「amo」の検索結果

Delivers changes incrementally. Use when implementing any feature or change that touches more than one file. Use when you're about to write a large amount of code at once, or when a task feels too big to land in one step.

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。

Splits scarce time, budget or traffic across competing products, channels, campaigns or variants by treating them as a multi-armed bandit - Thompson sampling over beta posteriors, with optimism for anything not yet tried enough to judge. Use when deciding what to work on next, how to divide a marketing budget, which product to prioritise, whether to kill something that is underperforming, or when picking a winner among test variants.

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。

Habilidade especializada para construir aplicações serverless prontas para produção na AWS. Abrange funções Lambda, API Gateway, DynamoDB, padrões orientados a eventos SQS/SNS, deployment com SAM/CDK e otimização de cold start.

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 the user wants to find or discover a Claude Code skill that is NOT already installed — e.g. "is there a skill for X", "find me a skill that…", "what skills exist for…", or "/find-skill <topic>". Searches the official plugin marketplace, GitHub, and curated awesome-lists for community skills matching a need, removes anything already installed, and recommends the top 5 with links. Do NOT use this to create or edit skills (that is skill authoring), or to pick among skills you already have.

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 anomalies in procurement data using Qdrant vector operations. Covers centroid-based outlier detection, near-duplicate finding via batch recommend, amount z-score analysis, and vendor variance scoring. Use when building anomaly detection, fraud detection, or data quality features.

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。

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

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 Stripe subscription billing cycles using a test clock. Use when you need to simulate time passing to trigger subscription renewals, recurring invoices, or to confirm that the next billing date generates the expected charge in Stripe test mode.

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。

Set up, verify, and clean up Stripe test data end to end. Use when creating test customers, products, or subscriptions and you want a repeatable setup-then-teardown workflow, or when you need to remove leftover test customers and products with delete_stripe_test_customers, archive_stripe_test_products, and delete_stripe_test_products.

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。

Reference for Stripe test clock constraints and pitfalls. Use when planning or debugging test-clock workflows, when an advance or customer-attach step fails, or to check rules before building a time-simulation scenario with create_stripe_test_clock, advance_stripe_test_clock, and create_stripe_test_customers.

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。

Stripe CLI の `stripe fixtures` コマンドで実行可能な fixture JSON を、対話的に整理しながら生成・検証する。test_clock を使った時間進行、サブスクのライフサイクル、失敗カードでの payment_intent 失敗、Connect 配下の Destination charge、bulk 件数のテスト顧客作成など、複数ステップの API リクエスト連鎖を扱う。「Stripe の fixture を書いて」「fixtures.json を作って」「このシナリオを fixture 化」「テストデータを大量に作りたい」「サブスクの月次失敗をテストしたい」「stripe trigger と fixture どっち使う」のような依頼で起動する。"fixture" という単語が無くても、複数ステップの Stripe テストデータ準備、 test clock を使った時間進行、 bulk なテスト顧客作成、 失敗パターンの再現といった意図が見えたら起動する。単発 API 呼び出しが目的なら起動せずに `stripe trigger` か API 1コールを案内する。

Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.

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。

Compare standardized metrics across multiple ego-benchmark-harness runs by recalculating them from tasks.jsonl and session JSONL, never from HTML reports. Covers overall aggregates (success rate/average score), Odysseys per-rubric pass rates, duration distributions (avg/p50/p90/max), turns/tools/cost, error and exception counts, per-task success matrices, helper distributions, and the share of DOM operations. Use whenever users ask to compare data or metrics across runs, calculate aggregate/Odysseys/duration/turn/error/helper metrics, inspect differences among runs, or compare multiple rwb/real-world-bench runs. Use pi-session-analyzer instead for deep forensic analysis of why an individual task was slow or failed.

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。

Knowledge base from the NASA Systems Modeling Handbook for Systems Engineering (NASA-HDBK-1009A Rev A, 2025). Use for model-based systems engineering (MBSE) wired into NASA's NPR 7123.1 SE processes: the three aspects of MBSE (language / methodology / framework), the NASA SE Engine and its two OOSEM-derived steps (Model Planning, Setting Up the Model), the Modeling Plan as a SEMP subset, the tool-agnostic NASA SE metamodel of elements and relationships across the four SysML pillars (structure / behavior / requirements / parametrics), worked SysML diagrams and tables for stakeholders, requirements, structure and V&V, generating SE work products (ConOps, MOE, MOP, TPM, V&V), the MBSE Grid framework, alternative modeling approaches (PBR, Scenario, System Specification, Verification), and the ConOps model-content template. Scope is bounded to four common technical processes (Stakeholder Expectation Definition, Technical Requirements Definition, Product Verification, Product Validation). Does NOT teach SysML itself, mandate a tool, define NPR 7123.1 processes, cover technical-management processes 10-17, or reproduce the OMG SysML specification.

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。

Pre-checks one expense claim or travel plan, line by line, against the expense or travel policy the user supplies, and returns a DRAFT precheck sheet: the policy clause matched to each line, the limit quoted as the policy states it, whether the amount sits within, at or outside that limit, missing receipts, pre-approvals or justifications, and neutral questions for the approver and the claimant, with UNKNOWN wherever the policy or the claim is silent. Use when the user asks to "check this expense claim against policy", "pre-check my travel request", "is this claim within policy", "review these expenses before I submit them", "what receipts or approvals are missing" or "prepare the approver's questions for this claim". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; do not use to compare a policy against a standard or regulation, use policy-gap-review instead. Drafts for human review; never approves, authorises or signs off.

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。

永続化ドキュメント（Steering Documents）を初期生成します。プロダクトのコードを自動解析し、ユーザーへのヒアリングを組み合わせて6種のドキュメントを生成します。併せて generate-working-docs のテンプレートをプロダクト用にカスタマイズします。「永続化ドキュメントを作成して」「steering docs を作って」などで呼び出されます。

開発作業ドキュメントを自動生成します。YYYYMMDD_要件名の形式でディレクトリを作成し、requirements.md、design.md、tasklist.md、testing.mdを生成します。「開発作業ドキュメント作成」「開発ドキュメント作って」「ドキュメント生成」などで呼び出されます。

Run an autonomous consensus round between agent peers on several machines: they negotiate a decision over a shared channel and execute it without a human relaying messages. Use when multiple machines must agree on a change. Triggers: "/03", "work it out among yourselves", "find consensus". Wakes the human only for irreversible actions or a deadlock.

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 inconsistencies in ID formatting, date/time display, monetary amounts, and enum/status labels compared to existing patterns in the codebase.

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。

Extract structured data from receipts and invoices for bookkeeping, expense tracking, and tax preparation. Supports email, photos, scanned images, PDFs, and accounting exports. Outputs vendor, date, amount, tax, line items as table, CSV, or JSON. Trigger on "process receipts", "extract receipts from email", "scan invoices", "capture expenses".

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。

Assign transactions to tax-aligned expense categories using vendor name, line items, and amount patterns. Handles ambiguous vendors like Amazon by inspecting line-item detail from the receipt source. Supports split categorization and learns from user corrections. Trigger on "categorize expenses", "sort transactions", "assign expense categories", "what category is this".

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。

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start opt...

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。

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly

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。

Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily skills.

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。