# The algo records whose force is semantic_consistency

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

## Entries

- [Semantic Operation Boundary](https://banes-lab.com/records/algo/semantic-operation-boundary.md): Define required work as semantic operations, defer runtime execution details to adapters, and reject runtime-specific primitives from the portable core contract.
- [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.
- [Existing Pattern Extraction](https://banes-lab.com/records/algo/existing-pattern-extraction.md): Inspect existing agent specifications, count phase markers, validation gates, and semantic operations, extract reusable structures, and derive baseline design conventions.
- [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.
- [Semantic Compliance Validation](https://banes-lab.com/records/algo/semantic-compliance-validation.md): Re-read the persisted agent specification, search for semantic operations, phase markers, validation gates, and runtime-specific leakage, then mark compliant only when required counts pass and leakage is absent.
- [Workspace Configuration Discovery](https://banes-lab.com/records/algo/workspace-configuration-discovery.md): Read workspace configuration, extract zones, semantic extensions, root path, agent definitions, shared zone, workflow zone, and task zone before generating any workflow artifacts.
- [Shared Document Workspace](https://banes-lab.com/records/algo/shared-document-workspace.md): Pre-create workflow documents once, require every agent to read and edit the same documents, prohibit duplicate versions, and enforce single source of truth across phases.
- [Agent Document Responsibility](https://banes-lab.com/records/algo/agent-document-responsibility.md): For each core document, require every agent to read current state, identify outdated or incorrect content, remove stale sections, replace with new discoveries, avoid duplicate findings, and maintain single source of truth.
- [Workflow Coordination Sequence](https://banes-lab.com/records/algo/workflow-coordination-sequence.md): Pre-create documents, execute parallel batches where eligible, execute sequential agents one at a time, update checkpoints, validate single source of truth, and mark workflow completion.
- [Anti-Pattern Propagation Kernel](https://banes-lab.com/records/algo/anti-pattern-propagation-kernel.md): Start with a local shortcut, repeat it under delivery pressure, normalize it as convention, allow dependent code to form around it, then make remediation expensive through coupling and hidden assumptions.
- [Measurement Normalization](https://banes-lab.com/records/algo/measurement-normalization.md): Convert each concept’s measured_by field into executable or reviewable metrics, collect metric values, normalize them to comparable scores, and attach confidence based on measurement quality.
- [Canonical Semantics](https://banes-lab.com/records/algo/canonical-semantics.md): Detect duplicated or conflicting models, schemas, terms, rules, and data definitions; select or create canonical authority; normalize variants; enforce single source of truth.
- [Control Plane Coordination](https://banes-lab.com/records/algo/control-plane-coordination.md): Separate control-plane policy from data-plane execution, centralize configuration, authentication, logging, or orchestration only where shared governance is beneficial, and preserve local autonomy where decentralization is required.
- [Interface Contract](https://banes-lab.com/records/algo/interface-contract.md): Define explicit interfaces with preconditions, postconditions, invariants, error semantics, version rules, and compatibility guarantees before implementation.
- [Canonical Data](https://banes-lab.com/records/algo/canonical-data.md): Normalize incoming data into a canonical schema, validate types and semantics, preserve one source of truth, and translate at system boundaries only.
- [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.
- [Transaction Boundary](https://banes-lab.com/records/algo/transaction-boundary.md): Define atomic state-change boundaries, isolate concurrent mutation, enforce consistency rules, and commit or roll back as a unit.
- [Idempotent Side Effect](https://banes-lab.com/records/algo/idempotent-side-effect.md): Assign a stable operation identity, check whether the effect was already applied, execute only once, and return the same semantic result for repeated requests.
- [Control Plane](https://banes-lab.com/records/algo/control-plane.md): Separate control concerns from data execution, centralize policy/configuration/authentication/logging where beneficial, and decentralize runtime execution where autonomy is required.
- [Streaming Dataflow](https://banes-lab.com/records/algo/architecture-streaming-dataflow.md): Process data sequentially through bounded pipeline stages, preserve forward-only semantics where required, apply backpressure, and keep stages stateless unless state is explicitly modeled.
