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Found 59 Skills
Migrates Temporal, Inngest, Trigger.dev, and AWS Step Functions workflows to the Workflow SDK. Use when porting Activities, Workers, Signals, step.run(), step.waitForEvent(), Trigger.dev tasks / wait.forToken / triggerAndWait, ASL JSON state machines, Task/Choice/Wait/Parallel states, task tokens, or child workflows.
This skill should be used when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph", "track entities", or mentions memory architecture, temporal knowledge graphs, vector stores, entity memory, or cross-session persistence.
Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment. Use PROACTIVELY for workflow design, microservice orchestration, or long-running processes.
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.
Comprehensive skill for Graphiti and Zep - temporal knowledge graph framework for AI agents with dynamic context engineering
Expert in designing durable, scalable workflow systems using Temporal, Camunda, and Event-Driven Architectures.
Review and fix comments containing temporal references, development-activity language, or relative comparisons. Use when reviewing code comments, preparing documentation for release, or auditing inline comments for timelessness. Use for "check comments", "temporal language", "comment review", or "fix docs". Do NOT use for writing new documentation, API reference generation, or code style linting unrelated to comment content.
Workflow orchestration expert using Temporal.io for durable executionUse when "temporal workflow, durable execution, saga pattern, workflow orchestration, long running process, activity retry, workflow versioning, temporal, workflows, durable-execution, saga, orchestration, activities, long-running, ml-memory" mentioned.
Execute comprehensive market research workflows. Covers market intelligence gathering, sector analysis, security research, and competitive intelligence with temporal validation.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory, or memory benchmarks (LoCoMo, LongMemEval). A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of durable agent knowledge and cross-session persistence.
Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception, with an instance bank for temporal tracking. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Sparse4D model. Trigger phrases include "train Sparse4D", "multi-camera 3D detection", "temporal 3D tracker", "sparse query 3D perception".
Build Temporal workflow applications in Go. Use when creating or modifying Temporal workflows, activities, workers, clients, signals, queries, updates, retry policies, saga patterns, or writing Temporal tests.