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Director-level AI art prompt creation for Eastern beauty series, including Eastern fantasy / gu feng Chinese beauty character art, classical Eastern beauty, realistic and modern Eastern beauty, Cosplay展会摄影 / official convention cosplay photography with AAA game character campaign and Vogue editorial quality, 古风闺蜜 / AncientFemaleCompanionship two-person ancient Chinese female companionship photography, 东方美学图鉴 / 小红书图鉴 cover and content systems for 四大美人、四大才女、十二花神、东方神女、敦煌飞天、朝代服饰、东方器物、东方神话, 甜系纯欲生活写真 / SweetHomeGirl lifestyle portraits built around realism, feminine charm, romantic feeling, story moment, and natural attraction, new-Chinese-style fashion editorials, magazine portraits, worldbuilding, mythic role design, costume/styling systems, cinematic composition, lighting, negative prompts, and model-specific prompt variants. Use when the user asks for 东方美人, 东方审美, 东方美学图鉴, 小红书图鉴, 图鉴封面, 图鉴正文, 四大美人, 四大才女, 十二花神, 东方神女, 敦煌飞天, 朝代服饰, 东方器物, 东方神话, Cosplay展会摄影, coser, Coser, 漫展摄影, 展会摄影, ChinaJoy, Bilibili World, Tokyo Game Show, 游戏展台, 官方展会摄影, AAA游戏角色Cosplay, 古风闺蜜, AncientFemaleCompanionship, Ancient Female Companionship, 闺阁夜话, 姐妹古风写真, 古代闺阁生活, 灯下梳发, 共读, 夏夜纳凉, 守夜, 江南同行, 湖亭共伞, 山寺同行, 灯会夜游, 柔美, 妩媚, 成熟, 优雅, 高贵, 清冷, 温婉, 英气, 神性, 慵懒, 东方幻想, 古风美女, 古典东方美人, 宋韵, 唐宫, 江南, 洛神, 青瓷, 昆曲, 国风人物, 汉服女子, 写实东方美人, 现代东方审美, 新中式, 东方高级感, SweetHomeGirl, 甜系纯欲生活写真, 甜系纯欲, 甜美女友感, 真实女生, 自然抓拍, 故事感, 女友视角, lifestyle girlfriend portrait, 仙侠/武侠/宫廷/江南/敦煌风 AI 绘画提示词, image prompt optimization, style word expansion, character series concepts, or prompt packs for Midjourney, Stable Diffusion, ComfyUI, DALL-E, Gemini, Seedream, or other image generation tools.

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。

Configuring Salesforce opportunity management: sales stages, sales processes, opportunity record types, Path configuration, opportunity teams, opportunity splits, and forecasting categories. Use when setting up or restructuring the opportunity lifecycle for Sales Cloud. NOT for forecast types, rollup methods, quotas or manager adjustments — use admin/collaborative-forecasts. NOT for agreeing what the stages and exit criteria should be before any build — use admin/sales-process-mapping. NOT for CPQ pricing or product configuration. Keywords: BusinessProcess, sales process, OpportunityStage, ForecastCategoryName, PathAssistant, OpportunitySplit, OpportunitySplitType, OpportunityTeamMember, Opportunity.settings, enableOpportunityTeam, IsClosed, IsWon, IsPrivate.

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。

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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。

Split the most recent git commit into multiple logical commits without interactive rebase. Use when invoked as `$git-split` or when the user asks to split HEAD into smaller commits.

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 adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

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。

Analyzes Windows Security, System, and Sysmon event logs in Splunk to detect authentication attacks, privilege escalation, persistence mechanisms, and lateral movement using SPL queries mapped to MITRE ATT&CK techniques. Use when SOC analysts need to investigate Windows-based threats, build detection queries, or perform forensic timeline analysis of Windows endpoints and domain controllers.

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。

Read, query, and write WooCommerce product variations correctly — the WC_Product_Variable (parent) vs WC_Product_Variation (child) class split, the parent's get_variation_prices( $for_display ) cached aggregation and how to bust the wc_var_prices_<id> transient, programmatic variation creation via WC_Product_Variation + set_parent_id + set_attributes + save followed by WC_Product_Variable::sync, the inherited-vs-own variation stock model (parent flag, variation flag, derived stock_status), get_available_variations for frontend variation data, and the $for_display tax-handling parameter that breaks display logic when forgotten. Use when scaffolding plugin code that creates / queries / modifies variations or when debugging stale variation data after programmatic writes. Triggers on WC_Product_Variation, WC_Product_Variable, get_variation_prices, get_available_variations, product_variation post type, wc_var_prices, sync_variation, or programmatic variation creation 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。

Use when working with Crossplane — crossplane Kubernetes-native infrastructure management. Covers managed resources, compositions, provider configurations, composite resource definitions (XRDs), claims, and provider health. Use when managing Crossplane resources, debugging provisioning failures, inspecting compositions, or auditing cloud resource status.