- [Runtime-Neutral Automation Boundary](https://banes-lab.com/records/algo/runtime-neutral-automation-boundary.md): Express discovery, reading, searching, validation, persistence, and reporting as semantic operations, then delegate concrete mechanics to runtime adapters.
- [Automation Opportunity Detection](https://banes-lab.com/records/algo/automation-opportunity-detection.md): Search target scope for manual registries, hardcoded references, static lists, and duplicated discovery logic; then classify each finding with count, location, role, and maintenance signal.
- [Centralized Reference Resolver](https://banes-lab.com/records/algo/centralized-reference-resolver.md): Group duplicated hardcoded references, derive resolver names, define scope-safe resolution rules, provide configuration override fallback, and map each old reference to a resolver migration.
- [Cache Invalidation Strategy](https://banes-lab.com/records/algo/cache-invalidation-strategy.md): Define cache keys by scope, convention, adapter version, and implementation identity; invalidate by resource change, explicit request, time expiry, or implementation version change.
- [Entry Point Migration](https://banes-lab.com/records/algo/entry-point-migration.md): Locate composition roots, identify manual registrations and hardcoded references, compose updates that use discovery registries and centralized resolvers, and apply only in implementation mode.
- [Runtime-Agnostic Adapter Boundary](https://banes-lab.com/records/algo/runtime-agnostic-adapter-boundary.md): Express all workflow operations as semantic verbs, delegate runtime-specific mechanics to adapters, and prohibit repository-, shell-, framework-, model-, or path-specific logic from entering the core contract.
- [Capability Disclosure](https://banes-lab.com/records/algo/capability-disclosure.md): Declare required capabilities, probe or emulate each capability, mark unavailable capabilities explicitly, and downgrade confidence where capability gaps affect evidence quality.
- [Pattern Classification](https://banes-lab.com/records/algo/pattern-classification.md): Extract the target pattern description, classify it into a known centralization category, select a search strategy, select a validation strategy, and request the developer's decision only when classification remains ambiguous.
- [Refactor Intent Classification](https://banes-lab.com/records/algo/refactor-intent-classification.md): Determine whether the scattered pattern represents a duplication problem, default multiple-occurrence problems to replacement refactor, and require explicit debt justification for additive enhancement.
- [Iterative Variation Discovery](https://banes-lab.com/records/algo/iterative-variation-discovery.md): Search for the primary pattern, inspect match context, infer variants, add new variants to the search set, and repeat until no new variations appear or the iteration cap is reached.
- [Canonical Variation Selection](https://banes-lab.com/records/algo/canonical-variation-selection.md): Compare all detected variations by frequency, completeness, architectural fitness, and semantic coverage, then select the canonical implementation from evidence rather than preference.
- [Architecture Compliance Targeting](https://banes-lab.com/records/algo/architecture-compliance-targeting.md): Load relevant architecture and validation guidance, inspect existing centralized patterns, check naming and capacity constraints, detect conflicts, and select a justified centralization target.
- [Existing Solution Conflict](https://banes-lab.com/records/algo/existing-solution-conflict.md): Search existing centralized locations for the same or overlapping pattern, classify whether to reuse, extend, create a distinct target, or cancel, and require the developer's decision when conflict resolution is not mechanically safe.
- [Replacement Refactor](https://banes-lab.com/records/algo/replacement-refactor.md): Create the centralized implementation, establish reference infrastructure, migrate all occurrences, delete old definitions, verify zero duplication, and run final validation.
- [Additive Debt Gate](https://banes-lab.com/records/algo/additive-debt-gate.md): When a proposed abstraction leaves old patterns in place, require explicit approval, document retained debt, define deprecation conditions, and prohibit calling the result complete centralization.
- [Pattern-Specific Validation](https://banes-lab.com/records/algo/pattern-specific-validation.md): Select validation checks by pattern type, execute or mark each unavailable, verify architecture compliance, check all known variations for orphaned duplicates, and compute validation score.
- [Zero-Duplication Verification](https://banes-lab.com/records/algo/zero-duplication-verification.md): Search every known primary and variant pattern outside the approved centralized location, classify remaining matches as approved or orphaned, and require zero unapproved matches for replacement refactors.
