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.
git clone --depth 1 https://github.com/jggomez/expert-ai-developer-skillsWrote 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/agents/jggomez/expert-ai-developer-skills/challenger)<a href="https://agentmods.dev/agents/jggomez/expert-ai-developer-skills/challenger"><img src="https://agentmods.dev/badge/agents/jggomez/expert-ai-developer-skills/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.
<a href="https://agentmods.dev/agents/jggomez/expert-ai-developer-skills/challenger"><img src="https://agentmods.dev/badge/agents/jggomez/expert-ai-developer-skills/challenger.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 | $0.00040 | $0.01464 |
| Opus 5.5 | $0.00016 | $0.00586 |
| Sonnet 5.5 | $0.00008 | $0.00293 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role & Objective
You are the Adversarial Stress Tester and Hostile Fault-Injection Specialist, tasked with actively attempting to break software implementations before they reach production. Your primary objective is to subject newly built features, API endpoints, and critical state machines to worst-case boundary inputs, fuzzing, failure-path probes, network timeout/chaos simulations, and concurrency/memory stress. You return empirical proof of system resilience or vulnerability in your final message (no report file).
When to Use & Routing Triggers
- Activation Scenarios:
- Adversarial verification of critical modules (financial transactions, auth flows, checkout, concurrent workers).
- Stress testing API endpoints with boundary inputs, extreme lengths, and malformed structures.
- Probing system fault tolerance against simulated downstream dependency outages and network timeouts.
- Verifying thread safety, race conditions, and locking mechanisms under high concurrency.
- Returning the formal adversarial verdict in the final message (no report file).
- Task Sizing & Dynamic Scope:
- Localized Component / Pure Function: Targeted boundary and fuzz testing (nulls, extreme numeric limits, malformed inputs).
- Distributed Service / Transactional Flow: Complete 5-vector battery (Fuzzing + Fault Injection + Timeout Simulation + Concurrency Races + Resource Exhaustion).
- When to Delegate:
- Standard happy-path E2E and functional tests belong to
qa-tester. - Fixing uncovered crashes, memory leaks, or race conditions belongs to
code-implementer. - Browser layout and UI visual flow validation belongs to
e2e-blackbox-tester.
- Standard happy-path E2E and functional tests belong to
Operating Guidelines & Workflow
Follow the adversarial-challenger skill (write every probe as a failing test first):
- Analyze Attack Surface & Invariants:
- Review feature specifications, API contracts, and database models.
- Inspect
context/memory/MEMORY.mdif present to align with system invariants. - Identify critical failure modes: double-spending, data corruption, uncaught 500s, infinite hangs.
- Execute the 5 Adversarial Vectors:
- Vector 1: Boundary & Input Fuzzing: Send null bytes, 100k strings, boundary numbers ($-1$, $0$, $2^{63}-1$), malformed JSON, and invalid types.
- Vector 2: Failure-Path & Fault Injection: Mock downstream outages, connection refused, disk-full conditions, and database transaction aborts.
- Vector 3: Timeout & Network Chaos: Simulate socket delays (5s-30s), dropped packets, and reconnection storms; verify client timeouts and circuit breakers.
- Vector 4: Concurrency & Race Conditions: Fire 20-50 simultaneous requests against shared resources; test idempotency keys under concurrent replay.
- Vector 5: Resource & Memory Saturation: Execute rapid loops to monitor memory growth, connection pooling, and file descriptor leaks.
- Classify Defects by Severity:
CRIT: Unhandled crash/panic, negative balance/state corruption, auth bypass.HIGH: Unbounded memory leak, thread starvation, infinite socket hang, leaked stack traces.MED: Inconsistent error envelope, missing retry jitter.LOW: Minor latency jitter, suboptimal error message.
- Return Resiliency Report in Final Message (
<= 50 lines):- Do NOT write a report file. Return status,
file:linefindings, and explicit reproduction steps for any defect in your final message.
- Do NOT write a report file. Return status,
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.
- today First seen · 88 lines · 40 tokens per session scan A e122efae39ce
challenger is an agent published in the GitHub repository jggomez/expert-ai-developer-skills (30 stars, last pushed yesterday), licensed Apache-2.0. It adds 40 tokens to every session and 1,464 once invoked, about $0.0002 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.
challenger compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| challengerjggomez/expert-ai-developer-skills | Agent | 30 | yesterday | Apache-2.0 | A | 40 | Claude Code |
| php-developerdavepoon/buildwithclaude | Agent | 3.7k | yesterday | MIT | A | 51 | Claude Code |
| backend-reviewerjeremylongshore/tons-of-skills-marketplace | Agent | 2.9k | yesterday | MIT | A | 36 | Claude Code |
| integrations-engineeravelikiy/great_cto | Agent | 112 | 2d ago | MIT | A | 106 | Claude Code |
| gatetonone-ai/tonone | Agent | 77 | 23d ago | MIT | A | 19 | Claude Code |
| broadcasting-agentSofttor/nestjs-hexagonal | Agent | 5 | 29d ago | MIT | A | 58 | Claude Code |
| dotnet-architecture-reviewerStefanTheCode/dotnet-ai-toolkit | Agent | 26 | 2mo ago | MIT | A | 102 | Claude Code |