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.
git clone --depth 1 https://github.com/redhat-community-ai-tools/UnifAIWrote 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/commands/redhat-community-ai-tools/unifai/review)<a href="https://agentmods.dev/commands/redhat-community-ai-tools/unifai/review"><img src="https://agentmods.dev/badge/commands/redhat-community-ai-tools/unifai/review/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/commands/redhat-community-ai-tools/unifai/review"><img src="https://agentmods.dev/badge/commands/redhat-community-ai-tools/unifai/review.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 | $0.00000 | $0.00260 |
| Opus 5 | $0.00000 | $0.00130 |
| Sonnet 5 | $0.00000 | $0.00052 |
| Haiku 4.5 | $0.00000 | $0.00026 |
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 11d 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
This command might include optional parameters basic / deep - this is the type of review to invoke if no parameter is given for that option the default should be basic files/folders - if file or folder specification is given only review them.
following my developing a new feature please follow these steps:
- go over the code, and get all the changes.
- (only if deep review) prepare a design you can use as a reference, in addition where the folder containing parts of the new feature includes an architecture.md file use it as reference as well.
- review the code added in this branch and gather all your comments in a file named <branch_name>_<review_type>_review.md (where branch name is the actual branch name you checked). pay close attention to the design and make sure it's not being broken in any way the review file should be built like this:
- list of issues found split by areas and severity (area first)
- at the top a short overview of the review and the purpose of the feature as you understood it
- if a design is available add it to the file (if you created the design by running deep review add it as is if the design is part of the added files to the branch just specify its location)
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.
- 11d ago First seen · 13 lines · 0 tokens per session scan A 97748d5e5621
review is a command published in the GitHub repository redhat-community-ai-tools/UnifAI (44 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 260 tokens. 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 commands, from other repositories
review-pr
PR review using parallel specialized agents for code quality, security, testing, architecture, and performance analysis. Synthesizes findings into a review report with conventional comments (praise/issue/suggestion/nitpick) and approve or request-changes verdict. Use when reviewing pull requests, conducting security…
afe
Evaluate feature - code review or comparison (shortcut for feature-eval).
factory-ticket
Implement exactly one already-claimed Linear ticket in the current worktree.
mach12:issue-review
Read a GitHub issue and all comments, review the implementation plan, and present findings.
mach12:pr-review-assessment
Independently assess each finding from a PR review and classify it.
mach12:gh-pr-read
Read a GitHub pull request's title, body, and all top-level PR conversation comments; optionally locate an HTML-marker comment.