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Use when UI changes must be proven safe across breakpoints using baseline and diff workflows. AE.LTD visual quality gate for release confidence.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/aegntic/cldcde --skill ae-ltd-visual-regression-forge명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Run a 5-adviser council to stress-test any decision. Five distinct lenses (Contrarian, First-Principles, Expansionist, Outsider, Executor) answer independently, then anonymously peer-review each other, and a Chairman synthesizes a single clear recommendation. Kills sycophancy and groupthink on high-stakes calls.
Diligence-grade product and technical analysis for the ae.ltd proof-agent concept, including comparison against Cursor and Omnara, MVP and production architecture, red-team review, and build-vs-buy evaluation. Use when Codex needs to create or critique PRDs, tech specs, competitive diligence, leadership memos, or multi-agent evaluation packs for agent-runtime, demo-agent, or proof-agent products.
Use when creating or upgrading CLAUDE.md instructions and selecting the right template baseline for repo size, architecture, and team constraints. AE.LTD template decision workflow.
SKILL.md 표시 중
SOC 직업 분류 기준
| name | ae-ltd-visual-regression-forge |
| description | Use when UI changes must be proven safe across breakpoints using baseline and diff workflows. AE.LTD visual quality gate for release confidence. |
Proves UI changes are safe across breakpoints using baseline and diff workflows. Visual quality gate for release confidence. Niche gem source: visual-regression plugin from awesome-claude-code-toolkit.
User: "Did my CSS change break anything?"
→ Define scope → Capture baseline → Run diff → Report pass/fail
Define snapshot scope. Pick critical pages/components and three base viewports: mobile, tablet, desktop.
Capture clean baselines. Wait for network idle and mask dynamic regions.
Capture current state with matching settings. Use identical viewport/device/browser settings for comparability.
Run diff and classify. Compute pixel deltas and mark pass/warn/fail against threshold.
Convert failures into actionable fixes. Map each diff to CSS/layout/asset causes and patch.