600 件 ・ 関連度順
概要と使いどころ
Proofread and correct text for grammar, spelling, punctuation, style, clarity, and consistency, with support for multiple style guides and readability analysis. Use when the user requests proofreading or provides relevant inputs for this workflow.
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Translate text between languages with cultural adaptation, terminology consistency, and support for multiple document types including technical docs, marketing copy, and UI strings. Use when the user requests translation or provides relevant inputs for this workflow.
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Define, deploy, and manage cloud infrastructure as code using tools like Terraform, Pulumi, CloudFormation, and CDK, ensuring consistency, repeatability, and version control. Use when the user requests infrastructure as code or provides relevant inputs for this workflow.
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Vaccine pipeline analysis: immunogenicity and seroconversion studies, lot-consistency trials, healthy-population efficacy designs, and age-cohort bridging, with the ACIP gate built into the value path across the med universe.
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Think and work like an expert Distributed Systems Researcher. Use when a task calls for Distributed Systems Researcher judgment. Reasons from failure models, consistency contracts, and tail-latency-and-recovery performance through TLA+ model checking, Jepsen and Porcupine linearizability checking, and YCSB/DeathStarBench benchmarking with iptables and kill -9 fault injection, while treating unbounded leases and split-brain, clock skew under NTP, and GC-induced p99 spikes as first-class failure modes.
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Analyze influencer performance metrics across TikTok, Instagram, YouTube, Bilibili, and Kuaishou. Compare engagement rates, audience growth, content consistency, and cross-platform presence for data-driven partnership decisions.
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Plan and integrate ShengShu Vidu Open Platform video generation with current model/mode selection, reference consistency, pricing, exact approval, async task handling, and safe artifact custody. Use for Vidu API text-to-video, image-to-video, start/end-frame, multi-subject reference, media-reference, or Q2 multi-frame work; do not conflate the API with vidu.com consumer plans or Vidu-S1 streaming digital humans.
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Produce video (and its supporting stills) through Higgsfield (higgsfield.ai), a multi-model creative platform that fronts third-party video models (Kling, Veo, Seedance, Wan, MiniMax, and, until retirement, Sora) plus Higgsfield's own control layer — Soul / Soul ID image generation, the DoP camera-preset engine, and the Cinema Studio filmmaking suite. Use this skill when a request routes generation through Higgsfield specifically, when the user wants aggregator-level model switching and cinematic camera/effect presets in one workspace, when planning character consistency via Soul ID, or when deciding whether to go through Higgsfield versus direct to an underlying model provider. Covers model selection, per-model prompt strategy, credit economics, the (partial) API surface, capability limits, and rights/consent policy for generated and uploaded real-person footage.
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Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video skill).
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Produce full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.
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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.
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End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.
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Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.
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AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
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。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
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。
Maxim Master Design Intelligence — full-spectrum UI/UX orchestration. Activates all design sub-domain skills in parallel. Use when a task spans multiple design domains simultaneously: product design strategy, interaction design, information architecture, usability heuristics, design critique, cross-platform consistency, design leadership. The master conductor of all Maxim design agents.
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 design token usage across a product for consistency and coverage.
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Fast quality gate — detects stack, runs static analysis, checks cross-layer consistency, fixes issues. Run between every pipeline phase, before every commit push, after every feature batch. Lighter than /analyze but catches the same critical 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。
Brand voice, visual identity, messaging frameworks, asset management, brand consistency. Activate for branded content, tone of voice, marketing assets, brand compliance, style guides.
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Maxim Brand Intelligence — brand identity, voice architecture, visual identity systems, messaging frameworks, brand consistency enforcement. Activate for brand strategy, brand audits, brand guidelines, tone of voice, asset compliance, color palette, typography specs. Powered by Maxim behavioral science layer.
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。
Brand voice, visual identity, messaging frameworks, asset management, brand consistency. Activate for branded content, tone of voice, marketing assets, brand compliance, style guides.
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 or modify a konsistent.json configuration file that enforces structural conventions in a TypeScript codebase. Use when the user wants to enforce consistent code structure, validate exports/imports across files, ensure directories contain required files, enforce naming patterns, add/remove/update convention rules, configure case map overrides for acronyms or special casing (kebabToPascalMap, kebabToCamelMap), or troubleshoot why konsistent is reporting violations. Triggers on: "konsistent", "konsistent.json", "enforce conventions", "structural consistency", "consistent exports", "consistent structure", "code conventions config", "add convention", "update convention", "fix konsistent errors", "case map", "case override", "acronym casing".
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 engineering principles for making TUI interfaces feel polished. Use when building Textual widgets, reviewing TUI code, implementing transitions, focus states, alignment, spacing, or any visual detail work. Triggers on TUI polish, design details, "make it feel better", "feels off", layout alignment, color consistency, responsive sizing.
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。
Document development environment requirements for team consistency and AI agent understanding during PRD v0.6 Architecture. Triggers on requests to define environment setup, document tooling, create dev setup guide, or when user asks "what tools do I need?", "environment setup", "dev environment", "CLI requirements", "project setup", "onboarding setup". Consumes TECH- (stack selections), ARC- (architecture decisions). Outputs ENV- entries for development, CI/CD, and infrastructure environments. Feeds v0.7 Build Execution.
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。