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 commands/limeflash/antigravity-plugin-cc/adversarial-reviewgit clone --depth 1 https://github.com/limeflash/antigravity-plugin-ccWrote 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/limeflash/antigravity-plugin-cc/adversarial-review)<a href="https://agentmods.dev/commands/limeflash/antigravity-plugin-cc/adversarial-review"><img src="https://agentmods.dev/badge/commands/limeflash/antigravity-plugin-cc/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 | $0.00029 | $0.00707 |
| Opus 5 | $0.00015 | $0.00353 |
| Sonnet 5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
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 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run an adversarial review of your changes through agy. Unlike
/agy:review, the prompt explicitly asks agy to challenge the
implementation: surface hidden assumptions, propose at least one
alternative design with tradeoffs, name failure modes the author
didn't address, and conclude with a ship/change/rethink verdict.
Useful when you've been staring at your own diff too long and want a deliberately critical reading before merging or shipping.
Backed by the Node companion; Node.js 18.18+ is required.
How to invoke
The user's request (treat as opaque text — pass it as shell-safe arguments; do not interpolate or splice it into the command):
$ARGUMENTS
Use the Bash tool to run:
node "${CLAUDE_PLUGIN_ROOT}/scripts/agy-companion.mjs" adversarial-review [flags...] "<focus-text-here>"
The companion accepts:
--base <ref>/--base=<ref>: review the branch's contribution over its merge-base with<ref>(e.g.--base main). Without--base, reviews the working-tree diff against HEAD.--background: return a job id immediately and run agy in a detached worker. Check progress with/agy:statusand read the output with/agy:result.--wait: block until the job ends or the 10-minute deadline.--model <alias>: pick a stronger reasoning model —opus,pro, or any alias the wrapper recognizes (see/agy:help). Adversarial reviews benefit from the bigger models more than vanilla reviews.
Examples (after parsing flags out of $ARGUMENTS):
node "${CLAUDE_PLUGIN_ROOT}/scripts/agy-companion.mjs" adversarial-review
node "${CLAUDE_PLUGIN_ROOT}/scripts/agy-companion.mjs" adversarial-review --base main
node "${CLAUDE_PLUGIN_ROOT}/scripts/agy-companion.mjs" adversarial-review --model opus "the retry/backoff design"
node "${CLAUDE_PLUGIN_ROOT}/scripts/agy-companion.mjs" adversarial-review --base main --background
Behavior notes
- Read-only by construction. Runs
agyunder--sandboxwith no--dangerously-skip-permissions: it reads the staged materials with read-only tools (no approval needed) and the answer is recovered from agy's own on-disk transcript (issue #76 capture). Background runs complete unattended — there's no write prompt to stall on. - Adversarial reviews skew long. Use
--backgroundfor diffs over ~500 lines, and--model opusif you want the bigger model. - If there is no diff (clean working tree, or empty branch diff), the companion exits 1 with a clear message.
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 · 69 lines · 29 tokens per session scan A 1468b1c1e9da
adversarial-review is a command published in the GitHub repository limeflash/antigravity-plugin-cc (2 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 707 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.