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 AlexShchuka/neuro-matrix --skill adversarial-reviewgit clone --depth 1 https://github.com/AlexShchuka/neuro-matrixWrote 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/alexshchuka/neuro-matrix/adversarial-review)<a href="https://agentmods.dev/skills/alexshchuka/neuro-matrix/adversarial-review"><img src="https://agentmods.dev/badge/skills/alexshchuka/neuro-matrix/adversarial-review/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/alexshchuka/neuro-matrix/adversarial-review"><img src="https://agentmods.dev/badge/skills/alexshchuka/neuro-matrix/adversarial-review.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 | $0.00072 | $0.01510 |
| Opus 5 | $0.00036 | $0.00755 |
| Sonnet 5 | $0.00014 | $0.00302 |
| Haiku 4.5 | $0.00007 | $0.00151 |
Grade A, and why
adversarial-review 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial-review: an independent audit gate, not a helper
BLUF: the dominant failure mode of post-build review is sycophancy — the reviewer treats «looks plausible» as confirmation, confirms what the author implied was correct, and produces approval theater. This skill makes that structurally impossible by framing the reviewer as an adversary whose job is to break the work, not bless it.
Stage 0 — frame the reviewer correctly
The reviewer is an independent external auditor (mental model: PhD architect encountering this code for the first time with no social obligation to the author). The instructions to the reviewer must include:
- Do NOT confirm. Do NOT hint. Do NOT ask leading questions. Do NOT soften.
- Your job is to find every way this system fails, is insecure, is brittle, or is wrong.
- «I could not find a problem in area X» is an admissible finding. «Everything looks good» is not.
- Every factual claim requires ≥2 confirmations: a code anchor (file:line or exact snippet) PLUS an external reference (spec, RFC, reference repo, web) OR an explicit «cannot verify without execution» with a stated reason.
Do NOT ask the reviewer to «confirm this is working» or «verify the implementation is correct» — those framings produce sycophancy. Ask: «find every blocker that would prevent this from working correctly in production.»
Stage 1 — audit dimensions (fixed; do not abbreviate)
Run the reviewer across all dimensions in a single pass. Skipping a dimension is only valid if explicitly stated with a reason.
| Dimension | What to look for |
|---|---|
| Concurrency / lifecycle | data races, leaked goroutines/threads, deadlocks, use-after-free, resources not GC-safe (closed by GC rather than owned lifecycle), subscriptions re-created per-event instead of once per session |
| Error handling | swallowed errors, false success (operation failed but caller sees success), missing propagation, error type that loses context |
| Isolation / security | credentials leaking across trust boundaries, over-privileged components, missing input validation at trust boundaries |
| Architecture & code smell | duplicated logic, reinvented wheels (stdlib or idiomatic library solves this), wrong layer (business logic in transport, I/O in domain), dead code with a live twin (two sources of truth) |
| Benchmark vs reference repos | compare idiom usage to 2–3 reference repos on the same problem; flag where this code diverges from the community's solved pattern |
| Tests-as-theatre | tests that always pass regardless of behavior, mocks that make the test vacuous, test coverage of the wrong layer |
| Robustness bar | every I/O and wait: is there a hard timeout/deadline? is there a forced unblock if the far side hangs? does the system degrade gracefully or panic? |
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 · 78 lines · 72 tokens per session scan A 462889d18e94
adversarial-review is a skill published in the GitHub repository AlexShchuka/neuro-matrix (3 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 1,510 once invoked, about $0.0004 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.
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