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/s977043/river-review/challengegit clone --depth 1 https://github.com/s977043/river-reviewWrote 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/s977043/river-review/challenge)<a href="https://agentmods.dev/commands/s977043/river-review/challenge"><img src="https://agentmods.dev/badge/commands/s977043/river-review/challenge.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 | $0.00023 | $0.00586 |
| Opus 5 | $0.00012 | $0.00293 |
| Sonnet 5 | $0.00005 | $0.00117 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
challenge 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 5d 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
Context
- Status:
git status - Diff:
git diff - Recent commits:
git log --oneline -10
Task
あなたは 敵対的レビュアー です。変更を3つの視点から徹底的に検証してください。
手法1: Pre-mortem（失敗シナリオ分析）
「この変更が6ヶ月後にインシデントを引き起こした」と仮定し、その原因を逆算せよ。
- 崩れる前提は何か？
- 因果連鎖はどうなるか？
- 事前に防ぐ/検知する方法は？
手法2: War Game（攻撃者シミュレーション）
攻撃者の立場で、この変更をどう悪用できるかを分析せよ。
- 新たに露出する攻撃面は？
- 具体的な攻撃手順は？
- 防御のギャップと最小限の対策は？
手法3: Logic Torturing（論理検証）
変更に含まれる設計判断の論理的な穴を突け。
- この判断の前提は常に成立するか？
- 代替案はなぜ棄却されたか？
- この判断が間違いだとわかったとき、元に戻せるか？
Output Format
## 🔍 Adversarial Review
### Pre-mortem（失敗シナリオ）
<file>:<line>: [シナリオ] ...
### War Game（攻撃シナリオ）
<file>:<line>: [シナリオ] ...
### Logic Torturing（論理検証）
<file>:<line>: [検証] ...
### 最も重大な発見
<1件の要約と推奨アクション>
Rules
- 各手法で最大3件（合計最大9件）に絞る（SKILL.md フル実行時は最大5件）。
- すべての指摘は差分の具体的な行に紐づける。
- 推測は推測として明示する。
- 指摘には必ず次のアクション（Fix）を添える。
- スキル定義の詳細:
skills/agent-skills/adversarial-review/SKILL.md
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.
- 5d ago First seen · 69 lines · 23 tokens per session scan A 4f7d8c350948
challenge is a command published in the GitHub repository s977043/river-review (3 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 586 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
adversarial-review
Run an adversarial Gemini code review that challenges the implementation approach and design choices.
logic-fix-all
Autonomous audit-and-fix — after consent, scan the target, fix every logic issue found (all severities), verify each fix, and report anything unresolved.
logic-diff
Check two code versions for semantic equivalence — use after a refactor or rewrite.
logic-explain
Trace execution step by step — use when code behavior is surprising or confusing.
logic-health
Sweep a whole codebase or directory for logic correctness — use before a release or to identify risk hotspots.
logic-review
Review code for logic bugs — use when you suspect something is wrong but have no failing test yet.