Results for “dag”

Algorithms for decomposing DAGs into chains for parallel execution and resource optimization

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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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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 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 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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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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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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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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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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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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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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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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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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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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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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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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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.

• Coordinate tasks that depend on earlier results
• Run document audits, revisions, and checks in order
• Retry failed tasks while keeping completed results

Plan work across several AI agents by defining task owners, deliverables, dependencies, and acceptance criteria. Choose a finite workflow or repeated improvement cycles, then review results before allowing dependent tasks to proceed.

• Divide a project among several AI agents
• Choose between a planned workflow and repeated improvement
• Define acceptance criteria for each deliverable

Manage a project’s execution plan and lifecycle across multiple agents. Track task dependencies or repeated work cycles, identify tasks ready to start, handle pauses and completion, and prepare project status reports and final results.

• Managing plans with task dependencies
• Tracking repeated work cycles
• Pausing and resuming projects

Manage an AI team's project plans and recorded progress, including task dependencies and repeated improvement cycles. Track accepted results, identify work ready to start, pause or resume projects, and prepare status reports for requesters.

• Planning tasks with dependencies
• Tracking repeated improvement cycles
• Updating progress after accepting results

Plan how multiple agents should work together. Choose between dependency-based tasks and repeated work cycles, define ownership and acceptance criteria, identify safe parallel work, and decide how to handle results, interruptions, or revised goals.

• Dividing work among multiple agents
• Choosing dependency plans or repeated cycles
• Defining result acceptance criteria

Analyze whether an intervention causes an outcome using DoWhy in Python. Build a graph of causal assumptions, determine whether the effect can be estimated, compare estimates, and test how well results withstand changes to assumptions or data.

• Estimating an advertising campaign’s causal effect on sales
• Analyzing observational data with confounding factors
• Testing how robust a causal estimate is