Other skills

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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Validate finite-DAG order objectives and construct reachability-chain or direct-edge path partitions with explicit proof boundaries. NOT for equating reachability chains with direct-edge paths without checking the chosen objective.

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Review of Cognitive Systems Engineering applications and current practice in safety-critical domains

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Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).

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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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Build and validate a static DAG reachability index from source-qualified path/chain decompositions and partial transitive-edge filtering. NOT for treating a partial edge filter as a complete transitive reduction or runtime scheduler.

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ALIAS for dag-orchestrator. The original DAG execution skill, now unified with orchestrator into dag-orchestrator. Use dag-orchestrator for the full HTDAG experience.

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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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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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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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Validates typed DAG dependency declarations, returns Kahn linear extensions or generations and residual cycle witnesses, and identifies changed consumers for revalidation. NOT for constructing graphs, scheduling resource capacity, granting effects, or executing nodes.

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Investigates DAG incidents with a precise top event, timeline, competing hypotheses, barriers, propagation, and bounded recovery. NOT for automated root-cause classification, trace collection, or performance profiling.

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Ranks eligible skill candidates with explicit retrieval evidence, capability coverage, and uncertainty. NOT for discovery, authorization, or execution planning.

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Records and evaluates event-specific probability forecasts for agent outputs. NOT for validation or automatic acceptance.

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Builds scoped, provenance-preserving context packets between DAG nodes. NOT for execution or aggregation.

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Propose and safely admit immutable DAG revisions after failures, requirements, resource changes, or unresolved effects. NOT for mutating an executing graph in place or retrying an effect with unknown commit state.

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Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

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AI-powered career biographer that conducts empathetic interviews, extracts structured career narratives, and transforms professional stories into portfolios, CVs, and personal brand assets. This skill should be used when users want to document their career journey, create professional portfolios, generate CVs, or craft compelling career narratives.

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Expert causal reasoning and mental model construction for diagnosis and prediction in complex domains

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Construct a finite oriented simplicial or cellular cochain model and compute its stated linear invariants when coefficient spaces, maps, metrics, and observations are explicit. Use for scoped Hodge, rank, or residual calculations; not for attributing agent intent or authorizing effects. NOT for attributing intent, identifying a liar, or authorizing external effects.

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Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

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Connect a customer agent to Coval and complete a small first evaluation with inspected voice or chat results. Use when no usable evaluation exists yet; reuse existing recordings and resources when available.

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Monitor a Coval run's progress with live updates. Use when user wants to check run status or wait for completion.

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Launch a specific Coval evaluation with explicit cases and execution bounds. Use when resources are already selected; for result analysis use quick-eval.

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