需求写了一半、规则没补齐、验收条件不清楚，就准备交给开发？ Requirement Ready AI 会从业务目标、功能范围、业务规则、验证、权限、UI、API、异常与边界条件等角度检查需求完整度，并补齐 User Story、Acceptance Criteria、QA Test Cases 与风险。 帮助你在进入开发前，先确认需求是否真的 Ready。
需求分析→系统设计→代码执行→质量审计→交付验收，一键完成大型项目全自动开发。 Requirements Analysis → System Design → Code Execution → Quality Audit → Delivery Acceptance. One command to autonomously develop large-scale projects.
Paste your work history and the target job description. This agent produces a Japanese resume (職務経歴書), an ATS-optimized English resume, a bilingual cover-letter pair, an ATS Fit Score (0-100) showing how well you match, and the Top 10 missing keywords to add before submitting. Runs locally, no data sent. Like this tool? ExpertPanel gives you a scored review with specific improvement suggestions from 3 AI experts.
Turn pasted AI evaluation cases into an evidence-bounded triage brief that separates expected and observed behavior, hypotheses, and missing checks without claiming a root cause or fix.
I design AI prompts professionally and have analyzed thousands of prompts across ChatGPT, Claude, and Gemini. I distilled the 5 essential elements that separate effective prompts from weak ones into this instant diagnostic tool. Rule-based scoring ensures consistent, deterministic results every time.
你把影片網址丟給 AI，它讀了標題就開始講——那不叫看過影片，那叫猜。 這支讓 AI 真的看：抽出去重複的關鍵幀、產九宮格 contact sheet（一次讀九張省 9 倍圖片量）、另外跑高準度中文逐字稿，三樣一起讀完才回答。 差別很具體：沒看過畫面的 AI 說「內容豐富值得參考」，看過的說「0–2 秒用失敗畫面當 hook，全片 6 個鏡位平均 2.4 秒換一次」。
Hands-on prompt engineering and LLM evaluation. Built around a diagnose-then-ship loop: forensic transcript critique ▎ that names the exact failure mode, paired with a fixer that produces complete, deployment-ready rewrites — not ▎ vague advice.
Built from auditing AI implementations across dozens of teams — finding where agents break, where prompts drift, where costs balloon, and where humans are still doing work that should be automated. This skill gives you a structured assessment of any AI workflow, with prioritized fixes.