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概要と使いどころ
Create a new cpp_tests entry for validating a C++ interface vtable layout against binary reference YAMLs. Creates the .cpp test file in cpp_tests/ and appends a configs/<GAMEVER>.yaml entry under cpp_tests:. Use when a user asks to add vtable layout validation for a new hl2sdk_cs2 interface class.
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。
Locate BotProfile::Cost in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Cost" (symbol BotProfile_Cost). This is a VIRTUAL function - resolve the BotProfile::Cost vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Cost, BotProfile::Cost
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。
Locate BotProfile::Aggression in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Aggression" (symbol BotProfile_Aggression). This is a VIRTUAL function - resolve the BotProfile::Aggression vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Aggression, BotProfile::Aggression
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。
Locate BotProfile::LookAngleDampingAttacking in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::LookAngleDampingAttacking" (symbol BotProfile_LookAngleDampingAttacking). This is a VIRTUAL function - resolve the BotProfile::LookAngleDampingAttacking vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_LookAngleDampingAttacking, BotProfile::LookAngleDampingAttacking
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。
Locate BotProfile::LookAngleStiffnessAttacking in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::LookAngleStiffnessAttacking" (symbol BotProfile_LookAngleStiffnessAttacking). This is a VIRTUAL function - resolve the BotProfile::LookAngleStiffnessAttacking vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_LookAngleStiffnessAttacking, BotProfile::LookAngleStiffnessAttacking
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。
Locate BotProfile::Skill in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Skill" (symbol BotProfile_Skill). This is a VIRTUAL function - resolve the BotProfile::Skill vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Skill, BotProfile::Skill
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。
Locate BotProfile::Teamwork in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Teamwork" (symbol BotProfile_Teamwork). This is a VIRTUAL function - resolve the BotProfile::Teamwork vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Teamwork, BotProfile::Teamwork
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。
Locate CBaseEntity_EmitSoundFilter_SoundName in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CBaseEntity_EmitSoundFilter_SoundName" (symbol CBaseEntity_EmitSoundFilter_SoundName). The gamedata consumer stores a BYTE SIGNATURE for this entry - ALWAYS emit the func schema (func_sig), never vfunc fields. The function may be virtual; locating it via the owning class vtable (RTTI) is a fine strategy, but once found, read its head bytes and emit func_sig. Locate via cross-references, distinctive constants/strings, callers of related symbols, or vtable slots; verify by decompilation before committing. Trigger: CBaseEntity_EmitSoundFilter_SoundName, CBaseEntity_EmitSoundFilter_SoundName
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。
Review database schema design and detect anti-patterns. Based on common RDB design knowledge and best practices. Supports Prisma, Drizzle, Rails, Django, Laravel, TypeORM, Sequelize, SQLAlchemy, and raw SQL (DDL). Use when the user says "review DB design", "check schema", "review table design", "review migration", "check for anti-patterns", "DB設計をレビューして", "スキーマを見て", "テーブル設計を確認して", "マイグレーションをレビューして", "アンチパターンがないか調べて", "この設計で問題ないか見て".
Runs a session's fan-out — splits a large request into tiered, recoverable units, dispatches and reconciles them against the repo. Trigger when work needs many agents or spans many repos or files (rebuild, re-architect, sweep, migrate, 'do all of this'); when subagents would launch with no model or effort set; for the plan table (units, models, tokens, meters) before launch; to resume a unit that died or hit a usage limit; when units would share a repo; or say dispatchwright (plan, dispatch, resume, audit, refresh). Tiers come from its own table (scoutwright flags it stale); pacing, pacewright's; hooks, rigwright's; schedules, agentwright's.
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。
Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.
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。
Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts.
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。
Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.
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 when implementing, refactoring, or extending the internal table runtime inside this repository. Covers the current architecture layers, state model, composable flow, where different responsibilities live, how to add features cleanly, and what kinds of refactors are encouraged.
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。
Expert AI art prompt engineer for Midjourney and Stable Diffusion. Generates high-quality prompts including unified batch illustrations, marketing designs, interactive compositions, and structured framework prompts with translations.
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 logo directions — "give me logo concepts for", "logo ideas for", "design a logo for". Emits 3 to 5 distinct directions as real editable SVG vector code, not raster mockups.
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。
Pull quotable lines from a source — "pull quotes from this", "give me quotes from this interview", "find the quotable lines", "best lines from this transcript". Reads pasted text directly or extracts from long documents.
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。
Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.
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。
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language 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。
Build auditable Excel workbooks headless with openpyxl — blue/black/green cell conventions, formulas over hardcodes, named ranges, balance checks, sensitivity tables. Use for financial models, audit outputs, reconciliations.
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。
Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts.
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 writing or applying a migration in site-os, adding a table or column, storing files, or when a migration fails with "must be owner of table" — covers the apply procedure, the postgres-ownership rule, money/rate units, and where files belong.
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 React Spectrum UI components for Adobe Experience Cloud Shell SPAs and AEM UI Extensions. Provides patterns for pages, forms, data tables, dialogs, and navigation using @adobe/react-spectrum. Guides ExC Shell integration with @adobe/exc-app including runtime.done(), IMS token passthrough, and shell theming. Guides AEM UI Extension development with @adobe/uix-guest for Content Fragment Console, CF Editor, Universal Editor, and Assets View surfaces. Trigger on: building App Builder UI, React Spectrum components, ExC Shell pages, forms, data tables, dialogs, modals, navigation, theming, web-src, Spectrum design system, @adobe/exc-app, AEM extension, AEM UI extension, Content Fragment Console, Universal Editor extension, uix-guest, @adobe/uix-guest, extension points for AEM, customizing AEM 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。
Comprehensive Linux privilege escalation methodology for offensive security engagements. Covers the full attack surface from a low-privilege shell to root: SUID/SGID binary abuse via GTFOBins, Linux capabilities exploitation (cap_setuid, cap_dac_override, cap_dac_read_search), sudo misconfigurations including NOPASSWD rules and Baron Samedit (CVE-2021-3156), cron job abuse through writable scripts, PATH hijacking, and wildcard injection with tar/rsync/chown. Includes writable /etc/passwd attacks, NFS no_root_squash exploitation, kernel exploits (DirtyPipe CVE-2022-0847, DirtyCow CVE-2016-5195, PwnKit CVE-2021-4034), Docker group container escapes, LD_PRELOAD and LD_LIBRARY_PATH hijacking for shared library injection, systemd service misconfigurations, and sensitive file enumeration for credential harvesting. Integrates automated enumeration with LinPEAS, linux-exploit-suggester, pspy for process monitoring, and GTFOBins for binary exploitation. Each technique includes detection signatures and defender-sid...
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。