「stt」の検索結果

Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK. Triggers: "speech to text REST", "short audio transcription", "speech recognition REST API", "STT REST", "recognize speech REST". DO NOT USE FOR: Long audio (>60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.

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。

End-to-end Stata replication pipeline — scaffolds numbered `.do` files in `scripts/stata/`, executes them via the `stata-mcp` MCP server, captures logs and outputs to `output/`, and produces publication-ready tables (esttab) and figures (graph export). Mirrors `/data-analysis` for R-first projects. Use when user says "stata replication", "set up Stata pipeline", "scaffold the .do files", "run Stata analysis", "AEA replication package in Stata", or when a project's analysis language is Stata not R.

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。

Transforms raw meeting transcripts into structured, evidence-based meeting minutes — merges multiple versions without losing content, reviews existing minutes against the transcript for missing items, and identifies anonymous speakers (Speaker 1/2/3, 发言人1). Triggers on 会议纪要, 会议记录, 整理纪要, 妙记转纪要, "write meeting minutes", "merge these minutes". Not for fixing ASR/STT errors first (use transcript-fixer).

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 ASR/STT transcription errors — homophones, garbled terms, person-name errors, mixed Chinese/English — with dictionary rules plus Claude's built-in AI, no external API key required. Use to clean meeting notes, lecture transcripts, interviews or speech-recognition text, even when the user only says "fix this transcript". Not for structuring a clean transcript into minutes (use meeting-minutes-taker).

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。

Writes, tests, registers and debugs Claude Code hooks (PreToolUse, PostToolUse, SessionStart, Stop) that turn a rule the model keeps breaking into a hard gate. Use when the user wants to block or intercept a tool call, add a guard rail, fix a misfiring hook, or says 拦截 / 守卫 / 钩子 — including "make it stop doing X".

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。

Deepgram speech AI: transcribe audio to text and synthesize speech (TTS). Trigger phrases: deepgram, transcribe audio, speech to text, STT, deepgram tts.

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 CarPlay-enabled apps using the CarPlay framework. Use when creating navigation, audio, communication, EV charging, parking, or food ordering apps for the car display, working with CPTemplateApplicationScene, CPInterfaceController template hierarchies, CPListTemplate, CPMapTemplate, CPNowPlayingTemplate, configuring CarPlay entitlements, or integrating with CarPlay Simulator for testing.

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 implement adaptive filters using DSP System Toolbox System objects. Use when working with adaptive filtering, system identification, noise cancellation, echo cancellation, active noise control (ANC), channel equalization, inverse system identification, or adaptive prediction. Covers dsp.LMSFilter, dsp.RLSFilter, dsp.FilteredXLMSFilter, dsp.FrequencyDomainAdaptiveFilter, dsp.AffineProjectionFilter, dsp.BlockLMSFilter, dsp.AdaptiveLatticeFilter, dsp.FastTransversalFilter, maxstep(), and algorithm selection for adaptive filtering problems. Replaces deprecated adaptfilt.* objects (removed R2020a).

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。

Scan recent Claude Code activity for errors and failure signals across all sessions using Agent Monitor data — APIError events and PreToolUse→PostToolUse gaps (tools that started but never completed) — then group failures by tool and model and rank them by frequency. Use when checking for errors or asking "what's failing right now".

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。

Render an ordered timeline of one Claude Code session's events (every event type) with per-event durations and tool names, reconstructed from Agent Monitor data. Pairs PreToolUse with PostToolUse to compute tool durations and surfaces gaps, errors, and compaction points. Use when reconstructing what happened in a session step by 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。

Define and check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining. Use when reporting reliability or when someone asks "are we meeting our SLOs?".

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 this period's reliability against the prior period using Agent Monitor data — error rate (APIError/total) and tool-failure rate (PreToolUse→PostToolUse gap) — flag any regression where reliability got worse, and optionally wire a persistent alert rule so the dashboard catches the next regression automatically. Use when checking whether reliability degraded.

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 hook delivery health from Agent Monitor data — balance PreToolUse vs PostToolUse (a gap means tools that started but never reported back), detect missing Stop/SubagentStop terminators (sessions/subagents that never closed), and check for stale ingestion (no recent events). Use when hooks look unreliable or events seem to be dropping.

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。

Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.

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。

Debug a specific session by inspecting its full event chain (PreToolUse, PostToolUse, Stop, SubagentStop, Compaction, APIError, TurnDuration, Notification events), agent hierarchy (recursive parent/child tree with subagent_type and depth), token usage with compaction baselines, workflow intelligence data (orchestration DAG, error propagation by depth), and session metadata (thinking_blocks, turn_count, total_turn_duration_ms).

