# The algo records whose force is domain_boundary

This index as JSON: https://banes-lab.com/json/api/facets/algo/force/domain-boundary

## Entries

- [Evidence-Before-Generation](https://banes-lab.com/records/algo/evidence-before-generation.md): Discover available context, inspect target-domain resources, extract concrete facts, construct a knowledge base, and generate only from verified evidence.
- [Creation History Collision](https://banes-lab.com/records/algo/creation-history-collision.md): Load existing agent and invocation registries, map existing identities, compare target name and domain, and require the developer's decision before refinement, renaming, replacement, or cancellation.
- [Domain Cache Validation](https://banes-lab.com/records/algo/domain-cache-validation.md): Normalize the domain path, compute a domain hash, search cache entries, calculate cache age, and reuse cached knowledge only when cache identity and TTL are valid.
- [Scope Extraction](https://banes-lab.com/records/algo/scope-extraction.md): Determine whether the target is a resource, directory, module, repository, or unknown scope; then extract interfaces, declarations, dependencies, systems, or boundaries appropriate to that scope.
- [Non-Destructive Domain Investigation](https://banes-lab.com/records/algo/non-destructive-domain-investigation.md): Prepare static investigation, optionally prepare safe executable analysis, prohibit source mutation, execute or emulate analysis, and merge observable outputs into domain intelligence.
- [Domain Knowledge Base](https://banes-lab.com/records/algo/domain-knowledge-base.md): Store target scope, discovered structure, purposes, dependencies, interfaces, patterns, statistics, timestamp, and evidence sources in a reusable knowledge object.
- [Risk Complexity Reversibility](https://banes-lab.com/records/algo/risk-complexity-reversibility.md): Analyze domain dependencies, side effects, resource count, interface count, dependency patterns, and uncertainty factors to assign risk, complexity, reversibility, and uncertainty.
- [Knowledge Documentation Relevance](https://banes-lab.com/records/algo/knowledge-documentation-relevance.md): Discover knowledge documents, read descriptions, compare them against domain characteristics, score relevance, and exclude irrelevant documents from core reasoning.
- [Principle Extraction](https://banes-lab.com/records/algo/principle-extraction.md): Start from structural principles, test applicability against domain characteristics, add domain-specific principles from relevant documents, and exclude runtime-specific principles from the core.
- [Adaptive Phase Boundary](https://banes-lab.com/records/algo/adaptive-phase-boundary.md): Select phase count from risk and complexity, assign phase boundaries, and strengthen validation density for high-risk or high-complexity domains.
- [Portable Contract Composition](https://banes-lab.com/records/algo/portable-contract-composition.md): Compose identity, purpose, methodology, domain scope, characteristics, capabilities, phases, validation strategy, constraints, and required outputs into a runtime-neutral agent contract.
- [Audit Artifact](https://banes-lab.com/records/algo/audit-artifact.md): Record agent name, domain, operation mode, runtime environment, risk, complexity, reversibility, uncertainty, phase count, validation gates, evidence sources, capability profile, adapter identity, portability status, and timestamp.
- [Final Generation Report](https://banes-lab.com/records/algo/final-generation-report.md): Summarize generated agent, target domain, operation mode, adapter, risk, complexity, reversibility, uncertainty, phase count, validation-gate count, compliance results, artifact references, degraded mode, and unsupported capabilities.
- [Agent Creator Kernel](https://banes-lab.com/records/algo/agent-creator-kernel.md): Load configuration, detect capabilities, check creation history, validate cache, discover domain evidence, build knowledge base, analyze characteristics, extract principles, define phases, compose portable contract, render adapter artifacts, validate outputs, audit, and report.
- [<Agent Generation Concern>](https://banes-lab.com/records/algo/agent-generation-concern.md): <Load context> → <Detect capabilities> → <Check collisions> → <Discover domain evidence> → <Build knowledge> → <Analyze risk> → <Design phases> → <Compose portable contract> → <Render adapters> → <Validate grounding> → <Audit> → <Report>
