546 件 ・ 関連度順
概要と使いどころ
ADHD-friendly output formatting. Restructures responses with chunking, visual hierarchy, and front-loaded key points. Modes: clean, flow (default), zen. Use when user says "hyperfocus", "focus mode", "adhd mode", "adhd friendly", or invokes /hyperfocus.
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 in groups, rings, fields, and algebraic structures with applications to cryptography and number theory
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。
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.
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。
Structures a vague engineering request into a well-scoped task brief before any implementation begins. Use when the user has an engineering goal but the ask is missing which repos are in scope, what "done" means, constraints, or open questions. Triggers on "shape this", "scope this out", "frame this work", "write a brief for", "I want to build" (with unclear scope), or the /shape slash command. Produces a filled task template (multi-repo feature, single-repo change, investigation, or bugfix). Not for already-scoped work — go straight to execution. If the domain is unclear, ask one qualifying question before routing — for marketing intake see marketing-shaper, game-design intake see game-design-shaper.
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 data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.
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 exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.
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。
Intelligently organize files across your computer by understanding context, detecting duplicates, suggesting folder structures, and automating cleanup. Maintain a clean digital workspace without manual effort.
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。
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, or when you need to organize work across multiple parallel streams.
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。
Structures git branches and worktrees: keeps unrelated changes on separate branches, isolates each in its own worktree created so the repository's setup runs, records dependencies with git-machete, and recombines them into a temporary mixdown branch for local testing. Use before creating any branch or worktree in a codebase that works on more than one branch, including branching off the default branch to start a change; when starting work in a fork, clone, or third-party checkout; when choosing what a new branch should be based on; when a branch is growing a second unrelated concern; when deciding whether to stack branches or keep them independent; when preparing a change for a pull request or patch submission; when handed an existing worktree or branch to implement and commit in a fork; or when a local build needs several in-progress branches combined. Not needed where the repository clearly commits everything to the trunk, or holds notes or personal data rather than code.
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。
Drafts Brownfields Agreements for contaminated-property transactions under CERCLA, EPA brownfields programs, and state voluntary cleanup programs. Structures BFPP liability protections, All Appropriate Inquiries documentation, remediation obligations, institutional controls, cost allocation, and long-term stewardship covenants. Use when drafting brownfields purchase agreements, prospective purchaser agreements, voluntary cleanup MOAs, or environmental covenant frameworks.
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。
Drafts board-ready Anti-Money Laundering compliance programs for U.S. financial institutions under BSA/FinCEN requirements. Covers CIP, CDD, EDD, SAR/CTR reporting, OFAC screening, risk assessment, training, independent testing, and governance structures. Use when creating or updating AML policies, BSA compliance programs, or financial institution regulatory documentation. Trigger keywords: AML, BSA, FinCEN, Bank Secrecy Act, anti-money laundering, SAR, CTR, OFAC, CIP, CDD, KYC, compliance program.
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。
Drafts a court-ready ADA failure-to-accommodate complaint under Title I (employment, 42 U.S.C. § 12112) or Title III (public accommodations, 42 U.S.C. § 12182). Structures jurisdiction, EEOC exhaustion, factual allegations, causes of action, and relief. Use when drafting ADA disability discrimination complaints, reasonable accommodation pleadings, or civil rights complaints for plaintiffs with disabilities.
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。
Drafts adverse possession complaints and quiet title pleadings. Structures jurisdictional foundations, legal property descriptions, and element-by-element proof of actual, open, continuous, exclusive, and hostile possession. Use when filing adverse possession claims, quiet title actions, or prescriptive ownership pleadings.
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。
Redis best practices for caching, data structures, and in-memory data architecture
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。
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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。
Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.
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。
Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.
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。
Atlassian Template and Files Creator/Modifier expert for creating, modifying, and managing Jira and Confluence templates, blueprints, custom layouts, reusable components, and standardized content structures. Use when building org-wide templates, custom blueprints, page layouts, and automated content generation.
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。
Representations for hierarchical skill structures including knowledge graphs and ontological decomposition
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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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 text-based visual communication using ASCII art, Unicode box-drawing, and structured diagram notation. Creates clear, maintainable diagrams for systems, processes, hierarchies, relationships, and psychological structures. Proactively generates diagrams to enhance understanding. Activate on visualization needs, system architecture, process flows, psychological mapping, or when complex concepts would benefit from visual representation. NOT for photo editing, vector graphics, or GUI-based design 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。
Parses complex problems into DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies, identifies parallelization opportunities, and creates optimal execution plans. Activate on 'build dag', 'create workflow graph', 'decompose task', 'execution graph', 'task graph'. NOT for simple linear tasks or when an existing DAG structure is provided.
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。
Validates DAG structures, performs topological sorting, detects cycles, and resolves dependency conflicts. Uses Kahn's algorithm for optimal execution ordering. Activate on 'resolve dependencies', 'topological sort', 'cycle detection', 'dependency order', 'validate dag'. NOT for building DAGs (use dag-graph-builder) or scheduling execution (use dag-task-scheduler).
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。