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 adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

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。

Analyzes Windows Security, System, and Sysmon event logs in Splunk to detect authentication attacks, privilege escalation, persistence mechanisms, and lateral movement using SPL queries mapped to MITRE ATT&CK techniques. Use when SOC analysts need to investigate Windows-based threats, build detection queries, or perform forensic timeline analysis of Windows endpoints and domain controllers.

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。

ZMK Firmware (オープンソースキーボードファームウェア) リファレンス。 Getting Started (hardware, user-setup, customization, zmk-cli)、 Features (bluetooth, split keyboards, encoders, displays, lighting, pointing, studio, low-power)、 Keymaps (keycodes, modifiers, combos, conditional layers)、 Behaviors (タップ・ホールド、レイヤー、マクロ、tap-dance, sticky-key)、 Input Processors、Hardware Integration、Troubleshooting (building, flashing, connection, hardware issues)。

Apple グラフィックスフレームワークリファレンス。 Metal, Core Animation, Core Graphics, Core Image, SpriteKit, SceneKit。 MTLDevice, MTKView, MTLBuffer, MTLTexture, Metal 4 (MTL4CommandQueue, MTL4CommandBuffer, MTL4ArgumentTable), CALayer, CABasicAnimation, CADisplayLink, CAMetalDisplayLink, CAMetalDrawable, CGContext, CGPath, CGImage, CIFilter, CIContext, CIImage, CIRAWFilter, CIWarpKernel, CIBlendKernel, SKScene, SKSpriteNode, SKAction, SCNNode, SCNGeometry, SCNMaterial。

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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。

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

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。

Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

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。

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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。

Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

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。

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

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 identify the CCSPlayerController_Respawn virtual function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the Respawn vfunc slot by RTTI-walking the CCSPlayerController vtable and confirming the candidate's decompiled body performs a full controller-state reset (networked flags, per-round fields, comeback/damage-info bookkeeping) rather than any other lifecycle vfunc. Trigger: CCSPlayerController_Respawn

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 identify the CCSPlayer_ItemServices_DropActivePlayerWeapon virtual function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the "drop the currently held weapon" vfunc by RTTI-walking the CCSPlayer_ItemServices vtable and confirming the candidate reads the player's active weapon and forwards it, with a computed toss velocity, into the weapon's own drop vfunc. Trigger: CCSPlayer_ItemServices_DropActivePlayerWeapon

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 adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

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。

Analyzes Windows Security, System, and Sysmon event logs in Splunk to detect authentication attacks, privilege escalation, persistence mechanisms, and lateral movement using SPL queries mapped to MITRE ATT&CK techniques. Use when SOC analysts need to investigate Windows-based threats, build detection queries, or perform forensic timeline analysis of Windows endpoints and domain controllers.

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。

Split one oversized commit or branch into a stack of independently reviewable Graphite (gt) PRs - deciding what genuinely separates, what is atomic and must stay whole, and proving each lower PR builds and passes without the ones above it. Use when asked to "split this PR", "this commit is too big", "break this into a stack", "can we separate this out", or when a diff bundles several concerns across different layers.

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。

Command and Control framework deployment, configuration, and operational tradecraft for red team engagements. Covers Cobalt Strike (malleable C2 profiles, Beacon types HTTP/HTTPS/DNS/SMB, Beacon Object Files for in-memory execution, sleep and jitter tuning, named pipe pivoting), Sliver (implant generation across mTLS/WireGuard/DNS transport, operator multiplayer mode, armory extensions), Mythic (agent ecosystem with Apollo/Poseidon/Medusa, C2 profile configuration, translation containers), Havoc (Demon agent with sleep obfuscation via Ekko/Zilean, indirect syscalls, dotnet inline execution), Metasploit (msfvenom payload generation, multi/handler staging, Meterpreter post-exploitation modules), redirector architecture using Apache mod_rewrite and Nginx, domain fronting through CDN providers, DNS-based C2 for restrictive network egress, and TLS certificate management for infrastructure OPSEC. Tools: Cobalt Strike, Sliver, Mythic, Havoc, Metasploit Framework. Aligns to MITRE ATT&CK T1071 (Application Layer Protocol), T1573 (Encrypted Channel), T1090 (Proxy/Connection Proxy).

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。