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/openai/codex/code-review-contextnpx skills add openai/codex --skill code-review-contextgit clone --depth 1 https://github.com/openai/codexWhat 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 | $0.00007 | $0.00142 |
| Opus 5 | $0.00003 | $0.00071 |
| Sonnet 5 | $0.00001 | $0.00028 |
| Haiku 4.5 | $0.00001 | $0.00014 |
Grade A, and why
code-review-context 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 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.
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.
Copies of this mod
8 near-identical copies found in the catalogue:
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 100% identical, 0 lines differ
- code-review-context — 88% identical, 13 lines differ
What it actually says
Codex maintains a context (history of messages) that is sent to the model in inference requests.
- No history rewrite - the context must be built up incrementally.
- Avoid frequent changes to context that cause cache misses.
- No unbounded items - everything injected in the model context must have a bounded size and a hard cap.
- No items larger than 10K tokens.
- Highlight new individual items that can cross >1k tokens as P0. These need an additional manual review.
- All injected fragments must be defined as structs in
core/contextand implement ContextualUserFragment trait
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 · 13 lines · 7 tokens per session scan A 0af80b6388cc
code-review-context is a skill published in the GitHub repository openai/codex (120,598 stars, last pushed today), licensed Apache-2.0. It adds 7 tokens to every session and 142 once invoked, about $0.0000 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.
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