「arr」の検索結果

把一句话需求转成可复现、可验收视频工程的乔木智能剪辑导演。Use when the user asks to create, plan, edit, remix, explain, narrate, subtitle, animate, composite, or render a video—including one-line requests such as “制作一个科普视频：介绍 LLM 中的 RL”. Builds English-learning movie mixes, person profiles, explainers, code-drawn motion graphics (UI morph loops, showreels, kinetic-type promos, line-art/pixel/Three.js animation, lyric MVs), product launch videos, cinematic shorts, social clips, course/PPT videos, AI image/video/TTS/music-assisted stories, stock-footage stories, or multi-source montage. Routes across 33台词, ClipSeek/open media, local files, ListenHub, image generation, HTML/SVG/Canvas motion graphics with beat-grid storyboards and determinism probes, Manim, ffmpeg-full, captions, sound design, frame-0 video covers with YouTube thumbnails, and release checks.

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。

Remove the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".

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 a user wants step-by-step help making an audio-first image-story, AI narration, slideshow, illustrated story, AI 讲书, 图片联播, 有声故事, or 静态图叙事视频 with Codex or WorkBuddy; not for a standalone script, single image, or ordinary video editing.

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。

Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery issues.

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。

Audit the instructions an agent already carries — CLAUDE.md, AGENTS.md, skills, tool descriptions — for contradictions, over-constraint, and duplication, then propose a cut list. Use when an agent ignores its own instructions, when a CLAUDE.md has grown bloated, or when the user asks to audit or rightsize their agent context.

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 and analyze key clauses from construction contracts. Identify payment terms, change order procedures, dispute resolution, warranties, and risk allocation provisions.

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。

Manage project closeout activities. Track completion of documentation, warranties, and final inspections.

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。

Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table. Every value carries its accession number, form, fiscal period, filed date, XBRL tag and a link to the filing, and the tool handles fiscal-year ends, quarterly vs YTD vs TTM, derived Q4, restatements and units. Use this whenever the user wants 10-K numbers or 10-Q numbers, says "pull financials for", asks for public company revenue, net income, EPS, cash, debt or capex, wants comps or a competitor side-by-side, a credit check, prospect research, or model inputs, or mentions SEC filings or EDGAR, even if they never say XBRL. Free and primary-source, no paid data terminal needed. Do not use it for private companies, stock prices or valuation multiples.

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。

Reviews pitch decks and investor presentations. Reads slide content, evaluates narrative flow, problem/solution clarity, market sizing, competitive positioning, financial projections, team credibility, and ask clarity. Generates a scored pitch-review.md with slide-by-slide feedback, overall score, top improvements, investor objection predictions, and comparisons to successful decks. Use when reviewing fundraising materials, investor decks, or pitch presentations.

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。

Professional narrative style with line breaks, hashtag strategy, and hooks in first 2 lines to avoid truncation

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 a personalized product-demo or sales-walkthrough video in HyperFrames for a specific prospect or account — narrated screen-by-screen, branded to them, ending in a clear next step. Turns a generic demo into a "this was made for me" asset for outbound, follow-up, or deal acceleration. Use when you want to send a tailored video instead of booking another live call.

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。

Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.

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。

Turn a website URL, product docs, or a founder's rambling description into a tight 60-90 second explainer video built in HyperFrames -- problem/solution narrative, animated product walkthrough scenes, captions, and voiceover, ready for the homepage hero, sales emails, and onboarding.

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。

Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.

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 a quarterly business review from a client folder -- reports, usage exports, support tickets, meeting notes, emails. Extracts delivered value with receipts, surfaces risks before the client does, and drafts the QBR narrative plus expansion asks. For agencies, consultancies, and CS teams.

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 a coding session is burning context fast, output is drowning in noise, the model keeps forgetting earlier decisions, or the user asks to reduce tokens, stop the verbose narration, work in a huge repo, or make a long session survive. Covers what to read, what to summarize, what to write to disk, and when to start fresh.

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。

ElevenLabsで生成したナレーション音声の品質を自動検証するスキル。 「ナレーションチェック」「音声確認」「発音チェック」等のリクエストで発動。

Refines an active research session by drilling deeper into a subtopic. Use after tome:research to narrow results to a specific channel or angle.

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。

Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions. Use when reversibility score is low and adversarial review is warranted.

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 writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper.

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。

Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.

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。

Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.

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 the einops Python package for readable tensor rearrangement, reductions, repetition, named-axis einsum, packing, framework layers, and repository maintenance workflows.

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 Diffusion Policy for robot imitation-learning configs, zarr replay data, policy/model interfaces, training/evaluation workflows, and safety-gated real robot operations.

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。