Results for “script”

Short-drama and product-video development skill for planning drama projects through a bundled Busabase App-in-Skill UI, with each project linked to a specified HyperFrame project and each episode linked to a HyperFrame composition for final motion/video tuning. Use when the user asks to create, edit, organize, or review short-drama scripts, product explainer episodes, character consistency cards, series bibles, episode beat sheets, storyboard shots, HyperFrame project/composition links, production checklists, or a drama planning workbench.

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Plan, verify, and track demo/marketing video production in Busabase — idea to hook/pain-point/storyboard, codebase claim verification, recording progress, post-production handoff, and HyperFrame (Remotion) linkage. Use when the user invokes $kelly-demo-video-factory or /kelly-demo-video-factory, wants to plan a product demo video, write a storyboard, verify a video script's product claims against the codebase, track which shots are recorded, or hand a finished storyboard off to editing/Remotion.

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Busabase App-in-Skill dashboard visualizing a generated MOCK fleet of LLM agents running behind a shared AI gateway for a generic organization. Use when the user invokes $kelly-agent-observability or /kelly-agent-observability, wants to review agent health, call volume, latency, error rates, cost, or trace-level chain breaks across an agent fleet. This is a demo/reference dashboard over a generated mock fleet snapshot seeded into Busabase by a trusted script — never a live monitoring integration, never a real gateway or real agents.

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Assess a company's economic moat, EBITA margin trajectory, and organic growth trajectory from analyst reports, earnings transcripts, or long-form articles. Produces structured qualitative outlook that feeds into the Financial Modeling skill's assumption generation.

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Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.

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Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.

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Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.

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Execute a single deterministic Python script to harmonize units, create/update metadata, move files to output_data/, and clean up input_data/.

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Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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Vite (フロントエンドビルドツール・開発サーバー, v8 / Rolldown) のリファレンス。 vite.config, defineConfig, server, build, optimizeDeps, HMR (import.meta.hot), import.meta.env, import.meta.glob, configureServer, transformIndexHtml, Environment API (DevEnvironment / ModuleRunner), SSR, library mode, JavaScript API, v7 から v8 移行。 テストは vitest スキル。

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Vercel AI SDK (TypeScript LLM ツールキット, ai / @ai-sdk/*, v7)。 generateText, streamText, Output, ToolLoopAgent, createMCPClient, useChat, UIMessage, embed, generateImage, wrapLanguageModel, WorkflowAgent, HarnessAgent, AI Gateway, @ai-sdk/rsc、 v4 から v7 への移行。

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Model Context Protocol (MCP 仕様 2026-07-28, modelcontextprotocol.io) リファレンス。 JSON-RPC, server/discover, tools/call, subscriptions/listen, MRTR (InputRequiredResult), CacheableResult, Streamable HTTP, CIMD, MCP Apps (ui://), MCP Registry (server.json, MCPB), tasks 拡張。ホスト設定・SDK は別スキル。

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React Native (Meta 製クロスプラットフォームネイティブアプリフレームワーク, react-native 0.87 / 0.88) の リファレンス。View, Text, FlatList, Pressable, TextInput, Animated, StyleSheet, Flexbox, Platform, New Architecture (Fabric, TurboModules, Codegen), Hermes, Metro, Fast Refresh, TypeScript, React Native DevTools, Android / iOS 統合、upgrade (Upgrade Helper)・0.84〜0.88 差分。

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Apple Intelligence (Foundation Models / Evaluations / Image Playground) の API リファレンス。 LanguageModelSession, SystemLanguageModel, PrivateCloudComputeLanguageModel (PCC), Generable, GenerationSchema, Tool, OCRTool, Transcript, DynamicInstructions, Attachment, Evaluation, ModelJudgeEvaluator, ToolCallEvaluator, ImagePlaygroundSheet, ImageCreator, Foundation Models SDK for Python (apple-fm-sdk)。

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当需求还只是"帮我写个东西""让 AI 做这个任务"这种一句话时使用。用至多 3–5 个高杠杆追问把它补成一份可交付的任务简报——目标、产物、受众、已有素材、约束、验收标准、不做什么、待确认假设，外加一段可直接粘贴的执行 prompt。简报的九个标题保持英文（内容用什么语言都行），字段完整性由 scripts/check_brief.py 校验。产出不绑定任何一家模型，人或 Agent 都能照着执行。不适用于：写歌交给 lyric-concept-builder，复习计划交给 deadline-to-study-plan，建模题交给 modeling-problem-reading-coach，物理题交给 physics-problem-router；使用者坚持直接要成品、一个问题都不肯答时也不用本 skill。

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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。

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当机制清单已经排好、需要给每个机制配数值锚点并合成一个站得住的量级结论时使用。把估算写成显式的因子分解，每个因子给低／中／高三值与证据档位，算出区间而不是装饰性的小数位，再与参考区间和守恒量核对。worksheet 的结构与算术由 scripts/check_estimate.py 校验。不负责发现或排序机制——那是 physics-mechanism-decomposer 的事。

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当使用者说"把照片做成极简海报"、"做一张几何风格的海报"、"把这张照片抽象成海报"时使用。Turn a photographer-provided description of an existing photo into a minimal, geometric SVG poster with an explicit visual abstraction, restrained palette, grid, typography, bleed, and deterministic validation. Use when the user wants to reinterpret one of their photos as a stylized poster rather than retouch or reproduce the photo.

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当问「这条公式怎么来的」「为什么长这样」，或说「帮我推导／推出 X 公式」时使用。从定义或守恒律出发重建标准公式，每一步标出用的是哪条原理、这一步在物理上做了什么；假设写在它真正被用到的那一步，不堆在开头。终式可用 scripts/compare_formula.py 与标准式做符号比对。会被评分的推导：结构与理由可以给，代数由本人做。要逐步核验一份已写好的推导，用 derivation-step-checker。

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当有一条公式、标度律或模型结果，想在相信它之前查它在极限下还讲不讲得通时使用。先找出控制参数与无量纲组，挑最小一组有用的极限，在动手算之前先写下物理上应该发生什么，再做数学化简两相对照；专抓在某个极限下发散、变号、或丢掉已知特例的表达式。manifest 的完整性由 scripts/check_limit_manifest.py 校验。

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当手上有一份已经写好的推导、要逐步核验时使用。每一步查四件事：代数是否等价、量纲是否一致、符号有没有翻错、以及这一步用的定律在此处适用条件是否仍然成立——变质量、非惯性系、准静态假设中途被悄悄破坏是高频翻车点。scripts/check_derivation.py 做前三项的确定性检查，第四项是人的判断。只核验已有推导，不代写推导。

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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.

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Generate release notes from merged PR history and commits. Categorizes changes by type and creates GitHub releases with semantic versioning. Use when the user asks to create a release or generate release notes (e.g., "リリースノート", "release note", "リリース作成"). Do NOT use for changelogs, commit summaries, or PR descriptions.

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Deploys and configures Zeek (formerly Bro) network security monitor to passively analyze network traffic, generate structured logs, detect anomalous behavior, and create custom detection scripts for threat hunting and incident response.

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