Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add faeton/claude-grok-plugin/plugin install grokWrote 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/faeton/claude-grok-plugin/adversarial-review)<a href="https://agentmods.dev/commands/faeton/claude-grok-plugin/adversarial-review"><img src="https://agentmods.dev/badge/commands/faeton/claude-grok-plugin/adversarial-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 5.1 | $0.00017 | $0.00294 |
| Opus 5 | $0.00009 | $0.00147 |
| Sonnet 5 | $0.00003 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
adversarial-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 8d 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
Run a Grok review with extra adversarial focus instructions. Same read-only guarantees as /grok:review; do not fix issues in this turn.
Raw slash-command arguments:
$ARGUMENTS
- Any non-flag text is the focus instruction for the reviewer (e.g. "hunt for race conditions in the new worker pool"). It is forwarded as the trailing positional text.
--scope,--base,--modelpass through.- Execution mode (
--wait/--background/ ask once) follows the same rules as/grok:review.
Foreground:
node "${CLAUDE_PLUGIN_ROOT}/scripts/grok-companion.mjs" adversarial-review $ARGUMENTS
Background:
node "${CLAUDE_PLUGIN_ROOT}/scripts/grok-companion.mjs" adversarial-review --background $ARGUMENTS
Return the command stdout verbatim. If the companion reports a failure, return it verbatim.
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.
- 8d ago First seen · 28 lines · 17 tokens per session scan A a73b7631d1d0
adversarial-review is a command published in the GitHub repository faeton/claude-grok-plugin (1 stars, last pushed 23d ago), licensed MIT. It adds 17 tokens to every session and 294 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
review
Review Grok output (diff gate — never auto-commit).
discover
Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.
tax-review
Tax-filing compliance check — invokes tax-reviewer to produce TM-tax-{slug}.md with MeF e-file schema, Form 8879, PTIN/Circular 230, and IRC §7216 consent gaps.
voice-compliance
Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.
simplify
The over-engineering review: five tags (delete, stdlib, native, yagni, shrink), a mandatory replacement per finding, and a real null result when there is nothing to cut.
git
The pre-finish status: branch, hygiene findings, message checks, workflow lint, template state.