# The algo records whose domain is pattern-distillation This index as JSON: https://banes-lab.com/json/api/facets/algo/domain/pattern-distillation ## Entries - [Analysis Workspace](https://banes-lab.com/records/algo/analysis-workspace.md): Create a unique analysis session, allocate phase/metric/migration artifact locations, load baseline documentation and registries, write a manifest, and block continuation if required context is unavailable. - [Registry Baseline](https://banes-lab.com/records/algo/registry-baseline.md): Read existing architectural registries, extract known base abstractions, count implementations, measure hierarchy depth, and record the current abstraction state before proposing changes. - [Compliance Gap](https://banes-lab.com/records/algo/compliance-gap.md): Discover implementation classes by role, detect which ones conform to expected base abstractions, calculate noncompliance counts, and compute architectural compliance rate. - [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. - [Cross-Class Pattern Detection](https://banes-lab.com/records/algo/cross-class-pattern-detection.md): Search across semantic domains for repeated imports, repeated initialization, repeated lifecycle code, repeated error handling, repeated state setup, and repeated dependency wiring. - [Behavioral Inconsistency](https://banes-lab.com/records/algo/behavioral-inconsistency.md): Detect multiple competing implementations of the same behavior, count each variation, compute dominant-pattern consistency, and flag low-consistency behavior for normalization. - [Sequential Chain Duplication](https://banes-lab.com/records/algo/sequential-chain-duplication.md): For each class in the role family, extract the ordered sequence of orchestration steps, align sequences across the family, and surface repeated ordered chains that no textual-duplication scan would catch. - [Temporal Coupling Detection](https://banes-lab.com/records/algo/temporal-coupling-detection.md): Detect must-precede and must-follow ordering constraints between operations in each class, intersect them across the role family, and surface the shared temporal-coupling contract that a base lifecycle would centralize. - [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. - [Causal Wiring Duplication](https://banes-lab.com/records/algo/causal-wiring-duplication.md): Extract cause-to-effect edges (event to handler, failure to recovery, state change to reaction) per class, match trigger/reaction pairs across the role family, and surface repeated causal wiring that a base policy would centralize. - [Anomaly Outlier Detection](https://banes-lab.com/records/algo/anomaly-outlier-detection.md): Score each class's deviation from the dominant behavioral signature, identify the outliers, and name why each deviates, so inconsistency is localized to the deviant implementation rather than reported as an aggregate rate. - [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. - [Fractal Scale Duplication](https://banes-lab.com/records/algo/fractal-scale-duplication.md): Test whether a duplication shape recurs at more than one scale (method, class, module), and determine the scale at which the abstraction belongs, so single-scale scanning does not abstract at the wrong level. - [Anti-Pattern Classification](https://banes-lab.com/records/algo/anti-pattern-classification.md): Convert duplicated, inconsistent, and architecture-violating findings into anti-pattern records with type, occurrence count, impact, effort, severity, and affected resources. - [Anti-Pattern Priority Matrix](https://banes-lab.com/records/algo/anti-pattern-priority-matrix.md): Assign numeric impact and effort scores, calculate priority, sort anti-patterns, and group them into remediation bands. - [Abstraction Boundary Principle](https://banes-lab.com/records/algo/abstraction-boundary-principle.md): Evaluate each high-priority anti-pattern against boundary principles: universal, invariant, foundational, enforcing, and cognitive-load-reducing. - [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. - [Concrete-vs-Abstract Responsibility Split](https://banes-lab.com/records/algo/concrete-vs-abstract-responsibility-split.md): Partition the family's behavior into what is invariant across every member (the topology preserved under substitution) and what varies per member (the novelty); the invariant set becomes the concrete base, the variant set becomes the abstract seam. The concrete/abstract boundary is derived from the evidence, not read off a fixed lifecycle vocabulary — so the split holds for any paradigm, not only OOP class lifecycles. - [Template Method Lifecycle](https://banes-lab.com/records/algo/template-method-lifecycle.md): Define public lifecycle methods that enforce guard checks, call shared setup or cleanup, invoke subclass hooks, and centralize error handling. - [Base Schematic Composition](https://banes-lab.com/records/algo/base-schematic-composition.md): Generate the base abstraction from the selected candidate, enforce size constraints, split if oversized, and record which anti-patterns the abstraction eliminates. - [Migration Ordering](https://banes-lab.com/records/algo/migration-ordering.md): Sort target classes by complexity from lowest to highest, migrate simpler implementations first, and use early migrations to validate the abstraction before complex adoption. - [Backup-Verified Migration](https://banes-lab.com/records/algo/backup-verified-migration.md): For each target class, create a recoverable checkpoint, refactor it to extend or use the abstraction, verify the removed anti-pattern no longer exists, and restore from backup on failure. - [Anti-Pattern Elimination Verification](https://banes-lab.com/records/algo/anti-pattern-elimination-verification.md): After migration, search the entire target scope for old duplicate patterns, allow only approved base-location occurrences, and fail completion if unapproved duplicates remain. - [Registry Regeneration](https://banes-lab.com/records/algo/registry-regeneration.md): After creating or migrating abstractions, regenerate or update the architecture registry, reread it, and confirm the new base and migrated implementations are represented. - [Anti-Reintroduction Gate](https://banes-lab.com/records/algo/anti-reintroduction-gate.md): After eliminating an anti-pattern, author or strengthen the custom lint rule that statically forbids its reintroduction and any bypass of the new base, build the rule plugin, and regenerate the rule catalog, so the distilled boundary is enforced by a gate rather than by discipline. - [Distillation Metrics](https://banes-lab.com/records/algo/distillation-metrics.md): Calculate duplication reduction, code reduction, adoption rate, lines saved, maintenance burden reduction, and cognitive-load reduction after migration. - [Pattern Distillation History](https://banes-lab.com/records/algo/pattern-distillation-history.md): Append the completed analysis summary to a durable history log, store metric snapshots, and preserve lessons learned for future abstraction decisions. - [Completion Truthfulness](https://banes-lab.com/records/algo/pattern-distillation-completion-truthfulness.md): Mark pattern distillation complete only if workspace, baseline, semantic analysis, anti-pattern classification, abstraction selection, migration, verification, registry update, anti-reintroduction gate, and metrics logging all pass. - [Pattern Distiller Kernel](https://banes-lab.com/records/algo/pattern-distiller-kernel.md): Initialize an analysis workspace, read registry baselines, partition semantic domains, extract behavioral signatures, detect anti-patterns, prioritize them, evaluate abstraction boundaries, compose base schematics, migrate targets, verify elimination, update registries, calculate ROI, and persist history. - [](https://banes-lab.com/records/algo/pattern-distillation-concern.md): → → → → → → → → → →