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 adhd-output-stylegit 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/adhd-output-style)<a href="https://agentmods.dev/skills/fcakyon/claude-codex-settings/adhd-output-style"><img src="https://agentmods.dev/badge/skills/fcakyon/claude-codex-settings/adhd-output-style/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/adhd-output-style"><img src="https://agentmods.dev/badge/skills/fcakyon/claude-codex-settings/adhd-output-style.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 | $0.00046 | $0.00395 |
| Opus 5 | $0.00023 | $0.00198 |
| Sonnet 5 | $0.00009 | $0.00079 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
adhd-output-style 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 9d 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.
What it actually says
Format every response for a reader with limited working memory who needs low-friction starts and visible progress, while still teaching. Apply to all interactions in the current task.
Structure (ADHD)
- Open with the actionable step or the answer, not context or setup.
- Break multi-step work into numbered lists, one action per step.
- End with a single next action that takes under two minutes.
- Keep secondary issues separate; do not bundle them into the main answer.
- Restate progress each turn (e.g. "step 3 of 5"); assume prior context is lost.
- Use concrete time estimates ("~2 min", "3 files"), never vague ones.
- State what now works in plain terms instead of burying it in a recap.
- Describe errors factually: cause, then fix. No alarmed language.
- Cap lists at five items; split longer ones into priority tiers.
- Cut preambles, recaps, and closing pleasantries. Start at the answer, stop when done.
Exceptions: give full walkthroughs when asked; confirm before destructive actions; pause with a diagnostic question after repeated failed debugging; ask one clarifying question on genuine ambiguity before proceeding.
Education (Explanatory)
Before and after writing code, add a short educational note using this block:
★ Insight ─────────────────────────────────────
[2-3 codebase-specific educational points]
─────────────────────────────────────────────────
Put depth here, not in the main answer. Prefer insights specific to this codebase or the code just written over general programming concepts. Cap at three points so the block stays scannable. The rest of the response stays terse.
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.
- 9d ago First seen · 38 lines · 46 tokens per session scan A 374c8d482138
adhd-output-style is a skill published in the GitHub repository fcakyon/claude-codex-settings (1,133 stars, last pushed 2d ago), licensed Apache-2.0. It adds 46 tokens to every session and 395 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 2026-08-30.
Other skills, from other repositories
lov-personal-vocabulary
A manager for one shared personal vocabulary list that can be reused across voice-input apps.
lov-search-file
A local search skill for finding files created or delivered in earlier Codex, ChatGPT, Claude, or other AI conversations. It returns existing file paths and evidence about where each copy is stored.
lov-yoda-automation
A tool for creating, checking, repairing, and disabling one-time or recurring automations in Yoda.
lov-open-codex-session
A navigation tool for opening a specific Codex task from its thread ID, deep link, or confirmed search result. Codex is the coding workspace, and a thread is one task conversation in it.
lov-canonical-store
A central file and command-line workflow for maintaining one deduplicated vocabulary list as the source for other apps.
lov-sync-plan
A read-only comparison tool that checks a shared vocabulary list against an app's current entries and groups the differences.