القائمة
Browser authentication state management: save and restore login sessions, cookies, and storage across automation runs.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Run forever as a resident worker in ONE Codex turn: every 5 minutes poll a watched directory for new task files and execute each one to completion, then keep watching — the turn never ends. Use when the user says "queue-loop", "무한 루프로 돌려", "큐 감시해서 계속 실행", "resident worker", "watch a directory and run new tasks forever", or wants a single invocation that keeps picking up dropped commands indefinitely. Because the work stays inside one already-running turn, it keeps issuing model requests across usage-limit windows (the workspace spend cap gates only NEW top-level turns, not an in-flight one). Do NOT use for a single one-shot task, or when the user wants the turn to finish and report back.
Use when the user wants Korean prose cleaned of AI tone, stiff old-fashioned (할아버지/훈장/공문서) tone, translationese, and excessive quotation marks - or asks to "글 다듬어", "자연스럽게", "AI 말투 제거", "할아버지 말투 빼", "deslop 한국어", or "writing-fixer". Removes as many AI and unnatural expressions as possible in one thorough pass while preserving meaning, code, math, and markup. Korean prose only.
Inject a task into the queue-loop worker's queue so a running Codex resident worker picks it up on its next poll (~5 min). Use when the user says "queue-submit", "큐에 작업 넣어", "task 주입해", "이거 워커한테 넘겨", "submit a task to the codex worker", "enqueue this for queue-loop", or wants to hand a job off to the always-on worker instead of doing it inline. It only writes a *.task file into the watched queue directory (default ~/codex-queue) — it does NOT run the task in this session. Do not use when the user wants the work done right now in the current session.
한국어 텍스트에서 AI가 쓴 흔적, 번역체, 부자연스러운 표현을 제거하고 자연스러운 한국어로 다시 써 주는 스킬. 말투 통일, 문장부호 교정, 피동 표현 정리, 영어 직역체 제거를 수행한다.
Iteratively review the current repository, plan fixes from the reviews, and implement the plans. Runs three prompts in strict order (deep review → plan from reviews → implement with ralph) and repeats the full three-prompt cycle up to N times (default 100). Context is cleaned between iterations by spawning each full cycle in a fresh subagent.
Browser configuration: viewport sizing, device emulation, geolocation, offline mode, color scheme, and HTTP settings.
| name | agent-browser-state |
| version | 1.0.0 |
| description | Browser authentication state management: save and restore login sessions, cookies, and storage across automation runs. |
| allowed-tools | ["Bash","Read"] |
Save and restore browser authentication state (cookies, localStorage, sessionStorage) across sessions.
agent-browser state save <path> # Save current auth state to file
Captures cookies, localStorage, and sessionStorage for the current context.
agent-browser state load <path> # Restore auth state from file
Load before navigating to apply saved authentication.
agent-browser state list # List all saved state files
agent-browser state show <file> # Show summary of saved state
agent-browser state rename <old> <new> # Rename a saved state file
agent-browser state clear [name] # Clear specific state file
agent-browser state clear --all # Clear all saved states
agent-browser state clean --older-than <days> # Delete states older than N days
# Login once and save the session
agent-browser open https://app.example.com/login
agent-browser fill "#email" "user@example.com"
agent-browser fill "#password" "secret"
agent-browser click "button[type=submit]"
agent-browser wait --url "/dashboard"
agent-browser state save auth-prod.json
# Reuse saved session in a later run
agent-browser state load auth-prod.json
agent-browser open https://app.example.com/dashboard
# Manage saved states
agent-browser state list
agent-browser state show auth-prod.json
agent-browser state clean --older-than 30
open to ensure cookies are set before navigationauth-staging.json, auth-admin.json