본문으로 건너뛰기
cccskills
무료GitHub에 공개

adversarial-review

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Adversarial review — presume it broken, find where

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.

When to use it — and which review

  • Use this when you want the thing attacked: a feature you just finished, a build before delivery, a flow you suspect is fragile, an unfamiliar codebase you must trust.
  • Use /code-review instead for a fast, conservative pass over a diff or PR — it statically reads the change for bugs and cleanups, scores confidence, and never runs the app. That is the right tool for routine change review.
  • The line between them: /code-review reads; adversarial-review runs. This skill presumes breakage, fans out diverse hostile lenses in clean contexts, and reproduces findings against the live target before believing them. Heavier, empirical, used deliberately.
  • Scale down, don't skip: for a small change one hostile reviewer is enough. Don't fan out a fleet to attack a typo fix — deliberating everything ships nothing.

The four invariants (what makes a review adversarial)

  1. Presumed-broken mandate. Every reviewer gets one instruction: "this was just built and is presumed broken; find where." Never "check if it looks ok."
  2. Context isolation. Reviewers run in clean contexts (separate subagents) — they get the target and the mandate, not the build's rationalizations. A separate context window is the closest thing to a genuine second opinion.
  3. Empirical reproduction. A finding is a hypothesis until reproduced against the real target. Run it. A finding that can't be reproduced is an opinion, not a bug.
  4. Senior triage. Reproduce before fixing, fix what's real, re-verify the fix, and name the false alarms. The report is only trustworthy if it admits what wasn't real.

How to run it

  1. Frame the target and scale. Name what's under review (a diff, a feature, a running app, a whole 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.

  2. Fan out hostile reviewers in clean contexts. Spawn subagents, each with the presumed-broken mandate and one lens, blind to each other. Choose lenses that fit the target — typically:

    • Correctness — edge cases, off-by-one, race conditions, broken flows, state that lies
    • Security — injection, authz/authn holes, secrets in code, unvalidated input, SSRF/path traversal
    • The empty world — first run, zero data, no config, expired token: does it still stand up?
    • Data integrity — partial writes, lost updates, migrations, constraints that don't hold
    • Failure & limits — network/dependency failure, timeouts, huge inputs, concurrent users
    • UX seams — error messages, loading/empty states, the run instructions a stranger needs (One-shot work dies at the seams and the empty world far more than the happy path — weight those.)
  3. Reproduce every finding. Before fixing anything, run the target and confirm the failure actually happens. Capture the evidence (output, screenshot, failing command). Unreproducible → demote to "unconfirmed," don't fix on faith.

  4. Triage like a senior. Fix what reproduced, re-run to confirm the fix closed it (and opened nothing new), and record what looked like a bug but wasn't. If a fix opens a real design fork, invoke free-will.

Output — the report

One honest summary:

  • Confirmed & fixed — what broke, the reproduction, the fix, the re-verification.
  • Confirmed & deferred — real but consciously not fixed now, and why (goes on the gap list).
  • False alarms — flagged then disproven. Listing these is what makes the rest credible.
  • Residual risk — what this pass could not exercise, and what would attack it next.

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Activate ATLAS pattern recognition mode. Identify repeated patterns, extract essential characteristics, and create reusable abstractions from concrete examples.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

syahiidkamil/Software-Engineer-AI-Agent-Atlas4032026년 6월 25일 업데이트

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

syahiidkamil/Software-Engineer-AI-Agent-Atlas4032026년 6월 25일 업데이트

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

syahiidkamil/Software-Engineer-AI-Agent-Atlas4032026년 6월 25일 업데이트

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

syahiidkamil/Software-Engineer-AI-Agent-Atlas4032026년 6월 25일 업데이트

free-will

무료

Deliberate-choice procedure for a medium-to-high-stakes engineering fork — when the first plausible solution (the instinct, the default next-token pull) would be costly to get wrong. Fires AUTONOMOUSLY: invoke proactively whenever a fork fits, never wait to be asked — mechanical triggers include a fix failing for the 2nd-3rd time, adding a dependency, schema/migration design, deleting or deprecating things others depend on, changing a public API, choosing an architecture or stack. Refuse the premature collapse: hold real options open (urge · contrarian · synthesis · out-of-box · intuitive dots · precedent · first-principles), ground each branch in at least one fact from outside the model (codebase, docs, benchmark, spike), future-model consequences (blast radius, reversibility, maintenance, pre-mortem), collapse by deliberate choice, then try to refute the winner before acting. Log the decision and rejected branches in docs/decision_logs/. Not for routine calls — a decision worth more than one forward pass.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

syahiidkamil/Software-Engineer-AI-Agent-Atlas4032026년 6월 25일 업데이트

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

syahiidkamil/Software-Engineer-AI-Agent-Atlas4032026년 6월 25일 업데이트

syahiidkamil의 스킬 모두 보기

이 스킬의 문제 신고