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 agentmods add skills/leejuoh/claude-code-zero/codex-reviewnpx skills add LeeJuOh/claude-code-zero --skill codex-reviewgit clone --depth 1 https://github.com/LeeJuOh/claude-code-zeroWhat 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 | $0.00053 | $0.02816 |
| Opus 5 | $0.00026 | $0.01408 |
| Sonnet 5 | $0.00011 | $0.00563 |
| Haiku 4.5 | $0.00005 | $0.00282 |
Grade B, and why
codex-review scanned grade B with 1 finding 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 2d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
`--model` and `--effort` route through `scripts/apply-codex-config.py` to update `~/.codex/config.toml` *before* the companion launches — see the Apply block below. Two reasons: How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Code Review + Double-Check
You are a translator + executor + double-checker. The user can type anything — flags, Korean, English, meta-instructions, emoji. Your first job is to figure out intent and produce a clean invocation of the Official Codex plugin's companion. Your second job is to double-check what Codex returns, without biasing yourself by reading the diff first.
Execution Contract
This contract overrides default exploration habits. Read it before Phase 1.
| Phase | Allowed | Forbidden |
|---|---|---|
| 1 ANALYZE | test -f/-s/-d, git rev-parse --verify, git branch --list, wc -l/-c, file, echo, printf |
cat, head, tail, git diff, git log -p, git show, git blame, Read, Grep, Glob |
| 2 INVOKE | Bash for companion launch (multi-arg form only — never $ARGUMENTS blob) |
All source reads |
| 3 WAIT | BashOutput |
All source reads, manual polling, ps/kill outside KillShell |
| 4 DOUBLE-CHECK | Read ONLY files/lines Codex cited | Reading whole files "for context"; reading uncited files; inventing citations |
| 5 REPORT + SAVE | Write report file | n/a |
The companion collects the diff and context itself. Your value-add is
the double-check, not pre-analysis. Unknown flags are silently joined
into the prompt by the companion (lib/args.mjs:47-49 + :643-650) —
there is NO post-hoc detection. Phase 1 whitelist is the only safety net.
Phase 1: Analyze
You are a translator. Use LM intelligence, not regex tables.
Whitelist for this skill: --base <ref>, --scope <auto|working-tree|branch>, --model <slug>, --effort <level>. Nothing else.
--model and --effort route through scripts/apply-codex-config.py to update ~/.codex/config.toml before the companion launches — see the Apply block below. Two reasons:
--effortis not a registered review flag (handleReviewCommandvalueOptions = ["base", "scope", "model", "cwd"]at:714). Passing--effortdirectly would become silent prompt corruption (references/companion-usage.md §3). Only the config.tomlmodel_reasoning_effortkey reaches the review path.- Consistency + persistence.
--modelIS honored as a flag in v1.0.4+ (startThread({ model }),lib/codex.mjs:1010-1015), but routing it through config.toml keeps every codex-advisor skill identical and lets the value persist for the next session without re-typing.
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.
- 2d ago First seen · 216 lines · 53 tokens per session scan B 199faff5f4a2
codex-review is a skill published in the GitHub repository LeeJuOh/claude-code-zero (51 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 2,816 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.