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 event4u-app/agent-config --skill adversarial-reviewgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/adversarial-review)<a href="https://agentmods.dev/skills/event4u-app/agent-config/adversarial-review"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/adversarial-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/event4u-app/agent-config/adversarial-review"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/adversarial-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.01727 |
| Opus 5 | $0.00023 | $0.00864 |
| Sonnet 5 | $0.00009 | $0.00345 |
| Haiku 4.5 | $0.00005 | $0.00173 |
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 2d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Review
When to use
Use this skill when:
- You've completed a plan, design, or proposed fix and are about to present it.
- The change is non-trivial (affects multiple files, changes behavior, touches critical paths).
- You're about to recommend an architecture or design decision.
- The user submits finished work (draft, post, naming decision, design proposal) and asks for an honest critical take — "what do you actually think?", "be brutal", "was hältst du wirklich davon". The flow is the same Attack-Defend-Revise loop, but on the user's artifact rather than the agent's plan.
Do NOT use when:
- The task is trivial (renaming, formatting, simple config change).
- The user explicitly asked for a quick/rough draft.
- You're exploring options, not committing to one yet.
Inbound delegation: decision-record
§ Weighted-matrix mode delegates a scoped attack here after the weighted
sums ("attack the winner, using the losing options' strongest criteria")
— treat the matrix + sensitivity block as the artifact under review.
Procedure: Adversarial review
- Inspect the artifact — Read the plan, diff, or draft you are about to critique; note its scope, assumptions, and the explicit asks before attacking.
- Attack — Run Step 1 below as the grumpy senior engineer.
- Defend — Run Step 2 as the balanced engineer; classify each criticism as must-fix / defer / reject.
- Revise — Run Step 3 to fold valid fixes back in, then report every criticism with its disposition. Deciding which trade-offs matter is the user's pass, not this step's.
Step 1: Attack (Grumpy Senior Engineer)
Assume your plan/fix is flawed. Ask yourself:
- What's the weakest assumption?
- Where will this break under load, at scale, or with edge cases?
- What did I ignore or hand-wave?
- Is this over-engineered for the actual problem?
- Would a simpler approach work just as well?
- What will the next developer curse me for?
Step 2: Defend (Balanced Engineer)
What ships with it
1 file 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.
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.
- 2d ago Changed · -8 lines 99f8332c43aa
- 10d ago First seen · 190 lines · 47 tokens per session scan A 79942cff875f
adversarial-review is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 1,727 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 2026-08-31.
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