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Helps DevOps engineers configure mirrord Operator's Temporal task queue splitting feature end-to-end. Generates the MirrordSplitConfig and MirrordPropertyList Kubernetes CRD YAMLs, the matching mirrord.json split_queues section with message_filter and jq_filter, and Helm value guidance. Use this skill whenever the user mentions Temporal splitting with mirrord, Temporal task queue splitting, splitting a Temporal worker, configuring mirrord with Temporal or Temporal Cloud, routing Temporal workflow or activity tasks to a local worker, or troubleshooting Temporal splitting sessions. Also trigger on split_queues with queue_type Temporal, or connecting mirrord to a Temporal frontend. This is an alpha Team/Enterprise feature of mirrord.

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Test Temporal workflows in Python with pytest. Use time skipping to avoid long waits, replace activities with controlled test versions, and replay execution histories to check that workflow changes remain consistent with previous runs.

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• Testing long-running workflows without waiting
• Testing workflow logic with mocked activities
• Checking workflow changes against execution histories

Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction.

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Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction.

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Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.

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• Investigating slow requests across services
• Understanding service dependencies
• Tracing how errors spread

Design long-running processes across services with Temporal, a system that preserves workflow progress. Covers separating coordination from external operations, handling retries and failures, and structuring workflows so they can resume after interruptions.

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• Designing order or booking processes across services
• Building approval flows with timeouts and escalation
• Planning recovery for distributed transactions

Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.

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Grafana Tempo provider for obz. Covers trace search, trace retrieval by ID, and extension commands for tag discovery. This skill should be used when the user mentions "Tempo", "Grafana Tempo", "TraceQL", "obz trace -p tempo", or needs to search traces from a Tempo backend.

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End-to-end bulk time-course analysis from an expression matrix to temporal gene modules and per-cluster pathway enrichment. Orchestrates temporal DE (limma splines or DESeq2 LRT), Mfuzz/tslearn soft clustering of expression-profile shapes, GAM trajectory fitting, per-cluster GO enrichment against a temporal-gene background, and an OPTIONAL circadian rhythm-detection branch (MetaCycle/CosinorPy) that runs only when the design covers >=2 full cycles with >=6-8 evenly spaced samples per cycle. Use when analyzing a bulk time-series expression experiment from any omics platform and deciding limma-splines vs DESeq2-LRT for temporal DE, soft vs hard clustering, whether the sampling design even licenses rhythm detection, and which background to use for enrichment. Not for single-cell pseudotime (see temporal-genomics/trajectory-modeling for the bulk-vs-pseudotime boundary) or unknown-period discovery (see temporal-genomics/periodicity-detection).

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Publish a Cloudflare Worker to a temporary live URL without creating an account first. The skill covers deployment, checking the response, updating the code, and handing over an ownership link before the 60-minute expiry.

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• Share a live URL for a Worker prototype
• Deploy during a session without browser sign-in
• Check live responses after each code change

Manage temporary access in Google Cloud with Privileged Access Manager. Configure who can request which roles and for how long, maintain access configurations, and handle access requests and approval decisions through guided workflows.

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• Request temporary access to Google Cloud resources
• Configure eligible requesters and approvers
• Review and approve or deny pending access requests

Agent fallback for CCSWeaponBase_ItemPostFrame_ProcessReloadAction (auto-generated, category: func). Locate CCSWeaponBase::ItemPostFrame::ProcessReloadAction in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CCSWeaponBase_ItemPostFrame_ProcessReloadAction, CCSWeaponBase::ItemPostFrame::ProcessReloadAction

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Access paid APIs using Machine Payments Protocol (MPP), a protocol for request payments. The skill checks payment requirements, selects a compatible wallet or client, submits payment, and helps inspect the API response and receipt.

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• Paying for an API request that returns HTTP 402
• Choosing a client for a payment method
• Finding paid services and setting spending controls

Inspect an agent’s current model, processing limits, workspace, tools, and message routing details. Diagnose unavailable capabilities, adjust permitted runtime settings, or keep temporary session notes, with advance notice before changes that affect the model or limits.

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• Diagnose why a capability is unavailable
• Check capacity before a large task
• Adjust allowed model presets or iteration limits

Temporarily disable a community skill installed through the hub while keeping its files and configuration. It also stops included automatic hooks, records the change, and lets you restore the skill by running the same command again.

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• Temporarily pause a community skill
• Stop automatic hooks while preserving configuration
• Re-enable a previously disabled skill

Turn “last month” into the full previous calendar month's date range. Get local calendar dates and precise UTC boundaries, the global time reference, to set consistent periods for monthly reports, summaries, or timestamp searches.

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• Set the reporting period for the previous calendar month
• Confirm the dates for a month-end summary
• Define UTC boundaries for last month's timestamp searches

Turn “last seven days” into a concrete date range ending today. Get local calendar dates and precise UTC boundaries, the global time reference, to use as consistent limits for reports or timestamp searches.

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• Set the reporting period for the last seven days
• Define UTC boundaries for timestamp searches
• Check the local dates covered by a trailing week

Pay-per-call access to the Nansen API via MPP (Tempo). Use when a user wants anonymous Nansen access without an API key and without managing their own Base/Solana wallet — they install the tempo CLI separately and call the API through `tempo request`.

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Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

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Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

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Automatically repair code violations of temporal properties using model-checking counterexamples as guidance. Use when working with formal verification results, temporal logic property violations (LTL, CTL), model checking counterexamples, or when asked to repair property violations, fix counterexamples, repair temporal properties, fix model checking violations, or repair code based on counterexamples. Applicable to concurrent systems, state machines, synchronization issues, safety/liveness properties, and resource management violations.

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Unified expert guide for async job queues & durable workflows: BullMQ v5 (Redis queues), Trigger.dev v3 (serverless tasks), Inngest, and Temporal.io (distributed sagas) / Panduan ahli terpadu untuk antrean job asinkron & workflow tahan-gagal: BullMQ v5, Trigger.dev v3, Inngest, dan Temporal.io.

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Use when designing a background-job system, choosing between BullMQ / Sidekiq / RQ / Temporal / SQS, deciding queue-vs-workflow, sizing concurrency vs rate limits, building dead-letter queues, or making handlers idempotent. Triggers: jobs running twice on retry, lost jobs after worker crash, DLQ filling up, Redis OOM from job backlog, exactly-once requested, "do we need Temporal?", visibility timeout / lockDuration confusion, exponential backoff vs jitter, fan-out fan-in workflows. NOT for outbound webhook publishing (different concerns), receiver-side webhook handling (different concerns), event-streaming/Kafka topology, or in-process async (event loop only).

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Especialista em construir aplicações de voz em tempo real - de agentes de voz em tempo real a apps habilitados para voz. Abrange OpenAI Realtime API, Vapi para agentes de voz, Deepgram para transcrição, ElevenLabs para síntese, LiveKit para infraestrutura em tempo real e fundamentos de WebRTC. Sabe como construir experiências de voz com baixa latência e prontas para produção. Use quando: voice ai, voice agent, speech to text, text to speech, realtime voice.

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