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 mlopscommunity/Coding-Agents-Conference-skills --skill adversarial-code-reviewgit clone --depth 1 https://github.com/mlopscommunity/Coding-Agents-Conference-skillsWrote 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/mlopscommunity/coding-agents-conference-skills/adversarial-code-review)<a href="https://agentmods.dev/skills/mlopscommunity/coding-agents-conference-skills/adversarial-code-review"><img src="https://agentmods.dev/badge/skills/mlopscommunity/coding-agents-conference-skills/adversarial-code-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 | $0.00047 | $0.02248 |
| Opus 5 | $0.00023 | $0.01124 |
| Sonnet 5 | $0.00009 | $0.00450 |
| Haiku 4.5 | $0.00005 | $0.00225 |
Grade A, and why
adversarial-code-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.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Code Review
Overview
A multi-agent review pattern where one agent builds (or authors), a second agent critiques the code, and a third agent critiques the review itself. This layered adversarial approach filters out low-value nitpicks and surfaces only high-confidence, high-priority issues that deserve human attention.
Core principle: Fewer, higher-quality review comments build trust. Filter ruthlessly to high confidence + high priority only. Target roughly two comments per PR.
Dependency: Claude Code CLI with --append-system-prompt support. Optionally, a different model for the review pass than the one used for writing.
When to Use
- Reviewing pull requests before merge, especially when review quality matters more than speed
- As a CI-integrated automated reviewer that developers actually read instead of ignore
- When existing automated reviews produce too much noise and developers have stopped trusting them
- During versioned critique cycles where a plan or design needs iterative refinement
When NOT to Use
- Trivial PRs (typo fixes, dependency bumps, single-line config changes)
- When you need instant feedback during live pairing sessions (too slow for interactive use)
- As a replacement for human review on security-critical or compliance-gated changes
Common Mistakes
| Mistake | Why it's wrong |
|---|---|
| Surfacing every finding to the developer | Noise kills trust. Developers stop reading reviews that cry wolf. Filter to ~2 high-priority, high-confidence comments per PR. |
| Using the same model for writing and reviewing | The model is biased toward its own patterns. Use a different model for review than the one that wrote the code — it catches different classes of issues. |
| Skipping the meta-reviewer (third agent) | Without a check on the reviewer, you get false positives and nitpicks dressed up as critical findings. The meta-reviewer filters the reviewer's output. |
| Running adversarial review without priming the critic | A neutral prompt produces polite, hedging reviews. Tell the reviewer the code likely contains bugs to prime it for genuine criticality. |
| Treating all review comments as equal priority | Without confidence and priority scoring, developers cannot triage. Every comment must carry explicit confidence (high/medium/low) and priority (high/medium/low). |
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 · 185 lines · 47 tokens per session scan A a9539a162637
adversarial-code-review is a skill published in the GitHub repository mlopscommunity/Coding-Agents-Conference-skills (37 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 2,248 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 2026-08-30.
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