- [Domain Boundary Governance](https://banes-lab.com/records/algo/domain-boundary-governance.md): Identify bounded contexts, ubiquitous language, context relationships, domain model ownership, anti-corruption layers, and explicit boundary rules.
- [Architectural Style Selection](https://banes-lab.com/records/algo/architectural-style-selection.md): Select monolith, modular monolith, layered, component-based, package-by-feature, clean, hexagonal, ports-and-adapters, or microservices architecture based on deployment autonomy, domain complexity, operational maturity, consistency needs, and coupling tolerance.
- [Domain Boundary](https://banes-lab.com/records/algo/domain-boundary.md): Identify bounded contexts, define ubiquitous language inside each context, map relationships between contexts, and use anti-corruption layers when semantics differ.
- [Architectural Style Boundary](https://banes-lab.com/records/algo/architectural-style-boundary.md): Select an architectural style by dependency direction, deployment needs, domain size, team topology, and change isolation requirements; enforce style through boundary rules.
- [Port Adapter](https://banes-lab.com/records/algo/port-adapter.md): Place domain logic behind inbound and outbound ports, implement external technology through adapters, and forbid domain dependence on infrastructure.
- [Event Messaging](https://banes-lab.com/records/algo/event-messaging.md): Convert state changes into events, classify domain versus integration events, publish through durable channels, consume idempotently, and preserve ordering where required.
- [Intentional Static Separation](https://banes-lab.com/records/algo/intentional-static-separation.md): For each static pattern, compare size, frequency, stability, and risk; classify small or stable patterns as intentionally static candidates instead of automation debt.
- [<Automation Concern>](https://banes-lab.com/records/algo/automation-concern.md): <Detect static coordination point> → <Classify intentional vs problematic> → <Measure scale pressure> → <Verify convention strength> → <Design dynamic contract> → <Preserve fallback> → <Validate performance and architecture> → <Capture knowledge>
- [PAG Node Decomposition](https://banes-lab.com/records/algo/pag-node-decomposition.md): Group directives into nodes, each one decision on one reasoning axis, headed by its layer, axis, math type and the shape its decision yields, tagged with the substrate stage its artifact comes to be at, and contracted so that its input names the prior node's output or a declared slot, its transform is stated in semantic operations, and its output is the one record the next node reads.
- [Semantic Domain Partitioning](https://banes-lab.com/records/algo/semantic-domain-partitioning.md): Group implementation resources by semantic role, such as manager, repository, handler, service, controller, adapter, or worker, then analyze each family separately.
- [Behavioral Signature Extraction](https://banes-lab.com/records/algo/behavioral-signature-extraction.md): For each class in a semantic domain, inspect constructor behavior, lifecycle hooks, error handling, state management, dependency acquisition, and public orchestration methods.
- [Relational Graph Duplication](https://banes-lab.com/records/algo/relational-graph-duplication.md): Build the dependency-acquisition subgraph for each class, test for isomorphic subgraphs across the role family, and surface repeated object-graph wiring that a base or factory would assemble once.
- [Conceptual Duplication Detection](https://banes-lab.com/records/algo/conceptual-duplication-detection.md): Derive a name-independent semantic signature for each behavior (intent, input-to-output shape, effects), cluster behaviors by meaning rather than identifier, and surface same-meaning/different-name clusters that only the semantic lens can detect.
- [Base-Class Candidate Selection](https://banes-lab.com/records/algo/base-class-candidate-selection.md): Promote an anti-pattern to a base-class candidate only when it satisfies enough boundary principles and applies across a meaningful portion of the semantic domain.
- [Concern Classification](https://banes-lab.com/records/algo/concern-classification.md): Read each governed file, assign the narrowest accurate concern from the declared vocabulary by its primary responsibility, record a file that fits two concerns as a split candidate, and break an irreducible overlap by the domain-ward layer, so classification is judgement against the file rather than pattern-matching against its path.