「generating」の検索結果

Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.

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Generate Word documents programmatically with the docx library — create paragraphs, tables, images, headers, footers, numbered lists, and styled content. Use when tasks involve generating contracts, proposals, resumes, or any .docx output from application data in Node.js.

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DALL-E 3 API for AI image generation — text-to-image, image editing, and variations via OpenAI. Use when generating images from text prompts, creating product visuals, illustrating content, or editing images with an inpainting mask.

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Parse and generate CSV files with the csv package — stream large files, handle custom delimiters, transform records, validate data, and generate CSV output from objects. Use when tasks involve data import/export, ETL pipelines, processing uploaded CSV files, or generating downloadable reports.

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Draw 2D graphics with the HTML5 Canvas API — shapes, text, images, gradients, transformations, pixel manipulation, and offscreen rendering. Use when tasks involve generating images server-side (with node-canvas), creating charts, image compositing, watermarking, or browser-based drawing applications.

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Automates invoice generation, billing workflows, payment tracking, and revenue recognition for SaaS and service businesses. Use when building billing pipelines, generating invoice PDFs, usage-based invoicing, subscription management, payment reminders, or financial reporting. Trigger words: invoice, billing, payment, subscription, usage billing, revenue recognition, accounts receivable, dunning, proration, Stripe billing, generate invoice, create bill.

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Assists with managing versioning and changelogs in JavaScript/TypeScript monorepos using Changesets. Use when automating semantic versioning, generating changelogs from PRs, coordinating multi-package releases, or publishing to npm. Trigger words: changesets, versioning, changelog, npm publish, monorepo release, semver.

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Create ZIP and TAR archives programmatically with Archiver — bundle files, directories, and streams into compressed archives with progress tracking. Use when tasks involve generating downloadable bundles, backing up directories, creating deployment packages, or building export features that zip multiple files together.

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Reference for architectural dimensions, proportions, and building codes for AI-generated 3D/2D models. Use when: generating floor plans, creating 3D buildings, validating architectural designs, ensuring correct human-scale proportions in models.

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Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.

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Build AI agents that automate scientific research — hypothesis generation, experiment design, data analysis, and paper writing using agentic tree search. Use when: automating research workflows, generating and testing hypotheses, building AI-powered research assistants.

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The art and science of narrative-driven brand communication. This skill transforms brand messages into compelling stories that connect emotionally, build memory, and drive action. In a world of infinite content, story is the only thing that cuts through. Great brands don't sell products—they tell stories. Apple tells the story of creative rebels. Nike tells the story of human potential. Patagonia tells the story of environmental stewardship. This skill teaches you to find, craft, and tell your brand's story across every touchpoint. AI amplifies storytelling capability—generating variations, adapting across channels, maintaining consistency—but the human insight that identifies the true story remains essential. This skill bridges narrative craft with AI execution. Use when "story, storytelling, narrative, brand story, origin story, hero journey, emotional connection, customer story, testimonial, case study, brand film, about us, storytelling, narrative, brand-story, emotional, hero-journey, content" mentioned.

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Mastery of AI image generation across the full spectrum: Midjourney for aesthetic perfection, Flux for prompt adherence, DALL-E 3 for concept clarity, Stable Diffusion for control, and Imagen 3 for photorealism. This skill transforms text into visual reality at the speed of thought. We've moved beyond "AI can make pictures" to "AI is the fastest concept artist, product photographer, and visual designer ever created." The skill isn't just prompting—it's understanding how each model thinks, what makes images work, and how to systematically produce exactly what you envision. The best AI image generators have internalized composition, lighting, color theory, and style—not by studying art, but by generating thousands of images and learning what works. They're visual directors who happen to work in text. Use when "AI image, generate image, Midjourney, DALL-E, Flux, Stable Diffusion, Imagen, text to image, AI art, AI photo, AI illustration, AI visual, AI graphics, generate picture, ai-image, midjourney, dall-e, flux, stable-diffusion, imagen, generation, text-to-image, visual, art" mentioned.

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The fusion of trend prediction and AI content generation: detecting emerging trends before they peak and generating content that rides—or creates—the wave. This is marketing alchemy, transmuting data signals into viral gold. Traditional trend marketing is reactive: see trend, create content, hope you're not too late. AI Trend Alchemy is predictive: detect signal, generate content, be there as the wave crests. The combination of AI trend detection and AI content generation creates a feedback loop that can outpace human competition. This skill combines data science intuition with creative generation, reading the digital tea leaves while simultaneously conjuring the content that captures the moment. It's part analyst, part creator, part prophet. Use when "trend, trending, viral, predict, emerging, what's next, cultural moment, before it peaks, early signal, catch the wave, trend prediction, going viral, trend, prediction, viral, cultural, emerging, timing, alchemy, detection" mentioned.

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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

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Ad Creative workflow skill. Use this skill when the user needs Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Ad Creative workflow skill. Use this skill when the user needs Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Generate behavioral/feature documentation for web apps using code-first analysis. Reads source code via tree-sitting to produce _FEATURES.md, with optional visual verification via browser automation. Companion to tree-sitting. Use when documenting app behavior, creating feature inventories, generating behavioral ground truth for agents, or before modifying UI code. Triggers on "map features", "document app behavior", "feature inventory", "what does this app do".

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Generates git patch files from codebase modifications for local application. Use when user mentions patch, diff, export changes, bring changes back, apply locally, or after editing uploaded codebases.

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Turn a short video into a narrated comic strip. Use when the user uploads a video (.mov/.mp4/etc.) and asks to make a comic from it, narrate it, draw it, Attenborough it, storyboard the clip, or otherwise wants a stills-plus-narration comic strip derived from footage. Covers probing and extracting frames, GROUNDING the actual storyline (native Gemini video parse or frame reading), composing narration in a chosen voice, generating comic panels with Gemini image models anchored to the real stills, QA-ing the panels, and compositing a titled strip. Do NOT use for plain video transcoding or trimming (that is processing-video) or for original illustration from a text prompt (that is invoking-gemini).

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Create beautiful, accessible user interfaces with shadcn/ui components (built on Radix UI + Tailwind), Tailwind CSS utility-first styling, and canvas-based visual designs. Use when building user interfaces, implementing design systems, creating responsive layouts, adding accessible components (dialogs, dropdowns, forms, tables), customizing themes and colors, implementing dark mode, generating visual designs and posters, or establishing consistent styling patterns across applications.

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Strategic product leadership toolkit for Head of Product covering OKR cascade generation, quarterly planning, competitive landscape analysis, product vision documents, and team scaling proposals. Use when creating quarterly OKR documents, defining product goals or KPIs, building product roadmaps, running competitive analysis, drafting team structure or hiring plans, aligning product strategy across engineering and design, or generating cascaded goal hierarchies from company to team level.

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This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.

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Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.

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