- [Completion Truthfulness](https://banes-lab.com/records/algo/completion-truthfulness.md): Mark centralization complete only if all critical criteria pass, all old patterns are removed or justified, zero duplication is verified, validation evidence is recorded, and remaining issues are empty.
- [Centralization Report](https://banes-lab.com/records/algo/centralization-report.md): Compose a final report containing classification, research confidence, detection metrics, architecture decision, plan, execution status, validation score, remaining issues, artifact references, and unavailable capabilities.
- [Centralization Kernel](https://banes-lab.com/records/algo/centralization-kernel.md): Initialize runtime context, classify the target pattern, gather research, discover all variations, analyze architecture, build a refactor plan, optionally execute migration, validate zero duplication, and report status.
- [<Centralization Concern>](https://banes-lab.com/records/algo/centralization-concern.md): <Detect context> → <Classify pattern> → <Discover all occurrences and variants> → <Choose canonical source> → <Map migration> → <Gate execution> → <Validate zero debt> → <Report evidence>
- [Intent & Directionality Normalization](https://banes-lab.com/records/algo/intent-directionality-normalization.md): Normalize the task into a requested outcome, actions, and entities, and resolve the change relation — introduce, retain, remove, analyze, or mention — that later stages key their semantic policy on; an unresolved direction is recorded as ambiguity, never silently assumed.
- [Protocol Semantic Selection](https://banes-lab.com/records/algo/protocol-semantic-selection.md): Match the requested state transition and architecture surfaces semantically against the protocol library, select every protocol whose use-condition fits, and always inject the mandatory verification protocol.
- [Validation Stage](https://banes-lab.com/records/algo/validation-stage.md): Judge the generated reasoning — not the future implementation — against evidence and semantic policy: run the governance validation suites, verify every material claim by evidence, and apply the semantic-debt rubric before rendering.
- [Semantic Debt Policy](https://banes-lab.com/records/algo/semantic-debt-policy.md): Classify the relation a task holds to each controlled debt concept — backward-compatibility path, failure-masking fallback, deprecated or dual production path, deferred required work, shortcut debt, and unsupported superlative claim — blocking only the introduce and retain relations while allowing mention, analysis, quotation, and removal.
- [Violation Classification](https://banes-lab.com/records/algo/violation-classification.md): For each verification error, classify the violation into a known remediation category and attach the matching strategy.
- [Base-Class Compliance Remediation](https://banes-lab.com/records/algo/base-class-compliance-remediation.md): Detect classes missing required base abstraction, refactor inheritance or composition according to role rules, migrate duplicated lifecycle logic into hooks, and verify behavior remains represented.
- [Console Usage Remediation](https://banes-lab.com/records/algo/console-usage-remediation.md): Replace direct console calls with the approved logging abstraction, preserve severity and message context, and verify no direct console usage remains.
- [Defensive String Normalization](https://banes-lab.com/records/algo/defensive-string-normalization.md): Reject null input, remove dangerous path/control patterns, normalize Unicode representation, and only pass sanitized strings to filesystem, parser, or command boundaries.
- [Token Source-of-Truth](https://banes-lab.com/records/algo/token-source-of-truth.md): Define every design primitive — color, space, radius, the type scale, size, duration, z-index — exactly once as a custom property in the tokens layer, and forbid any downstream literal where a token exists.
- [Custom Type Registration](https://banes-lab.com/records/algo/custom-type-registration.md): Extend the stylable element vocabulary beyond the native HTML tag set by registering each semantic type against a base tag and a `data-el` stamp; the registered type becomes a first-class node the globals layer binds one appearance contract to, so a concept no native tag can express — a field-label, an icon, a toast, an entry — is styled by its type, never by a class.
- [Type-Migration Centralization](https://banes-lab.com/records/algo/type-migration-centralization.md): To move an existing purpose-class or page-scoped stylesheet into this model, treat every appearance-bearing class rule as a migration unit, derive the element type it decorates, register a custom `data-el` type when no tag or variant expresses it, move its look, size, and spacing into one global rule keyed by that type, demote any residual position into components and any residual layout into app, and verify that zero appearance rule keyed by a purpose-class remains outside the globals layer.
