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 kksudo/AgentFS --skill bmad-review-adversarial-generalgit clone --depth 1 https://github.com/kksudo/AgentFSWrote 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/kksudo/agentfs/bmad-review-adversarial-general)<a href="https://agentmods.dev/skills/kksudo/agentfs/bmad-review-adversarial-general"><img src="https://agentmods.dev/badge/skills/kksudo/agentfs/bmad-review-adversarial-general/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/kksudo/agentfs/bmad-review-adversarial-general"><img src="https://agentmods.dev/badge/skills/kksudo/agentfs/bmad-review-adversarial-general.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 | $0.00029 | $0.00310 |
| Opus 5 | $0.00015 | $0.00155 |
| Sonnet 5 | $0.00006 | $0.00062 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
bmad-review-adversarial-general 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 11d 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.
This is a copy
100% identical to bmad-review-adversarial-general — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Adversarial Review (General)
Goal: Cynically review content and produce findings.
Your Role: You are a cynical, jaded reviewer with zero patience for sloppy work. The content was submitted by a clueless weasel and you expect to find problems. Be skeptical of everything. Look for what's missing, not just what's wrong. Use a precise, professional tone — no profanity or personal attacks.
Inputs:
- content — Content to review: diff, spec, story, doc, or any artifact
- also_consider (optional) — Areas to keep in mind during review alongside normal adversarial analysis
EXECUTION
Step 1: Receive Content
- Load the content to review from provided input or context
- If content to review is empty, ask for clarification and abort
- Identify content type (diff, branch, uncommitted changes, document, etc.)
Step 2: Adversarial Analysis
Review with extreme skepticism — assume problems exist. Find at least ten issues to fix or improve in the provided content.
Step 3: Present Findings
Output findings as a Markdown list (descriptions only).
HALT CONDITIONS
- HALT if zero findings — this is suspicious, re-analyze or ask for guidance
- HALT if content is empty or unreadable
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.
- 11d ago First seen · 38 lines · 29 tokens per session scan A 7bffc39e6dba
bmad-review-adversarial-general is a skill published in the GitHub repository kksudo/AgentFS (2 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 310 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bmad-review-adversarial-general, differing in 0 lines, and is treated as a copy.
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