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/jeffh/claude-plugins/reviewnpx skills add jeffh/claude-plugins --skill reviewgit clone --depth 1 https://github.com/jeffh/claude-pluginsWrote 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/jeffh/claude-plugins/review)<a href="https://agentmods.dev/skills/jeffh/claude-plugins/review"><img src="https://agentmods.dev/badge/skills/jeffh/claude-plugins/review.svg" alt="Measured on agentmods" 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 | $0.00161 | $0.01321 |
| Opus 5 | $0.00081 | $0.00660 |
| Sonnet 5 | $0.00032 | $0.00264 |
| Haiku 4.5 | $0.00016 | $0.00132 |
Grade A, and why
review 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 4d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Review
Delegate a read-only code review to a Codex subagent running GPT 5.6 Sol (or another GPT model the user names). The subagent reads code and returns findings; it does not write files.
Choosing the model
The -m flag selects the model. Default to gpt-5.6-sol, but honor any specific model the user asks for:
- Use
gpt-5.6-solunless the user names a different model. - If the user specifies a model — e.g. "review with gpt-5.6-terra", "use gpt-5.5-codex", "with the
<name>model" — pass that exact string to-minstead. Don't validate or second-guess the name; Codex will error if it's unknown. - If they typed
/codex:review --model <name> <task>(or-m <name>), strip that flag from the review instructions and use<name>as the model.
Choosing the effort
Reasoning effort is set with -c model_reasoning_effort="<level>". Default to high, but honor any level the user asks for:
- Use
highunless the user names a different level. - If the user asks in prose — e.g. "low effort", "medium effort" — substitute that level.
- If they typed
/codex:review --effort <level> [focus], strip that flag from the review instructions and use<level>as the effort.
The command below shows -m gpt-5.6-sol and high effort; substitute the chosen model and effort.
How to invoke
Codex has a dedicated review subcommand. Pick the target based on what the user wants reviewed:
| User asks to review | Flag |
|---|---|
| Working-tree changes (staged + unstaged + untracked) | --uncommitted |
Everything on this branch vs main (or another branch) |
--base main |
| A specific commit | --commit <SHA> |
| Nothing specified (defaults to current branch vs its merge base) | (no flag) |
Command shape:
codex exec review \
-m gpt-5.6-sol \
-c model_reasoning_effort="high" \
--skip-git-repo-check \
-C "$PWD" \
<target-flag> \
"<REVIEW-PROMPT>"
-m gpt-5.6-sol— the model; defaultgpt-5.6-sol, or the model the user named (see Choosing the model).-c model_reasoning_effort="high"— reasoning effort; defaulthigh, or the level the user named (see Choosing the effort).codex exec review(not plaincodex review) — theexecform is non-interactive and prints to stdout.- No
-s/-aneeded: review mode is inherently read-only. - Always set an explicit Bash
timeout. Reviews are slow and the Bash default (120000 ms / 2 min) will cut Codex off before it finishes. Passtimeout: 600000(10 min — the maximum the Bash tool allows) on everycodex exec reviewcall. For a very large diff that may exceed 10 minutes, run the Bash call withrun_in_background: trueand poll instead, since a foreground call cannot exceed the 600000 ms cap. - If the user did not specify a target, ask once — or default to
--uncommittedif there are uncommitted changes, otherwise--base main(or the repo's default branch).
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.
- 4d ago First seen · 78 lines · 161 tokens per session scan A c89d76bf9992
review is a skill published in the GitHub repository jeffh/claude-plugins (12 stars, last pushed yesterday), licensed Apache-2.0. It adds 161 tokens to every session and 1,321 once invoked, about $0.0008 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
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.
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.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…