「2026」の検索結果

生成符合 Kling 3.0（可灵 3.0，快手）规则的视频提示词。三种写法自适应：4 部分基础公式（短视频）/ 5 层进阶公式（剧情+音频）/ 图生视频专用（只描述运动）。Kling 是 2026 年中文理解最强、原生音画同步、最长 2 分钟、支持角色定向发声、Motion Brush 的电影级模型。用于"用 Kling 生成视频"、"可灵 AI 提示词"、"中文视频生成"、"带原生音频的剧情视频"、"图生视频"、"角色对话视频"等触发场景。

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 a weekly Instagram content plan from a theme, audience, and content pillars. Produces a per-day mix of Reels, carousels, single images, and stories, each with a hook formula, angle, posting time, and primary goal, plus a weekly saves-and-shares goal (the 2026 lever), a story cadence, and a balance check across saves, shares, comments, and follows. Use to plan a week instead of ad-hoc posting. Not for drafting one caption (use ig-caption-writer) or one carousel (use ig-carousel-planner).

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 sized Instagram hashtag set for a post using the 2026 reality that 3 to 5 well-chosen tags beat 30 random ones. Sizes each tag as niche (under 50k posts), mid (50k to 500k), or broad (500k+), then assembles a 2-3 niche, 1-2 mid, 0-1 broad mix that a small or mid account can actually rank in, matched to the post content and rotated to avoid spam patterns. Use to choose hashtags for a caption, carousel, or Reel. Not for writing the caption itself (use ig-caption-writer).

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 and rewrite an Instagram profile end-to-end for 2026: profile photo, searchable NAME field weighted with a keyword, @handle, bio (150 chars: value plus topic plus proof), goal-matched link, category label, story highlights, the first-9 grid, and up to 3 pinned posts. Triggers on "review my Instagram profile", "fix my bio", "optimize highlights", "profile audit". The whole follow decision happens on the profile header. Not for writing captions (use ig-caption-writer).

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。

Draft an Instagram caption with the hook in the first 125 characters, a skimmable body, and one clear CTA, using a 2026 hook formula chosen by goal (saves, shares, comments, follows). Runs the humanizer pass and, on approval, publishes with the image or video you supply via the Publora media flow. Use to caption a single image or a Reel. Not for slide-by-slide carousels (use ig-carousel-planner) or auditing a draft (use ig-humanizer --mode audit).

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。

Reverse-engineer the hook from a viral Instagram Reel or carousel, given a URL or a pasted caption. Identifies which of the 10 canonical 2026 formulas it uses (number-first, contrarian, relatable, confession, listicle, before/after, myth-buster, framework, pattern-interrupt Reel, how-I Reel), explains why it worked, names the primary goal, and returns a blank template mapped to your topic. Use to learn from a post you admire. Not for writing your own (use ig-caption-writer or ig-carousel-planner).

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。

Plan, draft, audit, and publish content for Instagram. Use when the user wants to write a caption with a first-125-char hook, plan a slide-by-slide carousel, reverse-engineer the hook from a viral Reel or carousel, size hashtags the 2026 way, remove AI tells from a caption, or plan a week of Reels and carousels. Instagram requires media on every post, so the skills write the caption and the user supplies the image or video; posts publish via the Publora API draft, upload, schedule flow.

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 AI tells readers react to in an Instagram caption or carousel slide: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, performed sincerity, emoji storms; caps em dashes. Includes --mode audit (first-125 hook, length, hashtags, emoji, CTA, media) and --mode profile. Not for beating AI detectors (no edit reliably does). Not for writing from scratch (use ig-caption-writer or ig-carousel-planner). Keywords: humanize, de-AI caption, audit before posting.

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。

Plan an Instagram carousel slide by slide, up to 10 slides, with a hook slide that opens a loop, value slides that front-load the payoff, and a payoff slide that earns the save and follow. Picks a 2026 carousel formula (listicle, before/after, myth-buster, framework) by goal (saves, shares, follows), drafts each slide's text plus the caption, then publishes the images you supply via Publora. Use to structure a multi-slide carousel. Not for single-image captions (use ig-caption-writer).

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。

Convert AutoCAD DWG files (1983-2026) to Excel databases using DwgExporter CLI. Extract layers, blocks, attributes, and geometry data without Autodesk licenses.

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。

Microsoft 365 / Entra ID attack - tenant discovery, user enumeration via OneDrive differential (2026 verified), AADSTS code reference, Smart Lockout math (hard cap 1-2 attempts/user), ROPC validation, Conditional Access mapping. Wiki-first, FIND schema output.

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。

Parallel multi-AI cross-validation research workflow (大版本). Dispatch N internal sub-agents + grok + gemini in parallel, automatically cross-validate findings, tier by confidence (strong consensus / partial / conflict / insufficient), generate tiered action items with arbitration. Use when user says "多 AI 调研", "交叉验证", "独立共识", "三脑调研", "multi-ai research", "parallel research", "cross-validate", or needs deep research that benefits from internal data + external 2026 consensus. NOT for quick factual Q&A, pure code reasoning, or tasks needing deep project 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。

Break a PRD into independently-workable issues and write each as a local markdown file in issues/. Use when the user wants to turn a PRD into a list of concrete 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。

Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

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 a PRD from the client brief and write it as a local markdown file in issues/. Use when the user wants to turn a client request into a structured PRD.

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。

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

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。

Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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 seeing errors about better-sqlite3 native module, NODE_MODULE_VERSION mismatch, "was compiled against a different Node.js version", or similar native binding errors.

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。

Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated 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。

Generate music with Suno (v5 / v5.5 era, 2026) and advise a production team on what they may legally do with the output. Use this skill when a user wants to create, extend, remaster, or stem-separate a track in Suno; craft Suno prompts (style field, lyrics/metatags, exclude-styles, personas, custom voices); choose a subscription tier for a given use; or get accurate answers about ownership, commercial rights, distribution/monetization on DSPs, the Warner Music Group settlement, the ongoing label/publisher litigation, and Suno's API situation. Do not use it for other music generators (Udio, ElevenLabs Music, Google Lyria) except by explicit contrast, and do not use it as a substitute for a lawyer on a specific commercial deal.

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。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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。

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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。

Complete marketing pipeline for apps, dev tools, libraries, and SaaS products in 2026. Generates positioning, listing/ASO/package-SEO assets, press kit, launch plan, social calendar, email sequences, growth/referral strategy, paid playbook, retention, and community plan. Adapts automatically to app vs dev-tool vs SaaS. Trigger on: marketing, launch plan, app launch, go-to-market, ASO, press kit, product hunt launch, hacker news launch, dev tool marketing, growth strategy, launch checklist, marketing plan, distribution plan.

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。