위 분류에 속하지 않는 스킬 (1.3만건)
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
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FIPA standard library of agent interaction protocols for structured multi-agent communication
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FIPA standard library of communicative acts (speech acts) for agent-to-agent messaging semantics
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Foundational impossibility result proving consensus cannot be guaranteed in asynchronous systems with even one faulty process
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Meta-skill that orchestrates the discourse skill suite: routes between steel-man-argument, socratic-questioning, toulmin-argument-analysis, logical-fallacy-detector, bad-faith-rhetoric-detector, discourse-elision-analyzer, forensic-speech-structure, and productive-discourse-facilitator. Use when analyzing conversations, facilitating discourse, or operating as a dialogue copilot. Maintains mode (silent analysis, coaching overlay, active facilitator) and decides which skills to invoke based on conversation dynamics.
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Foundational distributed computing algorithms for consensus, coordination, and fault tolerance
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Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution. 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 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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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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Validates agent outputs against expected schemas and quality criteria. Ensures outputs meet structural requirements and content standards. 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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Manages context passing between DAG nodes and spawned agents. Handles context summarization, selective forwarding, and token budget optimization. Activate on 'bridge context', 'pass context', 'summarize context', 'context management', 'agent context'. NOT for execution (use dag-parallel-executor) or aggregation (use dag-result-aggregator).
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Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
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Manages agent isolation levels and resource boundaries. Configures strict, moderate, and permissive isolation profiles. 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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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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Algorithms for decomposing DAGs into chains for parallel execution and resource optimization
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Review of Cognitive Systems Engineering applications and current practice in safety-critical domains
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Describe patterns that may indicate bad-faith or manipulative communication including gish gallop, whataboutism, tone policing, motte-and-bailey, sealioning, lying by omission, deflection, and strategic evasion. Use when the user suspects manipulation, notices repeated non-engagement, wants to analyze debate tactics, or mentions 'bad faith', 'deflection', 'manipulation', 'dodging the question', 'avoiding the issue', 'gaslighting', 'moving goalposts'. Describes behavioral patterns without making psychological diagnoses.
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Integration of BDI agent architecture with soft systems methodology for complex organizational problem-solving
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Expert causal reasoning and mental model construction for diagnosis and prediction in complex domains
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Implement executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.
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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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JADE platform for building FIPA-compliant multi-agent systems with standardized agent communication
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Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system
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