Adversarially review something just built — presume it is broken and find where. Use after implementing a feature, finishing a build, or before shipping, or whenever ATLAS or Boss wants a hostile second opinion on a diff, a running app, or a whole codebase. Spawns clean-context reviewers with opposing lenses (correctness, security, empty-world) and reproduces every finding by running the target before believing it. Distinct from /code-review, which statically reads a diff and never runs the app.
npx skills add https://github.com/syahiidkamil/Software-Engineer-AI-Agent-Atlas --skill adversarial-review
A normal review reads code and looks for reasons to approve it. An adversarial review starts from the
opposite prior: this was just built, it is presumed broken, and the job is to find where. The
builder believes in their work — that belief is exactly the blind spot. So the reviewer must be
incentivized to find failure, not confirm success, and must work *without the builder's context* so it
cannot inherit the builder's assumptions.
This skill is the canonical adversarial-review protocol for ATLAS. Reach for it autonomously after
building anything non-trivial, before shipping, or when handed unfamiliar code to harden.
delivery, a flow you suspect is fragile, an unfamiliar codebase you must trust.
/code-review instead for a fast, conservative pass over a diff or PR — it staticallyreads the change for bugs and cleanups, scores confidence, and never runs the app. That is the
right tool for routine change review.
/code-review reads; adversarial-review *runs*. This skill presumesbreakage, fans out diverse hostile lenses in clean contexts, and **reproduces findings against the
live target** before believing them. Heavier, empirical, used deliberately.
to attack a typo fix — deliberating everything ships nothing.
presumed broken; find where."* Never "check if it looks ok."
target and the mandate, not the build's rationalizations. A separate context window is the closest
thing to a genuine second opinion.
Run it. A finding that can't be reproduced is an opinion, not a bug.
alarms. The report is only trustworthy if it admits what wasn't real.
codebase) and how to exercise it (commands to run, URLs/flows to walk, entry points). Pick reviewer
count by stakes: 1 for a small change, 3–5 lenses for a feature or build.
mandate and one lens, blind to each other. Choose lenses that fit the target — typically:
(One-shot work dies at the seams and the empty world far more than the happy path — weight those.)
actually happens. Capture the evidence (output, screenshot, failing command). Unreproducible →
demote to "unconfirmed," don't fix on faith.
nothing new), and record what looked like a bug but wasn't. If a fix opens a real design fork,
invoke free-will.
One honest summary:
Never report "no issues found" from a read alone — that claim requires having *run* the lenses and
reproduced nothing. Report what you actually exercised, not what you assume holds.
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
GitHub CLI (gh) comprehensive reference for repositories, issues, pull requests, Actions, projects, releases, gists, codespaces, organizations, extensions, and all GitHub operations from the command line.
GitHub CLI - manage repositories, issues, pull requests, actions, releases, and more from the command line.
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti
Take syahiidkamil/adversarial-review from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.