「rd」の検索結果

Outbound email-marketing desk (Busabase App-in-Skill) for building segments, drafting campaigns, newsletters, and sequences, running pre-send deliverability and subject-line QA, and approving every send before it is scheduled. Structured around the SEND discipline — Setup, Engage, Nurture, Deliver — with an email-quality-auditor gate (EQS score + SHIP/FIX/BLOCK verdict). Use when the user invokes $kelly-campaigns or /kelly-campaigns, or mentions email marketing, campaigns, newsletters, drip/welcome/win-back sequences, broadcasts, segments, subject-line A/B tests, deliverability (SPF/DKIM/DMARC/spam score/inbox placement), or wants to review and approve marketing email before it is sent. This is OUTBOUND marketing to a subscriber list, distinct from kelly-email inbox triage (incoming mail). 出站邮件营销：策划分群、起草营销活动 / 新闻邮件 / 邮件序列，发送前做可送达性与主题行质检，人工审批后再排期发送。

日本語の概要は準備中です。原文の説明を表示しています。

Run a Busabase-backed job-search outreach desk with a bundled Hono App-in-Skill deployable to AirApp. Use when the user invokes $kelly-jobhunt or /kelly-jobhunt, wants to turn a resume into a structured profile and a typeset PDF, wants to find target companies and their contact addresses instead of applying through a job board, wants one tailored application email drafted per company, or wants to review and approve each send. Four subcommands - setup, profile, research, send - plus the desk itself. One company receives exactly one email; mailbox credentials live in the Busabase Vault and never reach browser code.

日本語の概要は準備中です。原文の説明を表示しています。

Busabase-backed App-in-Skill for one thing only — record who you need to follow up with after a meeting, see what is due today, mark it done. Use when the user invokes $kelly-followups or /kelly-followups, mentions a followup, follow-up, meeting action item, or wants a lightweight daily reminder of who to check in with, without a full project-management tool.

日本語の概要は準備中です。原文の説明を表示しています。

Build, analyze, repair, and audit finance three-statement models and operating forecasts. Use when the user invokes $kelly-finance, asks for 财务三表, 三表模型, income statement, balance sheet, cash flow statement, forecast model, budget model, financial projections, FP&A model, SaaS/unit economics forecast, fundraising model, board finance pack, investor model, model checks, balance-sheet balancing, cash runway, working capital, capex/depreciation schedules, debt schedules, scenario/sensitivity cases, or Excel/Sheets-ready finance outputs.

日本語の概要は準備中です。原文の説明を表示しています。

Personal App-in-Skill voice-of-customer desk (Busabase App-in-Skill) aggregating user feedback from support email, Discord/Slack communities, X replies, app-store reviews, surveys, and interviews into clustered feature requests and a roadmap decision queue. Use when the user invokes $kelly-feedback or /kelly-feedback, or mentions user feedback, voice of customer, feature requests, roadmap decisions, feedback clustering, feedback triage, request dedupe, changelog replies, decline replies, or reviewing what users are asking for.

日本語の概要は準備中です。原文の説明を表示しています。

Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live system.

日本語の概要は準備中です。原文の説明を表示しています。

Builds multi-touch pipeline attribution models and generates stakeholder-ready narratives from campaign performance data. Use when analyzing channel contribution to pipeline, preparing monthly/quarterly revenue reports for leadership, diagnosing pipeline misses, modeling budget reallocation scenarios, or translating raw attribution data into board-deck language. Designed for B2B SaaS demand gen teams that report on pipeline and revenue, not leads or MQLs.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end ABM program design and orchestration for B2B SaaS mid-market and enterprise motions. Use when building a target account list, designing account tiering criteria, planning ABM channel orchestration, coordinating BDR outreach with marketing campaigns, defining ABM measurement frameworks, or running an ABM program review. Also use when asked to "build an ABM program," "set up account-based marketing," "design a target account strategy," "plan ABM campaigns," or "measure ABM results." Designed for demand gen teams selling to Mid-Market companies with $10K-$50K+ ACV, multi-persona buying committees, and 3-6 month sales cycles.

日本語の概要は準備中です。原文の説明を表示しています。

Generates account research briefs, personalized outreach sequences, persona-specific talk tracks, and objection handling frameworks for B2B SaaS BDR teams. Use when preparing BDR outreach for target accounts, building prospecting sequences, creating call scripts, developing objection responses, onboarding new BDRs, or reviewing outreach performance. Also use when asked to "write outreach emails," "build a prospecting sequence," "create a call script," "handle objections," or "research an account for outreach." Designed for BDR teams selling compliance, security, and GRC platforms to mid-market and enterprise buyers with multi-persona buying committees.

日本語の概要は準備中です。原文の説明を表示しています。

Extract balance sheet, income statement, shares outstanding, organic growth, and GAAP reconciliation data from financial PDFs. Standardize via Tiger-Transformer and append structured output to the document markdown.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to modify an existing rough cut — shorten, lengthen, remove a clip, reorder, change BGM, adjust pacing. Works by generating a review_patch and re-compiling.

日本語の概要は準備中です。原文の説明を表示しています。

Finish an existing Japanese business talking-head, lecture, interview, or testimonial rough cut as a credibility-first vertical social video. Use when the user wants an SNS short that preserves the speaker's meaning while tightening repeated ideas, adding a first-second thesis, readable full captions, restrained punch-ins, speaker identification, a registered CTA card, dialogue-first audio, and retention QA.

日本語の概要は準備中です。原文の説明を表示しています。

Communicate between existing local Codex App tasks that share one working directory. Use when the user asks to list or inspect same-directory tasks, send one a message or context handoff, ask a task to return a result, wait for that result, continue a request that needs user input, present an inbound task reply, or explicitly broadcast a one-way notice. Also use for Codex delegations that invoke $codex-task-messenger or contain its message-card metadata.

