「exe」の検索結果

作成済みの実装計画を、同じセッションでタスクごとに実行します。テストの失敗と成功、進捗や判断を記録し、最後にブランチ全体をレビューするスキルです。

• 作成済みの開発計画を実装したいとき
• 実装用の別エージェントなしで進めたいとき
• 進捗記録から作業を再開したいとき

Trade execution modeling (backtest only) — slippage formulas (linear / square-root impact), VWAP/TWAP execution logic, market-impact cost estimation, and execution-assumption configuration.

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。

Parse Windows Prefetch (.pf) files with the windowsprefetch Python library to reconstruct application execution history, run counts, and accessed file/volume lists. Use when investigating renamed or masquerading binaries, verifying program execution timelines, or hunting for suspicious execution patterns in incident response.

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 designing, implementing, debugging, or reviewing Nexent relational schemas, FastAPI endpoints, backend services, database access, SQL migrations, or backend/SDK configuration. Includes table/field naming, types, JSONB boundaries, audit fields, keys, and indexes even before code paths exist. Skip frontend-only work and unrelated SDK algorithms.

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 and maintain Nexent's requirement-driven feature catalog, D1-D5 structured cases, change records, fixed automation, implementation manifest, generated Excel baseline, migration inventory, and Mock/Real execution declarations. Use for requirements, bug regressions, test implementation, V5 migration, or formal test-asset consistency work. Excludes the Legacy UT suite.

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。

[omh] Periodic report for executives: weekly/monthly reports, executive briefs, PPT-ready outlines, and upload packages. Use when the user says: report-package, report package, weekly report, monthly report, executive report, exec brief, leadership deck, status package.

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。

Builds resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like lambda durable functions, durable execution, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, reliable serverless applications, context.step, context.wait, context.invoke, context.runInChildContext, withDurableExecution, DurableContext, UnrecoverableInvocationError, durable-execution-sdk, qualified ARN invocation, and durable handler replay.

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。

Semantic topic cluster planning and automated execution engine for claude-blog. Performs SERP-based keyword research, groups keywords by search intent and SERP overlap, builds a hub-and-spoke cluster architecture, generates an interactive SVG cluster map, and executes the full cluster by orchestrating blog-write calls with shared cluster context and automatic internal-link injection. Fills the strategy-to-execution gap: blog-strategy plans the blueprint, blog-cluster builds the house. Use when user says "blog cluster", "topic cluster", "content cluster", "cluster plan", "cluster execute", "pillar content", "hub and spoke", "content ecosystem", "cluster map".

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。

Plan and execute entire application builds. Generates phased delivery roadmaps, then executes them autonomously — phase by phase, committing at milestones, deploying, testing, and continuing until done or stuck. Modes: plan (generate roadmap), start (begin executing), resume (continue from where you left off), status (show progress). Triggers: 'roadmap', 'start building', 'resume the build', 'keep going', 'build the whole thing', 'execute the roadmap', 'what phase are we on'.

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。

Execute arbitrary Python or PySpark code on Fabric Spark compute without creating a notebook artifact; ephemeral Livy sessions with full Delta table access. Automatically invoke when the user asks to "run PySpark in Fabric", "create a Livy session", "execute Python on Fabric compute", "run Spark without a notebook", "submit code to Fabric", "ephemeral Spark execution", "run ETL in Fabric".

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 knowledge for Azure AI Video Indexer development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring live camera analysis, custom AI insights, OpenAI summaries, Video Indexer APIs, or face redaction, and other Azure AI Video Indexer related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Search (use azure-cognitive-search).

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。

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。

Applying the ePrivacy Directive Article 5(3) strictly necessary exemption to classify cookies that do not require consent. Covers exemption criteria, functionality cookies, load balancing, session state, and non-exempt categories with regulatory guidance from EDPB and national DPAs.

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。

Designs and executes tabletop breach simulation exercises for testing organizational breach response capabilities. Covers scenario creation with realistic inject timelines, participant role assignment, communication testing across internal and external channels, decision-point evaluation, and after-action report generation. Keywords: tabletop exercise, breach simulation, incident response testing, scenario design, after-action report.

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。

Google Search Console indexing assistant. Use when the user wants to track page indexing status, compare sitemap against GSC indexed pages, manage submission tracking, or move false positives (pages indexed per URL Inspection but not yet in GSC export) to the indexed list. Maintains two tracking files (indexed.md, to-index.md) with prioritization support.

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。

Optimize slow PostgreSQL inserts in Python using psycopg2. Use when: (1) Row-by-row inserts are taking too long over network, (2) executemany() isn't providing speedup, (3) Migrating large datasets to PostgreSQL, (4) Network latency making individual INSERT statements impractical. The key is using execute_values() from psycopg2.extras instead of executemany() or individual execute() calls.

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。

Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.

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。

Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.

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。

Internal — for Boundless team members only. Query the Boundless Indexer REST API for on-chain market data, staking, PoVW rewards, delegations, and market efficiency on prod/staging environments. Use when the user asks about proof requests, provers, requestors, staking data, PoVW rewards, delegations, market aggregates, leaderboards, or wants to fetch data from the indexer API on live networks. Also use when the user mentions "indexer" and wants to look up addresses, requests, or market statistics on deployed environments. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.

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。

Semantic topic cluster planning and automated execution engine for claude-blog. Performs SERP-based keyword research, groups keywords by search intent and SERP overlap, builds a hub-and-spoke cluster architecture, generates an interactive SVG cluster map, and executes the full cluster by orchestrating blog-write calls with shared cluster context and automatic internal-link injection. Fills the strategy-to-execution gap: blog-strategy plans the blueprint, blog-cluster builds the house. Use when user says "blog cluster", "topic cluster", "content cluster", "cluster plan", "cluster execute", "pillar content", "hub and spoke", "content ecosystem", "cluster map".

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。

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-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。

Load, validate, and use DACP bundles as structured execution context. Detects .bundle/ companion directories, validates integrity (manifest schema, fidelity, provenance), loads typed data, and builds ExecutionContext for receiving agents. Scripts are NEVER auto-executed. Use when processing incoming DACP bundles, interpreting .bundle/ directories alongside .msg files, validating bundle integrity, or building execution context from bundle payloads.

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。