- [Persistence Fork](https://banes-lab.com/records/algo/persistence-fork.md): Specializing the Port Adapter, resolve the durable store by connection mode — an authoritative per-owner store when the trusted arm is connected, a read-only mirror otherwise — and write back only where accumulation is trusted.
- [Seed Composition](https://banes-lab.com/records/algo/seed-composition.md): Specializing Canonical Data, overlay the accumulated knowledge onto the frozen baseline scope as additive context without mutating the baseline.
- [Composed Turn Contract](https://banes-lab.com/records/algo/composed-turn-contract.md): Compose a model turn's validation schema and its instruction from one self-registering field registry, projected per mode, so the schema the response is graded against and the instruction the model is given can never drift.
- [<Mode-Driven Response Schema>](https://banes-lab.com/records/algo/mode-driven-response-schema.md): Make the model's structured output a mode-driven composed contract — the output mirror of composed input context: per loop-owned mode the schema and instruction are projected from one field registry, the response is validated and self-heals against it, the typed fields are governed, and every turn is persisted as a versioned queryable record — so the output has one source, cannot drift from its instruction, and can be audited.
- [PAG Document Declaration](https://banes-lab.com/records/algo/pag-document-declaration.md): Bind a document to a declared type and its default verb (THIS <TYPE> <VERB> <description>) after the YAML frontmatter and before any node, then declare in the meta block what the document is for, which sources ground which, what it trusts, what it may touch and what it declares outside itself, and how far repair may recurse.
- [PAG Keyword Ontology](https://banes-lab.com/records/algo/pag-keyword-ontology.md): Draw every operative token from a fixed, uppercase, code-frequent vocabulary partitioned into semantic categories, each token grounded to a record in the reasoning ontology, and bind targets to sources through explicit prepositions, so the model completes recognised structured patterns rather than interpreting prose.
- [PAG Ambiguity Reduction](https://banes-lab.com/records/algo/pag-ambiguity-reduction.md): Replace interpretive prose with explicit structured tokens so the model completes recognized patterns, while accepting that output stays probabilistic: the grammar reduces input ambiguity, it does not constrain output tokens or guarantee determinism.
- [<PAG Instruction Concern>](https://banes-lab.com/records/algo/pag-instruction-concern.md): <Declare document type and jurisdiction> → <Draw grounded uppercase directives> → <Decompose into headed, contracted, gated nodes> → <Bind semantic operations and structure declarations> → <Bound with invariant records> → <Validate well-formedness against the failure taxonomy>
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [<Pattern Distillation Concern>](https://banes-lab.com/records/algo/pattern-distillation-concern.md): <Initialize evidence workspace> → <Measure current architecture> → <Group semantic families> → <Extract behavioral signatures> → <Detect duplicate/inconsistent behavior> → <Score anti-patterns> → <Evaluate abstraction boundary> → <Compose reusable schematic> → <Migrate with rollback> → <Verify old-pattern elimination> → <Record ROI>
- [Quality Governance Loop](https://banes-lab.com/records/algo/quality-governance-loop.md): On each update (an apply), normalize the proposal, resolve a CheckPlan, verify it, and on failure fix-then-reverify the downstream set until the verdict is clean or the pass bound is reached, then escalate.
- [Canonical Config Resolution](https://banes-lab.com/records/algo/canonical-config-resolution.md): Expand one canonical config across selected profiles and native rules into per-tool config, resolve exactly one owner per contested surface, detect the four conflict kinds fail-closed, route loosening or self-certification to the developer's gate, and emit a resolved plan.
- [Stage Ordering](https://banes-lab.com/records/algo/stage-ordering.md): Build the runnability DAG from dependsOn, topologically order the cascade by the invalidates relation so fixes point forward, partition into normalization prefix, structural band, and semantic suffix, and permit back-edges only inside the suffix.