日本語の概要は準備中です。原文の説明を表示しています。

照片电影感调色、低饱和冷暖分离、先保原片再调色。用确定性像素处理而不是生成式重绘；先确认用途与色彩输入，输出带 sRGB ICC 的新图和隐私优先的参数/QA 边车。支持 8-bit SDR JPEG/PNG 的 render、固定三选候选小样 candidates 与数值 QA，以及原尺寸前后对比 compare；card、LUT 尚未实现。不负责 RAW 显影、修图换景、冲印交付或精确复刻电影/胶片。

日本語の概要は準備中です。原文の説明を表示しています。

当想知道一段 AI 回答"哪些能直接用、哪些必须先验证、哪些只能当建议"时使用。按声明类型分四级——低风险草稿材料、观点与策略建议、可查证的事实声明、会改变状态的可执行指令——每级配一句默认处置动作；再按影响半径排序，给出最小的下一步检查。可执行类一律标"不能直接执行；先做代码审阅/沙箱测试/回滚准备"，其中破坏性或提权的命令排在最前。教的是一套可复用的复查习惯，不是替使用者把所有内容都核实一遍：本 skill 只分级、排序、定处置，要逐条核实走 ai-output-fact-checker，整段代码要做入库体检走 ai-code-onboarding-checklist。

日本語の概要は準備中です。原文の説明を表示しています。

当拿到一段 AI 回答、准备直接复制去用之前使用。把回答拆成可检验的声明（链接、库名与版本、论文 DOI、命令行参数、API 名、数字、引文），按"用错了会炸多大"排序，为每条配一条最直接的核验路径；产出声明台账、核验计划和"现在可以用 / 必须先核实"两栏。专门防住引用了不存在的库、论文或命令这种直接复制就会出事的情况；包名存在不等于可信（幻觉出的名字可能已被人抢注），DOI 能解析也要标题对得上。不替使用者背书，核不了就写核不了。与 ai-answer-triage 的区别：那边只分级、定先查哪几条，这里逐条给核验路径；要判断一整段 AI 代码能不能跑，用 ai-code-onboarding-checklist。

日本語の概要は準備中です。原文の説明を表示しています。

当拿到一段 AI 生成的代码，还没决定要不要信、要不要跑、要不要往上继续加东西时使用。先做一遍客观体检：行数与嵌套深度、有没有测试、有没有硬编码密钥或遗留 TODO、依赖是不是真实存在、有没有危险调用（eval/exec、shell=True、递归删除、反序列化、提权、外发网络，按正文给的 grep 模式逐类查）；再给风险评级和下一步动作。老实写明它查不了算法正确性——体检通过不等于代码是对的。回答里文字、事实和代码混在一起时先用 ai-answer-triage 分级；只审相对已有代码的一份改动用 ai-diff-review-protocol。

日本語の概要は準備中です。原文の説明を表示しています。

当 AI 改完代码、要决定这份 diff 放不放行时使用。不逐行精读，按四步走查高风险点——意图是否匹配、有没有碰到边界条件、副作用面有多大、能不能回滚——每步有明确的放过与打回判据。改动文件数、增删行数、动到的迁移/锁文件/CI/配置/鉴权/测试文件和危险 hunk 先由 scripts/diff_risk.py 统计（粘贴进来的 diff 文本也能读，不需要 Git），越过固定阈值时必须人工逐段过。只有口头描述、没有改动内容时不给结论，只列要补的材料。diff 里新写的测试可不可信交给 ai-generated-test-auditor；没有基线的整段新代码走 ai-code-onboarding-checklist。

日本語の概要は準備中です。原文の説明を表示しています。

当使用者给出「说话人名字 + 说话内容 + 主色」，说"做一张车队无线电那种语录卡"、"生成 XX RADIO 的图"、"把这句话做成无线电字幕卡"、"照那个红色波形的格式来一张"时使用。产出单文件 HTML，800×1000 竖版：顶部是名字（主色）加 RADIO（白色）的粗斜体标题、中间一条主色音频波形加通栏横线、下方是主色粗斜体全大写的引号语录、底边一条主色横条，底色固定为近黑。使用者没给图标就不放图标。不生成任何车队或厂商的徽标，不伪造真人说过的话。

日本語の概要は準備中です。原文の説明を表示しています。

Design, refine, implement, or review application UI and user journeys with minimal controls, clear actions, shared components, realistic prototypes, and polished behavior. Use for product screens, forms, onboarding, navigation, settings, and chat/task interfaces; not backend-only work or general graphic design.

日本語の概要は準備中です。原文の説明を表示しています。

Investigate why a defect or insufficiency was introduced and accepted by tracing code, pull requests, task context, and accessible development sessions. Use for second-order learning after a fix, blameless root-cause analysis, or preventing a recurring class of engineering mistakes.

日本語の概要は準備中です。原文の説明を表示しています。

Structure any communication artifact — documents, reports, plans, issue comments, PR descriptions, emails, presentations, slides, charts, and dashboards — using Jean-luc Doumont's "Trees, Maps, and Theorems" method. Use whenever the goal is to get a message across to a busy audience, when asked to improve clarity or structure of writing or slides, or when a deliverable will be judged by whether readers act on it, not just read it.

日本語の概要は準備中です。原文の説明を表示しています。

Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."

日本語の概要は準備中です。原文の説明を表示しています。

Handle user-facing GUI, sign-in, and file handoffs from an isolated Windows agent session; not ordinary headless work.

日本語の概要は準備中です。原文の説明を表示しています。