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 agentmods add skills/tarekkharsa/agentstack/adversarial-reviewnpx skills add Tarekkharsa/agentstack --skill adversarial-reviewgit clone --depth 1 https://github.com/Tarekkharsa/agentstackWrote 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/tarekkharsa/agentstack/adversarial-review)<a href="https://agentmods.dev/skills/tarekkharsa/agentstack/adversarial-review"><img src="https://agentmods.dev/badge/skills/tarekkharsa/agentstack/adversarial-review.svg" alt="Measured on agentmods" 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 | $0.00056 | $0.00630 |
| Opus 5 | $0.00028 | $0.00315 |
| Sonnet 5 | $0.00011 | $0.00126 |
| Haiku 4.5 | $0.00006 | $0.00063 |
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 5d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial review
Use when you are the reviewer in a generate-review-fix loop: another agent (or person) wrote the change, and your job is to find what's wrong with it — not to appreciate what's right.
The stance matters more than the checklist: the agent that wrote the code wants it accepted; you want to find the failure. Those must be different agents — never review your own diff adversarially and call it done.
Ground rules
- Assume the code is wrong. Your null hypothesis is "this diff contains a bug"; your job is to locate it. Only after honestly failing to construct a failure do you approve.
- Work from the diff. Judge what's in front of you; read surrounding source only to verify a suspicion (call signatures, invariants, callers) — not to absorb the author's framing or comments as truth.
- Concrete failures only. Every finding names inputs/state → wrong outcome. "This looks risky" is not a finding; "empty list → index panic at line 42" is.
- The paragraph rule: if a workaround needs a paragraph-long comment to justify why it's OK, the code is wrong — reject and say what to fix. Suspiciously long justifications are where stubs and shortcuts hide.
- No trust in green. "Tests pass" is not evidence the change is right — check whether the tests were weakened, skipped, or never covered the changed behavior in the first place.
Where to look first
- Edges: empty/zero/max inputs, error paths, early returns, off-by-one.
- Deletions: what did the diff remove, and who still depended on it?
- Renames & moves: behavior changes hiding inside "mechanical" churn.
- Stubs:
todo!(),unimplemented, hardcoded returns, and their explanatory comments. - Concurrency & resources: lifetimes, locks, cleanup on the failure path.
Output
Return findings ranked most-severe first, each as: where (file:line), what breaks (the concrete scenario), fix direction (one sentence). If nothing survived honest scrutiny, say so plainly — a forced nitpick is noise, and an empty report from an adversary is a strong signal.
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.
- 5d ago First seen · 56 lines · 56 tokens per session scan A e97defaf2bb4
adversarial-review is a skill published in the GitHub repository Tarekkharsa/agentstack (3 stars, last pushed 22d ago), licensed Apache-2.0. It adds 56 tokens to every session and 630 once invoked, about $0.0003 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.
Other skills, from other repositories
local-frontend-check
Smoke-test or verify UI behaviour on the local Jarvis Registry frontend running at http://localhost/gateway. Use for manual regression checks, bug-fix verification, and end-to-end confirmation of specific flows without running the automated test suite.
release-notes
Edit an existing GitHub release's body into the project's bilingual (English + Chinese) template format with a References section built from merged PRs. Use when 修改 release、整理发布说明、release notes、编辑 release 内容、发版后整理、edit release body.
release-notes
Create release notes for a new version tag. Gathers all commits, PRs, issues fixed, and breaking changes since a previous release. Creates the release notes markdown file, tags the repo, and pushes. Asks the user to confirm the base version to diff against.
create-milestone
Create a GitHub milestone for an upcoming release. Suggests the next version based on the latest release, gathers all merged PRs and closed issues since that release, presents a draft with two tables (Issues and PRs) for user approval, then creates the milestone and assigns all approved items.
pr-review
Review a GitHub pull request using multiple expert personas. Takes a PR URL as input, analyzes the changes, and generates comprehensive review feedback from different perspectives (Merge Specialist, Frontend, Backend, Security, DevOps, AI/Agent, SRE, Chief Architect).
generate-agent-card
Generate an A2A agent card JSON by analyzing agent source code in a folder or GitHub URL. Studies the code to detect agent name, skills, tools, auth, protocol, and generates a spec-compliant agent card.