348 件 ・ 関連度順
概要と使いどころ
Route full software-development architecture work from product intent through design, implementation, testing, release, and operations. Use when the user asks for a complete development architecture, wants to know which Spellbook skills to combine, needs an execution path across PRD/spec/API/data/security/performance/release/SRE, or asks to turn an idea or repo into a production-ready engineering 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。
Write model-native Seedance 2.0 video prompts from text, images, video, or audio references. Use for text/image generation, multimodal reference, video editing, extension, track fill, or combined tasks that need stable subjects, explicit asset roles, coherent event order, camera, sound, visible text, and a 4–15 second feasibility check.
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 original short videos using the 分组对话覆盖｜两边接力后全桌合流 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.
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 this skill to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness.
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 prioritized CVE watchlists and actionable security recommendations for repositories. Use when analyzing CVE scan results, creating security reports, prioritizing vulnerability remediation, or generating security gate reports for CI/CD. Takes CVE scan results (JSON/SARIF from npm audit, pip-audit, Snyk), reachability analysis, and cutoff date as input. Combines severity, reachability, exploitability, and dependency criticality to rank CVEs by practical risk. Outputs markdown reports with concrete next-step guidance (immediate upgrade, monitor, ignore with justification, apply mitigation) suitable for issue trackers, security reviews, and CI security gates.
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 and validate Blender 5.2 Cycles 3D-to-2D materials using four camera-plane-offset Shader Raycast tests to derive an editable silhouette mask, combined with Cycles-compatible toon or flat shading. Use when a Cycles render needs an in-material outline without Shader to RGB.
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。
Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.
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。
Condense Stage 3 combined discussion content into compact markdown context for Stage 4 multi-round follow-up drafting.
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。
Prepare for sales conversations with comprehensive account intelligence — company news, hiring signals, product launches, social activity, and decision-maker profiles. Combines firmographic data, news monitoring, and social signals for sales-ready briefings.
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。
Identify emerging trends early by monitoring signals across Twitter trending, TikTok viral content, YouTube rising videos, Google News velocity, and Reddit community buzz. Combines platform-specific trend indicators into a unified trend confidence score.
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。
Research potential leads with verified contact information, professional backgrounds, company context, and social presence. Combines LinkedIn profiling, email verification, company enrichment, and social signals for sales-ready prospect intelligence.
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。
Research startups and emerging companies with funding data, team composition, product positioning, market traction, and news coverage. Combines Apollo firmographics, LinkedIn team data, and web intelligence for comprehensive startup due diligence.
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。
Monitor any website for content changes, availability, performance, and security status. Combines page scraping, screenshot comparison, SSL monitoring, and security header checks for comprehensive website health and change detection.
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 targeted outreach lists with verified contact information, professional context, and conversation starters. Combines Apollo company data, LinkedIn profiling, and email verification to produce ready-to-send outreach campaigns for sales, PR, and partnership 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。
Research potential partners by analyzing company profiles, product fit, market positioning, team background, and mutual connections. Combines firmographic data, content analysis, and social signals for partnership qualification and relationship mapping.
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。
Enrich any company with comprehensive firmographic data including industry, size, revenue, funding, tech stack, beneficial ownership, and regulatory status. Combines multiple data sources for complete B2B company intelligence and due diligence.
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。
Enrich any entity — companies, domains, emails, or people — with verified data from multiple sources. Combines Apollo, Strale, LinkedIn, and Akta data to fill gaps in your CRM, lead databases, or research datasets with production-quality enrichment.
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。
Predict protein-coding genes in eukaryotic genomes using BRAKER3 for combined RNA-seq and protein evidence, or GALBA for protein-only evidence. Runs Augustus with trained parameters for accurate gene models. Use when annotating a newly assembled eukaryotic genome or improving existing gene predictions.
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。
Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.
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。
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
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。
Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.
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。
Full enhancement pipeline for an existing Lattice skill — rewrites a molecule or atom to modern-capable-model grade: restores degraded grammar, deduplicates, hardens gates and branching to one-unambiguous-action precision, verifies zero behavioral loss against a pre-capture diff, then runs the combined QA gate (skill-review, skill-tighten, skill-validate) and an independent fresh-context verifier, fixing everything found. Consumer-first: optimized for first-time open-source users on fresh repos with minimal .lattice/ setup, running capable models. Use when the user says 'enhance this skill', 'upgrade this molecule', 'modernize this skill', 'optimize this skill', 'make this skill production grade', 'full enhancement', 'polish this molecule like design-blueprint', or names a molecule/atom to overhaul. For gap-finding only use skill-review; for conventions only use skill-validate — this skill runs the whole loop end to end.
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。
Apply the changes from all open Renovate bot pull requests of a GitHub repository into the local working tree. Use this skill when asked to apply, merge locally, consolidate, batch, or try out Renovate/dependabot-style dependency update PRs so the combined upgrade can be built and tested in one go. This skill only modifies local files; it never commits, pushes, merges, or closes anything on GitHub.
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。
Symfony UX frontend stack -- decision tree and orchestrator for choosing between Stimulus, Turbo, TwigComponent, LiveComponent, UX Icons, and UX Map. Use when the user is unsure which tool fits, wants to combine multiple UX packages, or asks a general frontend architecture question in Symfony. Also trigger when the user asks "which UX package should I use", "how to make this interactive", "should I use Stimulus or LiveComponent", "how to structure my Symfony frontend", "what is the difference between Turbo and LiveComponent", "should this be a Frame or a LiveComponent", "how do these UX packages work together", "what is the Symfony way to do frontend". Do NOT trigger when the user clearly names a specific tool (stimulus, turbo, twig-component, live-component, ux-icons, ux-map) -- defer to the specialized skill instead.
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。