.dev/skills/dayfinggg/openai-codex-agent-skills/verify"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/verify/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/verify"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/verify.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 | $0.00047 | $0.00493 |
| Opus 5 | $0.00023 | $0.00246 |
| Sonnet 5 | $0.00009 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
verify 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 3d 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify
Follow the governing instructions and the user's requirements for communication, code style, authorization, and delegation. This skill supplies task-specific guidance, not permission to expand the task. Its workflow and output fields describe internal checks and relevant content, not a mandatory response layout or a progress report. When used within broader authorized work, continue that work through completion rather than stopping to deliver this skill's intermediate result.
Completion is an evidence claim. Select checks that directly support that claim.
Derive the claims
List the observable requirements, preserved invariants, and material failure modes. Map each claim to the strongest practical check. Prefer user-visible execution, integration behavior, or authoritative state over proxies.
Run the checks
Start with focused checks that isolate the changed behavior. Add broader tests, builds, static analysis, or compatibility checks in proportion to the blast radius. Inspect the final diff for unintended files, stale code paths, debug output, and mismatched tests.
Build or package the real artifact when construction or deployment changed, then run a smoke path through that artifact rather than only through source-level tests. For performance claims, preserve the baseline workload, profile the relevant path, and compare repeated measurements after the change.
For replay, retry, durability, or recovery claims, use isolated test state to exercise relevant interruptions and recovery, then verify duplicate and partial outcomes. Do not interrupt live operations, alter production data, or introduce failure injection without explicit authorization.
For security-sensitive delivery, check the existing artifact provenance and release controls relevant to the change. Exercise restore, failover, revocation, or key rotation only when these behaviors are in scope and an isolated environment or specific authorization is available. Do not turn routine verification into a production resilience exercise.
What ships with it
1 file 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.
- 3d ago Changed · +2 lines ebdb26277951
- 4d ago Changed · +6 lines 08ae6abeb7c7
- 10d ago First seen · 27 lines · 47 tokens per session scan A 9ac817a3526b
verify is a skill published in the GitHub repository dayfinggg/openai-codex-agent-skills (4 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 493 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
verification-loop
This skill should be used when the user asks to "verify code", "run verification", "check quality", "validate changes", or before creating a PR. Provides comprehensive verification including build, type check, lint, tests, security scan, and diff review.
sc-skill
Capture deterministic macOS screenshots for testing, docs, release notes, and marketing assets. Use when asked to automate app screenshots, batch-generate screenshot sets, standardize window sizing/composition, or choose between Peekaboo and native macOS screenshot tooling.
agent-session-format-check
Verify agent session format compatibility for Agent Sessions. Use when any agent CLI updates, when monitoring flags drift, or when bumping max verified versions (fixtures + docs + tests). Covers session schema, usage/limits tracking, storage backends, and discovery path contracts for all supported agents.
intuitive-tests
Use this skill whenever the user asks about unit test best practices, test organization, flat test suites, redundant tests, test refactors, pytest/JUnit/Jest/xUnit layout, test taxonomy, flaky tests, coverage quality, fixtures, mocks, parametrization, pruning existing UTs, or "which tests are worth keeping." It…
qa
Visual + a11y QA — screenshot-first critique, contrast, touch targets, mockup-vs-impl diff. For adversarial logic checks use /verify. Triggers "visual QA", "QA check", "does this look right", "a11y check", "contrast check", post-component changes.
verify
Adversarial verification — three competing agents (issue-finder, disprover, judge). Triggers "verify", "double check", "are you sure", "poke holes"; pre-prod, post-critical-fix.