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 jimmc414/claude-code-plugin-marketplace --skill adversarial-analysisgit clone --depth 1 https://github.com/jimmc414/claude-code-plugin-marketplaceWrote 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/jimmc414/claude-code-plugin-marketplace/adversarial-analysis)<a href="https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/adversarial-analysis"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/adversarial-analysis/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/jimmc414/claude-code-plugin-marketplace/adversarial-analysis"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/adversarial-analysis.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 | $0.00037 | $0.00897 |
| Opus 5.5 | $0.00015 | $0.00359 |
| Sonnet 5.5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
adversarial-analysis 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 1mo 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Code Analysis
Purpose
Map the "Surface of Reality" for target code. Your goal is to distinguish:
- Contract Violations (invalid inputs the caller shouldn't send) → NOT testable
- Logic Bugs (valid inputs the code handles poorly) → TESTABLE
Analysis Checklist
1. Contract Extraction
Explicit Contracts:
- Type hints (
int,str,Optional[T],List[T]) assertstatements and preconditions- Docstring
Args,Raises,Returnssections - Validation code at function entry
Implicit Contracts:
- Variable names implying limits (
retry_count→ small positive int,buffer_size→ memory-reasonable) - Context from call sites (what values are actually passed?)
- Domain knowledge (user IDs are positive, emails contain @)
External Constraints:
- Database column limits (VARCHAR(255))
- API rate limits and timeouts
- Filesystem permissions and path length limits
2. Realism Baseline (The 3-Sigma Rule)
Use Grep to scan existing tests in tests/ and call sites in src/. Calculate boundaries:
String Inputs:
existing_lengths = [len(s) for each test string argument]
if len(existing_lengths) >= 5:
Max_Realistic_Length = mean(existing_lengths) + 3 * std(existing_lengths)
elif len(existing_lengths) >= 1:
Max_Realistic_Length = max(existing_lengths) * 2
else:
Max_Realistic_Length = 256 # Zero-sample fallback
Numeric Inputs:
existing_values = [v for each numeric argument]
if len(existing_values) >= 5:
Lower = mean(existing_values) - 3 * std(existing_values)
Upper = mean(existing_values) + 3 * std(existing_values)
elif len(existing_values) >= 1:
Lower = min(existing_values) - abs(min(existing_values))
Upper = max(existing_values) + abs(max(existing_values))
else:
Lower, Upper = -1000, 1000 # Zero-sample fallback
Special Boundaries (Always Valid):
0,-1,1for integers (if type allows)- Empty string
""(if not explicitly forbidden) Noneonly if type isOptional
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.
- 1mo ago First seen · 110 lines · 37 tokens per session scan A 3bf848b67451
adversarial-analysis is a skill published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 897 once invoked, about $0.0001 per session on Opus 5.5. 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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