“tsk”的搜索结果

Use GEO Optimizer from the CLI or MCP so agents can audit a site for AI search visibility, generate llms.txt/schema fixes, check bot access, and track citation readiness.

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audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR tasks.

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Integrate Audiobookshelf's self-hosted audiobook and podcast server into AI agent workflows. Agents can manage libraries, track listening progress, search metadata, and automate podcast episode downloads through the comprehensive REST API.

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Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta's Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation.

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Identifies audio content using Chromaprint/AcoustID fingerprinting, Shazam API recognition, and ACRCloud monitoring. Matches music, speech, and ambient audio against fingerprint databases.

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The Atlassian Rovo MCP Server bridges your Atlassian Cloud workspace with any MCP-compatible client. Search and summarize Jira issues, create tickets from natural language, update Confluence pages, and query Compass services.

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mcp-atlassian is a Model Context Protocol server that connects AI assistants to Atlassian Jira and Confluence. It enables searching and managing Jira issues, reading and editing Confluence pages, and performing project management tasks through natural language via any MCP-compatible client.

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Generates event-driven architecture documentation from AsyncAPI 3.0 specifications. Uses the AsyncAPI parser-js library to extract channels, message schemas, and server bindings for Kafka and RabbitMQ.

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Use ast-grep (sg) to search, lint, and rewrite code across large codebases using AST pattern matching. A blazing-fast alternative to regex-based code transformations that understands syntax structure.

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Transcribes audio and generates auto-chapters with summaries using AssemblyAI's /v2/transcript endpoint with auto_chapters=true. Extracts key topics, sentiment analysis, and content safety labels via AssemblyAI SDK.

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Streams audio from Twilio Media Streams over WebSocket to AssemblyAI real-time transcription, extracting speaker-diarized transcripts with word-level timestamps. Triggers entity detection and sentiment analysis via AssemblyAI LeMUR on completed call segments. Results are pushed to HubSpot or Salesforce contact records via their REST APIs.

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Summarizes audio content using AssemblyAI's LeMUR (Large Language Model for Audio Understanding) API. Chains the /v2/transcript endpoint with /lemur/v3/generate/summary for contextual audio intelligence.

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Extract structured intelligence from audio using the AssemblyAI API with sentiment analysis, entity detection, topic modeling, and auto-chapter generation. Uses the assemblyai Python SDK for transcript processing pipelines.

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Use DeepAgents to build long-running task agents with a batteries-included harness for planning, tools, and workflow structure.

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Use apko to build small OCI images from declarative package manifests when supply-chain clarity and minimal contents matter more than a conventional Dockerfile flow.

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Manages ArgoCD Application sync waves and hooks through the ArgoCD REST API and Kubernetes custom resources. Uses kubectl diff and Helm template rendering to validate manifests before triggering progressive rollouts via Argo Rollouts.

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Manages ArgoCD Application and ApplicationSet resources for Kubernetes GitOps deployments. Uses the ArgoCD REST API and argocd CLI to automate sync waves, health checks, and progressive rollout configurations.

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Manages GitOps deployments via the ArgoCD REST API and argocd CLI. Triggers application syncs through /api/v1/applications/{name}/sync, monitors health status via /api/v1/applications/{name}, and manages rollbacks using /api/v1/applications/{name}/rollback for Kubernetes workloads.

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Monitors ArgoCD application deployments using the ArgoCD REST API and gRPC interface. Tracks sync status, health checks, and rollback history across Kubernetes namespaces.

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Monitors ArgoCD application sync status via the ArgoCD REST API and argocd CLI. Detects OutOfSync conditions, tracks sync wave progress, and alerts on failed sync operations with detailed resource diff analysis using argocd app diff.

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Manages ArgoCD application synchronization using the ArgoCD REST API and argocd CLI. Handles sync waves, hooks, and health assessments for GitOps-driven Kubernetes deployments.

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Diagnoses ArgoCD application sync failures and degraded states using the ArgoCD REST API and argocd CLI. Queries /api/v1/applications/{name} for sync status, resource health, and operation state. Provides automated remediation steps for OutOfSync, Degraded, and Missing resource conditions.

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Maps and transforms data between systems using Apache Camel route definitions and the Camel Component API. Supports XSLT, JSONPath, and DataFormat transformations via camel-core SDK.

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Validates Ansible playbooks using ansible-lint with custom rule plugins and the Ansible Collections API. Checks for deprecated modules, missing handlers, insecure variable practices, and role dependency conflicts.

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