「entities」の検索結果

ERC-8004 Trustless Agents - Register and manage AI agent identities on TRON and BSC blockchains with on-chain reputation tracking

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。

Personal knowledge base for capturing and retrieving information about people, places, restaurants, games, tech, events, media, ideas, and organizations. Use when: user mentions a person, place, restaurant, landmark, game, device, event, book/show, idea, or company. Trigger phrases: "remember", "note that", "met this person", "visited", "played", "what do I know about", etc. Brain entries take precedence over daily logs for named entities.

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 and apply Azure role-based access control with least privilege at the right scope. Use this skill when users ask about permissions, role assignments, access errors, or governance. Covers built-in roles, custom roles, managed identities, PIM, and access reviews per Well-Architected principles.

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。

Corrects output that refers to entities by bare identifier, restating it with a human-readable label beside every ID, and keeps that rule for the rest of the conversation. Use when the user invokes `/ids` or `$ids`, or complains that bead IDs, PR numbers, commit SHAs, or ticket keys were given without titles.

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 FRAP 26.1-compliant Certificate of Interested Persons identifying all parties, entities, and affiliates with financial or legal interests in a federal appeal. Adapts to circuit-specific local rules. Use when filing appeals, corporate disclosure statements, or conflict-of-interest certificates in U.S. Courts of Appeals.

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 a traceable local Fusion engineering review draft with source identities, artifacts and unresolved assurance gates. Not for sending, publication or release approval.

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, execute and verify authorized Fusion CAD changes using resolved entities, explicit units and state-bound plans. Use for reviewed parameter, sketch, feature, assembly or configuration 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。

Verify data validity through Openapi Trust - email deliverability, mobile number HLR checks, IP reputation, URL safety scans, document-based identity verification (IDV). Use for anti-fraud validation of user-provided contact data and identities.

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 Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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。

Manage BAP (Bitcoin Attestation Protocol) identity files using bap-cli. This skill should be used when users need to create, decrypt, list, or extract BAP identity backups, work with .bep encrypted files, or generate test fixtures for Playwright tests involving BAP identities.

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。

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

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。

Map Delphi classes to a relational database using TMS Aurelius ORM attributes. Use when the user asks to create entity classes, add Aurelius mapping to existing classes, fix or review mapping attributes, explain how a class is mapped, or work with associations, inheritance, automapping, nullable fields, blobs, or composite identifiers. Triggers on requests like "create Aurelius entities for...", "map this class to Aurelius", "add ORM mapping", "fix the mapping on this class", "how do I map a one-to-many in Aurelius".

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。

Guides AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record linkage, deduplication, Senzing SDK integration, data mapping for Senzing ingestion, or troubleshooting Senzing error codes. Also use when the user mentions matching records across data sources, finding duplicate entities, identity resolution, master data management, or needs to resolve who is who across datasets.

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 relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.

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。

[Documentation] Use when scanning the codebase to (re)generate ONE project-reference doc. Parameterized by `--target=<key>`: project-structure | backend-patterns | frontend-patterns | scss-styling | design-system | code-review-rules | domain-entities | feature-spec | docs-index | e2e-tests | integration-tests | seed-test-data | ui-system. Shared 4-phase scan engine; per-target detail lives in references/targets.md. `ui-system` is an orchestrator meta-target (`kind: orchestrator`) that runs the 3 UI child scans in parallel instead of the 4-phase engine.

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。

[Architecture] Use when you need to analyze business domain: bounded contexts, aggregates, entities, ERD, domain events, and cross-context integration.

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。

Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.

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 a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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 public repository changes for secrets, private paths, signing identities, bundle IDs, generated artifacts, local logs, and private workflow leaks.

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。

Load for EF Core entities/configurations, semantic value persistence, portable constraints, DbContext/query filters, repositories/specifications, migrations, model snapshots, transactions, or seeded lookup tables; use query-optimization specifically for N+1 or slow SQL diagnosis.

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。

Load when EF Core reads are slow, emit N+1 or excessive SQL, over-fetch, track too many entities, cause high DB load, or need projection/split-query/compiled-query analysis; not for general migrations, DbContext setup, or repository conventions.

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。

- Mine GitHub, GitLab and git history for identities, infrastructure and leaked credentials using commit author emails, GitHub code search, the commit… Use when investigating a developer or organisation on GitHub, finding leaked API keys, AWS keys or tokens in code, enumerating org members and their personal repos, or recovering secrets deleted from HEAD but still present in history or forks

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。

Introduce two or more people from the owner's network: resolve and confirm both identities, DM each side for consent before any group exists, then create the group and introduce both sides inside it. Requires the owner's approval first. Triggers: "/intro", "introduce X and Y", "connect X with Y".

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。

Manage encrypted vault storage for sensitive information — passwords, secure notes, payment cards, digital identities, and folder organization. Use when storing, retrieving, or organizing credentials, managing secrets, or working with encrypted data.

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。