adversarial-challenger

adversarial-challenger is a skill for Claude Code, Codex from jggomez/expert-ai-developer-skills. It costs 35 tokens per session (2,407 once invoked), scanned A, original, Apache-2.0.

Adversarial stress testing, fuzzing, worst-case inputs, failure-path probes, timeout/reconnection simulations, and concurrency/memory stress.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; positional $N argument; mentions Claude Code.

Install with agentmods
npx agentmods add skills/jggomez/expert-ai-developer-skills/adversarial-challenger
Install

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.

Any agent
npx skills add jggomez/expert-ai-developer-skills --skill adversarial-challenger
Clone the repo
git clone --depth 1 https://github.com/jggomez/expert-ai-developer-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for adversarial-challenger

README.md
[![agentmods](https://agentmods.dev/badge/skills/jggomez/expert-ai-developer-skills/adversarial-challenger/github.svg)](https://agentmods.dev/skills/jggomez/expert-ai-developer-skills/adversarial-challenger)
Your own site
<a href="https://agentmods.dev/skills/jggomez/expert-ai-developer-skills/adversarial-challenger"><img src="https://agentmods.dev/badge/skills/jggomez/expert-ai-developer-skills/adversarial-challenger/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.

agentmods 80×15 button for adversarial-challenger

Your own site · 80×15
<a href="https://agentmods.dev/skills/jggomez/expert-ai-developer-skills/adversarial-challenger"><img src="https://agentmods.dev/badge/skills/jggomez/expert-ai-developer-skills/adversarial-challenger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,407 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.02407
Opus 5.5 $0.00014 $0.00963
Sonnet 5.5 $0.00007 $0.00481
Haiku 4.5 $0.00003 $0.00241

Measured today against content hash a3f5326ade20, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

adversarial-challenger 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 today.

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.

plugins/antigravity/python-backend/skills/adversarial-challenger/SKILL.md · 170 lines

How it starts

The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adversarial Challenger & Stress Testing Skill

Overview

This skill implements an adversarial testing philosophy designed to actively attempt to break software components. Operating as an objective, hostile challenger, it subjects systems to worst-case boundary inputs, fuzzing, intentional fault injection, network latency/disconnections, race conditions, and memory/resource saturation.

Its primary objective is discovering brittle edge cases, memory leaks, concurrency races, and unhandled exceptions before code reaches staging or production environments, returning findings in its final message (no report file).

Return Contract: Return the review in your final message (<= 50 lines: verdict, findings with file:line, required fixes); do NOT write a report file.


When to Use

Trigger Scenarios

  • Validating business-critical features (payments, authentication, order processing, state machines) prior to release.
  • Probing multi-threaded, asynchronous, or distributed systems for concurrency race conditions and deadlocks.
  • Simulating network chaos, slow dependencies, socket timeouts, and intermittent connection drops.
  • Fuzzing APIs and interfaces with malformed JSON, boundary integers, null bytes, unicode overflows, and schema violations.
  • Returning the mandatory adversarial verdict (final message, no file) during adversarial verification stages.

When NOT to Use

  • Happy-path functional verification: Route standard acceptance testing to qa-tester.
  • Static code smell and complexity review: Route architectural review to code-critic.
  • Interactive browser visual flow testing: Route UI/GUI flows to blackbox-tester.
  • Routine unit test authoring during TDD: Route unit test creation to test-driven-development and code-implementer.

Process

The adversarial probing lifecycle tests resilience across 5 core attack and stress vectors:

flowchart TD
    START["Identify Critical Component / API"] --> V1["Vector 1: Boundary & Input Fuzzing"]
    V1 --> V2["Vector 2: Fault Injection & Failure Paths"]
    V2 --> V3["Vector 3: Timeout & Network Chaos"]
    V3 --> V4["Vector 4: Concurrency & Race Conditions"]
    V4 --> V5["Vector 5: Resource & Memory Saturation"]
    V5 --> COMPILE["Aggregate Findings & Severity Ratings"]
    COMPILE --> REPORT["Return report in final message"]
    REPORT --> STATUS{"Any CRIT or HIGH Defects?"}
    STATUS -->|"Yes"| REJECT["VULNERABLE / AT RISK: Escalate to code-implementer"]
    STATUS -->|"No"| APPROVE["RESILIENT: Release verification cleared"]

Read the full file on GitHub · 170 lines

Files

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.

Changes

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.

  1. today First seen · 170 lines · 35 tokens per session scan A a3f5326ade20

Subscribe to this mod's changes

adversarial-challenger is a skill published in the GitHub repository jggomez/expert-ai-developer-skills (30 stars, last pushed yesterday), licensed Apache-2.0. It adds 35 tokens to every session and 2,407 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 2026-11-06.

Related

adversarial-challenger compared with skills from other repositories

adversarial-challenger next to 6 skills from other repositories
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adversarial-challengerjggomez/expert-ai-developer-skillsSkill30yesterdayApache-2.0A35Claude Code, Codex
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supermemorysupermemoryai/supermemorySkill32kyesterdayMITA81Claude Code, Codex
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