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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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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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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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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

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

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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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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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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Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

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• Classify time series and predict continuous outcomes
• Group time series with similar patterns
• Forecast future values

Turn “this week” into a concrete date range from Monday through today. Get local calendar dates and precise UTC boundaries, the global time reference, for week-to-date activity summaries, progress reports, or timestamp searches.

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• Set the period for this week's activity summary
• Confirm the dates covered by a weekly progress update
• Define UTC search boundaries from Monday through today

Turn “last week” into the previous complete Monday-to-Sunday date range. Get local calendar dates and precise UTC boundaries to use for weekly digests, week-over-week comparisons, and queries that filter records by timestamp.

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• Setting the reporting period for a weekly digest
• Checking the date range for week-over-week comparisons
• Getting UTC boundaries to search last week's records

Get the date range from the start of this quarter through today. The skill returns local dates and precise UTC boundaries, based on when it runs, for quarter-to-date metrics and timestamp-based data queries.

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• Setting the period for quarter-to-date metrics
• Checking date filters for this quarter's progress
• Getting UTC boundaries for timestamp queries

Get the date range for the previous full quarter, based on when the skill runs. It returns local dates and precise UTC boundaries to define reporting periods for quarterly reviews, quarter-over-quarter comparisons, and timestamp-based queries.

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• Setting the period for a quarterly review
• Checking the previous quarter's comparison period
• Getting UTC boundaries for timestamp queries

Turn “this year so far” into a concrete date range from January 1 through today. Get local calendar dates and precise UTC boundaries to use when filtering records or calculating year-to-date metrics.

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• Setting the date range for year-to-date metrics
• Filtering records from January 1 through today
• Checking UTC boundaries for timestamp queries

Get the date range from the first of this month through today, based on when the skill runs. It returns local dates and precise UTC boundaries for month-to-date reporting and timestamp-based data queries.

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• Setting the period for month-to-date metrics
• Checking date filters for this month's data
• Getting UTC boundaries for timestamp queries

Get the date range for the previous full calendar year, from January 1 through December 31. It returns local dates and precise UTC boundaries for annual reports, year-over-year comparisons, and timestamp-based data queries.

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• Setting the period for an annual report
• Checking the previous year's comparison period
• Getting UTC boundaries for timestamp queries