- [Comment Normalization Remediation](https://banes-lab.com/records/algo/comment-normalization-remediation.md): Express the comment-strip rule once as canonical behavior, bind it per language from a comment-grammar descriptor, run the toolchain-free token-scan backing on the in-memory proposal and the real-AST backing in the sandbox, preserve directive comments, and trust a backing only after calibration and adversarial testing.
- [Quality-Engine Kernel](https://banes-lab.com/records/algo/quality-engine-concern.md): Resolve a canonical policy into a per-ecosystem CheckPlan, order its stages by the invalidates relation, normalize the proposal, run generation checks on the server and project tools via the relay, derive a machine verdict, and drive a bounded downstream cascade to clean — holding no runtime execution in the engine itself.
- [Taxonomy Jurisdiction](https://banes-lab.com/records/algo/taxonomy-jurisdiction.md): Read the registry's root, container, bucket and ignore declarations, enumerate every file under a declared root, and separate the governed set from the ignored set and from the folders declared as neither, so jurisdiction is read rather than inferred.
- [Path Role Walk](https://banes-lab.com/records/algo/path-role-walk.md): Walk every governed path from its root, assign each folder depth a role from the declared grammar in the ordered sequence container then subject then concern, and emit the depth-to-role edge list together with the depths that repeat a role, revisit an earlier one, or exceed the cap.
- [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.
- [Name Projection](https://banes-lab.com/records/algo/name-projection.md): Compose the target name from the assigned concern, the subject, and a variant taken only where a collision or a facet requires one, and derive the folder chain from that same concern, so the filename and its placement are one projection rather than two decisions.
- [Vocabulary Admission Gate](https://banes-lab.com/records/algo/vocabulary-admission-gate.md): Hold every proposed word against the declared vocabularies and the rejection table, resolve and name the declared word that covers it where one does, refuse a word that names a process, an adjective, a grouping label, or a measurement, sort a surviving word by the is-a test into concerns or subjects, and admit it only by developer-approved registry edit.
- [Taxonomy Completion](https://banes-lab.com/records/algo/taxonomy-completion.md): Mark the taxonomy complete only when every governed file resolves against the grammar at both levels, no split candidate is outstanding, and every shape-discovered surface has a preserved verdict, leaving it incomplete while any residual remains.
- [Taxonomy Kernel](https://banes-lab.com/records/algo/taxonomy-kernel.md): Resolve jurisdiction from the registry, order the containers by reshape risk, walk each path into its ordered roles, classify each file to its narrowest concern, project the name and the folder chain together, reshape one container at a time, hold every proposed word at the admission gate, verify that pattern-resolved surfaces still collect what they collected, ledger the state, and terminate only when no residual finding remains.
- [<Taxonomy Concern>](https://banes-lab.com/records/algo/taxonomy-concern.md): <Resolve jurisdiction from the declared roots> → <Order containers by reshape risk> → <Walk each path into ordered roles> → <Classify each file to its narrowest concern> → <Project the name and its folder chain together> → <Reshape one container at a time> → <Hold every proposed word at the admission gate> → <Verify pattern-resolved discovery survived> → <Ledger the state> → <Terminate on no residual finding>
- [Surface Grid Walk](https://banes-lab.com/records/algo/surface-grid-walk.md): Walk the dimension-by-lens grid across the catalog, mark every cell that carries a surface, and surface the empty cells as candidate gaps using the anomaly lens, so a missing aspect is detected structurally rather than by recollection.
- [Test Coverage Kernel](https://banes-lab.com/records/algo/test-coverage-kernel.md): Load the surface catalog for a unit, walk the dimension-by-lens grid, derive the required-uncovered surfaces, prioritise them by risk, select a mode-matched technique and invariant per surface, author the test, issue an evidence verdict, ledger the state, and terminate only when no required surface remains unknown.
- [<Test Coverage Concern>](https://banes-lab.com/records/algo/test-coverage-concern.md): <Load the surface catalog for a unit> → <Walk the dimension x lens grid> → <Derive the required-uncovered surfaces> → <Prioritise by risk> → <Select a mode-matched technique and its invariant> → <Author the test> → <Issue an evidence verdict> → <Ledger the coverage state> → <Terminate when no required surface is unknown>