631건 · 관련도순
Use when working with Dagster — dagster data orchestration platform management. Covers asset management, pipeline runs, sensor and schedule status, IO manager configuration, partition management, and resource health. Use when checking asset materialization status, investigating run failures, managing schedules/sensors, or analyzing Dagster deployments.
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Use when working with Dagster Deep — dagster deep data orchestration management covering job and asset inventory, run monitoring, schedule and sensor health, partition status, resource configuration, and daemon health checks. Use when investigating run failures, analyzing asset materialization, monitoring schedule ticks, or auditing Dagster configurations.
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Use when working with Dagger — dagger CI/CD pipeline management. Covers pipeline-as-code configuration, module management, function execution, caching layers, container builds, and pipeline debugging. Use when managing Dagger pipelines, debugging function execution, inspecting modules, or optimizing container-based CI workflows.
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Manage DAG execution lifecycles including start, stop, pause, resume, and cleanup. Activate on 'execution lifecycle', 'stop execution', 'abort DAG', 'graceful shutdown', 'kill process'. NOT for cost estimation, DAG building, or skill selection.
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Runtime enforcement of file system boundaries and tool access restrictions. Blocks unauthorized operations and logs violations. Activate on 'enforce scope', 'access control', 'boundary enforcement', 'tool restrictions', 'runtime security'. NOT for validation (use dag-permission-validator) or isolation management (use dag-isolation-manager).
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Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).
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Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).
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Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).
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Design modern, intuitive DAG/workflow visual editors that feel like LEGO, not LabView NOT for runtime DAG admission, graph execution, or claiming usability without user testing.
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Synthesizes evidence-linked feedback from validation results, confidence evidence, and iteration triggers. Proposes bounded revision guidance for a stated acceptance failure. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).
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Performs claim-evidence verification for agent outputs, recording supported, contradicted, or insufficient-evidence results alongside source accessibility. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).
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Manages agent isolation levels and resource boundaries. Specifies concrete controls and the evidence needed to assess their boundaries. Activate on 'isolation level', 'agent isolation', 'resource boundaries', 'sandboxing', 'agent containment'. NOT for permission validation (use dag-permission-validator) or runtime enforcement (use dag-scope-enforcer).
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Identifies when task outputs require iteration based on quality signals, unmet requirements, or explicit feedback. Triggers appropriate re-execution strategies. Activate on 'needs iteration', 'retry needed', 'not good enough', 'try again', 'refine output'. NOT for feedback generation (use dag-feedback-synthesizer) or convergence tracking (use dag-convergence-monitor).
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Validates agent outputs against expected schemas and quality criteria. Reports structural and content checks within the declared evidence scope. Activate on 'validate output', 'output validation', 'schema validation', 'check output', 'output quality'. NOT for confidence scoring (use dag-confidence-scorer) or hallucination detection (use dag-hallucination-detector).
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Validates versioned permission requests against a grant scope and records comparison evidence. Activate on 'validate permissions', 'permission check', 'inheritance validation', 'permission matrix', 'security validation'. NOT for runtime enforcement (use dag-scope-enforcer) or isolation management (use dag-isolation-manager).
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Apache Airflow DAGs, operators, SLA monitoring, and workflow orchestration. Activate on: Airflow, DAG, operator, sensor, scheduler, task dependency, SLA, backfill, XCom. NOT for: dbt transformations (use dbt-analytics-engineer), streaming pipelines (use streaming-pipeline-architect).
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Coordinate child agents on tasks that must happen in a particular order. Define dependencies, pass results through handoff files, verify the outcome, and recover individual failed tasks while reusing work that has already finished.
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Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).
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Holistic pedagogical review of a lecture deck (`.qmd` or `.tex`). Checks narrative arc, prerequisite assumptions, worked examples, notation clarity, and deck-level pacing. Use when user says "pedagogy review", "does this teach well?", "is the flow right?", "will students follow?", "review the narrative", or before teaching a deck for the first time. Read-only; produces a report.
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Develop TPACK for integrating a specific technology or AI tool into subject teaching with pedagogical alignment. Use when adopting new ed-tech, reviewing AI tools, or planning technology integration.
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Develop pedagogical content knowledge for a topic by surfacing common misconceptions and effective representations. Use when preparing to teach unfamiliar content or improving teaching of hard topics.
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Use when working with Dagster OSS: assets, jobs, Definitions, config/resources, schedules/sensors, automation, CLI/local development, deployment operations, GraphQL/webserver, Pipes external processes, components/projects, or editing the Dagster repository itself.
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Apply airflow dag patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses airflow dag patterns or its strengths fit the task.
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Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Learn to define assets, ops, jobs, schedules, sensors, and resources for building maintainable data platforms.
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