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 stellarshenson/claude-code-plugins --skill adversarial-reviewgit clone --depth 1 https://github.com/stellarshenson/claude-code-pluginsWrote 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/stellarshenson/claude-code-plugins/adversarial-review)<a href="https://agentmods.dev/skills/stellarshenson/claude-code-plugins/adversarial-review"><img src="https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/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 | $0.00062 | $0.00956 |
| Opus 5 | $0.00031 | $0.00478 |
| Sonnet 5 | $0.00012 | $0.00191 |
| Haiku 4.5 | $0.00006 | $0.00096 |
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 7d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Review
Invoke the devils-advocate:adversarial-review skill first - it is the single source of truth for the two modes (diff bug-hunt vs whole-repo audit), the rounds protocol, the spawn mechanics, the gotchas, and every adversary persona beside it in adversaries/<name>.md. Do NOT duplicate it here. This skill is only the data-science entry point into it.
Requires the devils-advocate plugin installed - the skill and its adversaries live there. For a non-data-science target, /devils-advocate:adversarial-review is the same skill with the full roster up front.
What to do
- Invoke the
devils-advocate:adversarial-reviewskill - No adversary named? ASK before spawning - state the inferred target, list the fitting candidates with their lens, recommend one, wait. Wrong lens = fluent review of a risk the target lacks. Skip only when the prompt names it
- Cap at 3 unless the user explicitly asks for more - your triage, not the spawn, is the bottleneck; 3 lenses catch most of what 5 would, at a review you finish
- Pick by where the risk lives - the four that earn their keep in a data science project:
- data-scientist → an experiments log, notebook, data-prep pipeline, or metric/eval design, before trusting a conclusion
- architect → the project / pipeline architecture, config, repo structure
- popular-science → the article, story, or README, before publishing for non-specialists
- ux-designer → notebook visuals, figures, dashboards
- each is also fully generalist - use it on any target that fits its lens. The skill's roster carries more (
bug-hunter,qa-engineer,methodologist,tui,devops,analyst) - reach for them when the risk is there:methodologiston an experiment's verdict ladder,qa-engineeron the test suite,analyston a spec or acceptance-criteria doc
- Pick the mode: Mode 1 (inline diff, no tools) for a specific change; Mode 2 (whole-repo, tools ON) for systemic rot
TaskCreatethe review before spawning,TaskUpdateit each round -completedonly on a clean confirming round. One task per review, not per lens- Spawn the
devils-advocate:adversarial-reviewersubagent - one per lens, naming the adversary and scope in its prompt; a panel goes in a single message so the lenses run concurrently and the user can watch each. Write the prompt as if it were aclaude -pcommand line - a process that knows nothing but what you typed. Pass target, scope and locked decisions; never your reasoning for the change, which is the thing under review. Drop toclaude -p(skill's mechanics -env -u CLAUDECODE,< /dev/null,--no-session-persistence) only for what a subagent cannot do - genuinely deny tools in Mode 1, or pin a different model - Adjudicate before fixing - for a panel, or any round past 3, spawn
devils-advocate:adjudicatorwith every lens's findings plus anything you know (a blast radius you already have, a locked decision, domain insight, the previous round's findings and the fixes since). It returns one change plan grouped by root cause, with each change's radius and what it could break. Skip only for a single lens with one or two findings you can verify yourself - Triage what the adjudicator left UNPROVEN and spot-check its confirmed and refuted calls - you still own the final call - then fix the real ones and run the re-confirm round - do not call it clean until a confirming round comes back clean
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.
- 7d ago First seen · 29 lines · 62 tokens per session scan A 5b42c4aed683
adversarial-review is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 956 once invoked, about $0.0003 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.
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