claude-codex-settings is a collection of configurations and reusable extensions for Claude Code, OpenAI Codex, Cursor, and related coding tools. Developers use its skills, commands, hooks, agents, plugins, and MCP servers to shape coding-agent workflows and connect alternative model APIs. The catalogue entries are components of this collection that can be installed into supported coding tools.
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 fcakyon/claude-codex-settings --skill test-auditgit clone --depth 1 https://github.com/fcakyon/claude-codex-settingsWrote 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/fcakyon/claude-codex-settings/test-audit)<a href="https://agentmods.dev/skills/fcakyon/claude-codex-settings/test-audit"><img src="https://agentmods.dev/badge/skills/fcakyon/claude-codex-settings/test-audit/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/fcakyon/claude-codex-settings/test-audit"><img src="https://agentmods.dev/badge/skills/fcakyon/claude-codex-settings/test-audit.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 | $0.00079 | $0.01576 |
| Opus 5.5 | $0.00032 | $0.00630 |
| Sonnet 5.5 | $0.00016 | $0.00315 |
| Haiku 4.5 | $0.00008 | $0.00158 |
Grade A, and why
test-audit 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 15d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Audit
Three modes, one value bar. Authoring mode gates every new or changed test at write time. Audit mode runs focused sweeps of tests that re-assert source, duplicate stronger proof, couple behavior to implementation, or keep test-only production seams alive. Continue broad audits as separate coherent follow-up PRs; optimize for confidence, not deletion count. Campaign mode prunes one whole subsystem's test surface (every test file a plugin or core area owns); before starting one, read references/campaign.md.
Authoring gate
Before adding any test, answer four questions; a missing answer means do not add it yet:
- What observable behavior, invariant, or independent contract does it protect?
- What credible regression makes it fail?
- Why does existing coverage not already catch that failure? Each contract has one primary test owner at the strongest boundary; another layer needs its own distinct risk, such as a transport or lifecycle failure the owner cannot reach. Prefer extending a table-driven case or shared fixture over a near-duplicate test; consolidate duplicated setup in the same change.
- Does it need a production seam (export, flag, wrapper, injection hook) that no production caller needs? If yes, move the test to the real boundary instead.
Then check the test against every junk pattern; a match fails the gate unless the retention bar names the contract it independently guards. A test that would break under behavior-preserving refactoring is asserting implementation, not behavior; rewrite it at the owning boundary before landing it.
Bug regression tests must fail on the pre-fix code for the intended reason and pass after the owner-boundary repair. A regression test that never demonstrably failed proves the mock, not the fix. One regression at the owner boundary covers the bug; do not replay the same scenario at every layer it crosses.
Junk patterns
The shared checklist for both modes: the authoring gate rejects a new test that matches one, and audits hunt for existing tests that do.
What ships with it
2 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.
- 15d ago First seen · 166 lines · 79 tokens per session scan A 0056f0287e45
test-audit is a skill published in the GitHub repository fcakyon/claude-codex-settings (1,178 stars, last pushed yesterday), licensed Apache-2.0. It adds 79 tokens to every session and 1,576 once invoked, about $0.0003 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 2026-10-01.
Other skills, from other repositories
update-package-skill
Updates an existing package Skill for a new release of its npm or local package by testing only what changed since the version the Skill names. Use when a maintainer asks to refresh a package Skill after a release, or when a SKILL.md names an older tested version than the package.
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lov-runtime-verification
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lov-wechat-parity-validation
A review workflow for checking whether a WeChat compatibility fix works across data parsing, shared meaning, interfaces, downstream uses, and the real user path. It treats regression checks as proof that the fix is complete.
lov-fix-until-no-error
A verification-focused debugging workflow that repeatedly runs specified checks, fixes the highest-priority failure, and stops when the checks pass or a genuine external blockage remains.
lov-atom-operations
A checklist and implementation workflow for making a completed feature ready to explain, test, find, authorize, sell, measure, and maintain. It decides which supporting work applies instead of adding every integration by default.