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。

Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).

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 Speech in Foundry Tools development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using STT/TTS, custom voice or avatars, Speech containers, Voice Live, or telephony/WebRTC streaming, and other Azure Speech in Foundry Tools related development tasks. Not for Azure Content Understanding in Foundry Tools (use azure-content-understanding), Azure AI Vision (use azure-ai-vision), Azure AI Video Indexer (use azure-video-indexer), Azure Translator (use azure-translator).

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。

Pointer to the upstream skill for building voice agents with speech-to-speech and STT-LLM-TTS pipelines, installed from upstream.

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。

R package development with devtools, testthat, and roxygen2. Use when the user is working on an R package, running tests, writing documentation, or building package infrastructure.

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 before answering, planning, or editing any MSTest 3.x-to-4.x upgrade or post-upgrade failure. Triggers include "MSTest v4 breaking changes"; CS0507/CS0103/CS1061/CS1615; ExecuteAsync, CallerInfo, DisplayName, or custom TestMethodAttribute; ClassCleanupBehavior; ContainsKey; ThrowsExactly or ExpectedException; IsInstanceOfType out parameters; TestTimeout.Infinite; ManagedType; net6/net7 compatibility; TestCase.Id history; TestName in ClassInitialize; TreatDiscoveryWarningsAsErrors; discovery errors after a clean build; and MSTest.Sdk/MTP or vstest.console discovery changes. Do not use for v1/v2-to-v3 leftovers, framework conversion, runner-only migration, or a general .NET upgrade.

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 you need to write or improve integration tests for Quarkus — including @QuarkusTest, Dev Services for automatic container provisioning, Testcontainers via QuarkusTestResourceLifecycleManager, WireMock for external HTTP stubs, @QuarkusIntegrationTest for black-box testing against packaged artifacts, REST Assured, data isolation strategies (@TestTransaction vs @BeforeEach cleanup), and Maven Surefire/Failsafe three-tier split (*Test, *IT, *AT). This should trigger for requests such as Add or improve integration tests in a Quarkus project; Configure Testcontainers or Dev Services for Quarkus tests; Add WireMock stubs for external HTTP dependencies in Quarkus integration tests; Set up @QuarkusIntegrationTest for packaged artifact or native binary testing; Fix test data isolation or configure Maven Surefire/Failsafe split. Part of Plinth Toolkit

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 you need to write or improve integration tests — including Testcontainers with @ServiceConnection, @DataJdbcTest persistence slices, TestRestTemplate or MockMvcTester for HTTP, data isolation, and container lifecycle management for Spring Boot 4.0.x. This should trigger for requests such as Review Java code for Spring Boot integration tests; Apply best practices for Spring Boot integration tests in Java code; Write @SpringBootTest integration tests with Testcontainers; Configure dynamic properties for Spring integration tests; Review Spring Boot integration test profiles. Part of Plinth Toolkit

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 you need to implement acceptance tests from maintainer-authored or maintainer-sanitized Gherkin scenario facts for Spring Boot applications — including selecting scenarios tagged @acceptance, implementing happy path tests with TestRestTemplate, @SpringBootTest, Testcontainers with @ServiceConnection for DB/Kafka, and WireMock for external REST stubs. Never ingest raw .feature files. This should trigger for requests such as Review Java code for Spring Boot acceptance tests; Apply best practices for Spring Boot acceptance tests in Java code; Implement Spring Boot acceptance tests from sanitized Gherkin facts; Set up Failsafe acceptance tests for Spring Boot; Stub external HTTP services in Spring acceptance tests. Part of Plinth Toolkit

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 you need data access with Quarkus Hibernate ORM Panache — including PanacheEntity / PanacheEntityBase, PanacheRepository, named queries, JPQL, native SQL, DTO projections (project(Class)), pagination (Page.of()), N+1 avoidance (JOIN FETCH), optimistic locking (@Version / OptimisticLockException), @NamedQuery for validated reusable queries, transactions, @TestTransaction for test isolation, and immutable-friendly patterns. This is the Quarkus analogue to Spring Data for relational persistence. This should trigger for requests such as Review Panache entities or repositories in Quarkus; Improve Hibernate ORM data access with Panache; Add DTO projections, JOIN FETCH, pagination, or optimistic locking to Panache queries; Fix N+1 query problems or add @Version concurrency control in Quarkus Panache; Improve Panache active record versus repository design. Part of Plinth Toolkit

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。