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 openshift-eng/ai-helpers --skill deep-reviewgit clone --depth 1 https://github.com/openshift-eng/ai-helpersWrote 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/openshift-eng/ai-helpers/deep-review)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/deep-review"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/deep-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/skills/openshift-eng/ai-helpers/deep-review"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/deep-review.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 | $0.00049 | $0.03644 |
| Opus 5 | $0.00024 | $0.01822 |
| Sonnet 5 | $0.00010 | $0.00729 |
| Haiku 4.5 | $0.00005 | $0.00364 |
Grade A, and why
deep-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 5d 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 — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Review — Multi-Specialist Panel Review with Reproducers
Review a branch's changes with parallel specialist subagent reviewers, each examining the code through a different lens. Verify every bug finding with a runtime reproducer. Optionally post to GitHub/GitLab as a PENDING review.
No PR/MR is required — the review works on any branch with commits ahead of its base.
Two execution modes:
- Parallel (default): Each specialist runs as a dedicated sub-agent concurrently. Thorough but expensive — each sub-agent independently derives its own view of the codebase.
- Serial (
--serial): All specialists run inline in the main agent, one after another. Significantly cheaper because the codebase context is derived once and shared across all specialists. Trade-off: reviews run sequentially, and later specialists can see prior specialists' findings (which may bias their analysis).
Arguments
/code-review:deep-review [flags] [pr-url-or-number]
| Argument | Description |
|---|---|
--serial |
Run all specialists inline instead of as parallel sub-agents |
--comment |
Post the verdict as a PR comment after review. Requires a PR identifier |
--coderabbit |
Include CodeRabbit as an external reviewer |
--codex |
Include OpenAI Codex as an external reviewer |
-reviewer |
Exclude a specialist (e.g., -writer,-qa). All enabled by default |
| pr identifier | GitHub/GitLab PR URL or bare PR number. Optional |
Examples:
/code-review:deep-review— all reviewers, review current branch/code-review:deep-review --serial— cheaper serial mode/code-review:deep-review -qa,-writer— skip QA and Technical Writer/code-review:deep-review --comment 42— review PR #42, post verdict as comment/code-review:deep-review --coderabbit https://github.com/org/repo/pull/42/code-review:deep-review https://gitlab.com/org/repo/-/merge_requests/7
Specialist Panel
All are enabled unless excluded with -:
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/pr-posting.md 3.6 KB
- references/reproducer-prompt.md 1.9 KB
- references/specialists/adversarial.md 1.7 KB
- references/specialists/architecture.md 1.1 KB
- references/specialists/bugs.md 1.1 KB
- references/specialists/consistency.md 1.3 KB
- references/specialists/qa.md 1.1 KB
- references/specialists/security.md 1.6 KB
- references/specialists/writer.md 1.1 KB
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.
- 5d ago First seen · 377 lines · 49 tokens per session scan A 29da64b905a4
deep-review is a skill published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 3,644 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 2026-09-03.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…