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Skill registry architecture defines the structural design of a centralized or federated catalog in which AI agent capabilities are formally registered, versioned, and made discoverable to orchestration engines. Each registry entry encodes a skill's input schema, output format, authentication requirements, and operational constraints as machine-readable metadata. The architecture supports dynamic registration and deregistration, enabling orchestration layers to maintain accurate real-time inventories of available capabilities across distributed agent ecosystems.

A capability descriptor schema is the formal specification language used to express what an AI agent skill can do, what inputs it requires, what outputs it produces, and under what operational conditions it functions reliably. Descriptors follow a standardized format that enables automated parsing by orchestration engines during skill selection and task planning phases. Rich descriptors include performance envelope metadata such as expected latency bounds, throughput limits, and accuracy confidence intervals.

A skill discovery protocol is the standardized query-and-response mechanism through which an orchestration engine locates agent capabilities matching a given task requirement from one or more skill registries. The protocol supports both exact-match and semantic-similarity search modes, allowing discovery of skills that partially satisfy complex task specifications. Discovery results are ranked by relevance, freshness, and provider trust scores to guide optimal skill selection.

Orchestration plan compilation is the process of translating a high-level task objective into an executable sequence of skill invocations, specifying the order of operations, data flow between skills, parallelization opportunities, and conditional branching logic. The compiler resolves skill dependencies, validates input-output type compatibility across chained invocations, and produces a deployment-ready execution graph. Failed compilation emits structured diagnostic output identifying the specific constraint violations that prevented plan assembly.

A skill invocation contract is the formal agreement between an orchestration engine and a skill provider that specifies the exact parameters, authentication credentials, rate limits, and service-level expectations governing a single skill execution. Contracts are instantiated at invocation time from the skill's capability descriptor and the orchestrator's runtime policies. Contract violations trigger automated fallback behavior or escalation to alternative skill providers.

Dynamic skill composition is the runtime assembly of compound capabilities by chaining multiple atomic skills into novel execution sequences that were not explicitly pre-defined in the orchestration plan. The composition engine evaluates intermediate outputs against downstream skill input requirements and adaptively selects the next skill in the chain based on the evolving task state. This approach enables orchestration systems to handle novel task requests that exceed the coverage of any single registered skill.

Skill version governance establishes the policies and mechanisms for managing concurrent versions of the same skill within a registry, including version numbering conventions, deprecation timelines, backward compatibility requirements, and migration path documentation. Governance rules ensure that orchestration plans compiled against a specific skill version remain executable even as newer versions are published. Breaking changes trigger automated notifications to all dependent orchestration pipelines.

Capability gap analysis is the systematic comparison between the skills required by a target task specification and the skills currently available in the orchestration environment's registries. The analysis produces a structured deficit report identifying missing capabilities, partial capability matches, and skills that require parameter adaptation to meet task requirements. Gap analysis outputs drive procurement decisions for new skill integrations and inform the prioritization of custom skill development efforts.

Skill trust attestation is the verification process by which an orchestration engine confirms that a registered skill meets minimum security, reliability, and compliance standards before authorizing its inclusion in execution plans. Attestation evidence may include cryptographically signed capability claims, third-party audit certificates, and historical performance metrics validated against declared service-level targets. Skills lacking valid attestation are quarantined from production orchestration flows.

Execution context propagation is the mechanism by which task-level metadata, authentication tokens, tracing identifiers, and accumulated intermediate results are passed between successive skill invocations within an orchestration plan. Proper propagation ensures that downstream skills receive the full environmental context needed to execute correctly without requiring redundant parameter specification. Context boundaries are enforced to prevent information leakage between logically isolated orchestration sessions.

Skill performance benchmarking is the standardized evaluation of registered skills against defined workload profiles to establish baseline metrics for latency, throughput, accuracy, and resource consumption. Benchmark results are stored alongside capability descriptors in the skill registry, enabling orchestration engines to make performance-informed selection decisions when multiple skills satisfy the same functional requirement. Benchmark suites are versioned and re-executed periodically to detect performance regression.

Adaptive skill selection is the runtime decision process by which an orchestration engine chooses among functionally equivalent skills based on current system conditions, including available capacity, recent error rates, cost constraints, and geographic proximity to the data being processed. The selection algorithm continuously re-evaluates its choices as conditions change during plan execution, dynamically rerouting to alternative skills if the initially selected provider degrades. Selection policies are configurable per orchestration tenant to reflect differing cost-performance trade-off preferences.

The orchestration observability layer is the instrumentation framework that captures distributed traces, structured logs, and real-time metrics across all skill invocations within an orchestration plan's execution lifecycle. Observability data is correlated by trace identifiers to reconstruct the full execution path of complex multi-skill workflows. The layer surfaces anomalies such as unexpected latency spikes, silent skill failures, and data transformation errors that would otherwise be invisible to the orchestration engine's control logic.

Skill marketplace economics encompasses the pricing models, billing mechanisms, and incentive structures that govern the commercial exchange of AI agent capabilities within an orchestration ecosystem. Marketplace economics determine how skill providers are compensated for usage, how orchestration tenants are billed for composite skill invocations, and how quality-based ranking algorithms promote high-performing providers. Economic models range from per-invocation metering to subscription-based capacity reservations with burst pricing tiers.

Federated skill resolution is the cross-registry discovery mechanism that enables an orchestration engine to locate and invoke skills hosted across multiple independent registries operated by different organizations or trust domains. The resolution protocol negotiates authentication, access control, and data sovereignty constraints at the inter-registry boundary before permitting skill invocation. Federation enables organizations to share specialized capabilities without centralizing their skill inventories into a single registry under common administrative control.