1,247 件 ・ 関連度順
概要と使いどころ
Save, organize, and describe reusable analysis artifacts such as SQL, result snapshots, CSV exports, summaries, caveats, plots, and report-ready files. Use when users ask to save, export, share, cite, reproduce, or organize data-analysis 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。
Save, organize, and describe reusable analysis artifacts such as SQL, result snapshots, CSV exports, summaries, caveats, plots, and report-ready files. Use when users ask to save, export, share, cite, reproduce, or organize data-analysis 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。
Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).
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。
Generate 5 different narrative scripts for visual slideshows from a single positioning angle. Each script uses a different storytelling framework (AIDA, PAS, BAB, Star-Story-Solution, Stat-Flip) producing genuinely different stories, not layout variations. Make sure to use this skill whenever the user wants slideshow scripts, TikTok content scripts, carousel copy, narrative frameworks for visual content, or says anything about writing scripts for slides or social media storytelling. Even 'write me some TikTok content' or 'I need carousel copy' should trigger this. Outputs structured YAML content specs that chain directly to /paper-marketing for visual 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。
Build a persistent visual brand identity for image generation. Defines how the brand looks visually — aesthetic, lighting, composition, mood — and writes it to brand/creative-kit.md so /image-gen and other creative skills produce consistent on-brand visuals. Three modes: Extract (from website/URL), Build (interview), Reference (mood board/examples). Use when starting any project that needs images, when the user says "visual style", "brand aesthetic", "image style", "visual identity", "how should our images look", "build visual brand", "define our look", or when /image-gen outputs feel generic because no visual style exists yet.
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。
Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.
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 feedback collection for AI outputs. TRIGGERS - Use when user needs help with ai-feedback-collector related tasks.
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。
Create evaluation frameworks for AI outputs with rubrics and scoring. TRIGGERS - Use when user needs help with ai-evaluation-framework related tasks.
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 content filtering for AI outputs. TRIGGERS - Use when user needs help with ai-content-filter related tasks.
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 a conversion loop for a one-person company from reach to lead capture to purchase. Use when Codex needs to explain conversion concepts when needed, verify MVP prerequisites, ask one question at a time, present multiple conversion-path options, and write user-confirmed outputs into `opc-doc/`.
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。
Define the smallest viable experiment and MVP for a selected one-person company opportunity. Use when Codex needs to explain what MVP means when needed, verify prerequisites, ask one question at a time, present multiple MVP options, and write user-confirmed outputs into `opc-doc/`.
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。
Find and position a viable niche market for a one-person company by combining market mapping and customer segmentation. Use when Codex needs to explain niche concepts when needed, check founder-resource prerequisites, ask one question at a time, generate multiple niche options, and write user-confirmed positioning outputs into `opc-doc/`.
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 a viable business model for a one-person company using Lean Canvas and a simplified Business Model Canvas. Use when Codex needs to explain the business-model concepts when needed, verify niche and value-proposition prerequisites, ask one question at a time, present multiple model choices, and write user-confirmed outputs into `opc-doc/`.
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 value propositions for candidate customer segments and help the user choose the strongest one. Use when Codex needs to explain jobs, pains, and gains when needed, check niche-positioning prerequisites, ask one question at a time, present multiple value-proposition options, and write user-confirmed outputs into `opc-doc/`.
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。
International journal matcher — performs L3 6-dimensional matching for SCI/SSCI/EI/Scopus journals, computes conditional acceptance probability with review cycle estimation, applies 4-layer weighted ranking, and outputs tiered submission strategy including CAS warning screening and Chinese scholar friendliness analysis.
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。
Chinese journal matcher — performs L3 6-dimensional matching (keywords, cited direction, trend, solicitation, institution, cross-discipline), calculates conditional acceptance probability with review cycle estimation, and outputs tiered submission strategy (aspiration-stable-safety) using 4-layer weighted ranking.
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。
Efficiently extract and convert the contents of any local file—such as PDF, DOCX, DOC, ODT, RTF, XLSX, XLS, or HTML—into clean, well-formatted markdown saved to disk. Use this skill whenever the user requests to parse, read, or extract information from a file on their computer, including phrases like “parse this PDF”, “convert this document”, “read this file”, “extract text from”, or when a local file path (not a URL) is provided. This skill offers advanced options like generating AI-powered summaries and answering questions based on the file's content. Prefer this tool over `scrape` when handling local files to deliver precise, structured outputs for downstream tasks.
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。
Customize any Cowork skill or workflow to match your specific industry, company, and style. For tuning default skill outputs to your voice, when adapting generic workflows to a specific industry, or during a skill quality review. Use when the user says "customize this skill", "output is too generic", "make this work for my industry", "personalize this workflow", "results do not match my style", or "how do I get better results from Cowork".
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 create, edit, rebuild, or refine editable diagrams in `.drawio` / diagrams.net format, especially for academic figures such as system architecture diagrams, technical roadmaps, workflow diagrams, method pipelines, module relationship diagrams, and experiment flowcharts. Use this skill when the user wants publication-ready outputs with both an editable source file and a PNG export, or when an existing figure image needs to be rebuilt into a clean editable academic-style diagram.
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 an end-to-end GEO visibility measurement through a real AI interface, persist the captures, and return a portable JSON run artifact plus optional PDF/dashboard outputs. Use automatically on an explicit request to measure a brand's AI-search visibility, and as a composable data-collection step inside another agent workflow; the user should not have to launch the pipeline or dashboard manually.
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。
Peer review checklist and quality standards for intelligence products, plus schema validation for graded evidence items and claim tables from /quality-of-information-check and /claim-extraction. Rejects outputs that break the evidence schema or the grading caps. Loaded by the quality-reviewer agent.
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 needing 100% type-safe, schema-valid data extraction from LLMs. Keywords: structured output, JSON schema, Pydantic, Zod, type-safe, constrained decoding, validation, tool use.
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。
Standardized output formats for research, planning, implementation, and review agents. Use when generating agent outputs or parsing agent responses.
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 reliable, safe, and trustworthy agent systems that fail gracefully and operate within bounds. Use when building guardrails, handling edge cases, preventing harmful outputs, monitoring for failures, implementing safety constraints, or designing error recovery. Covers failure modes, constraint systems, oversight mechanisms, and safety validation.
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。