feat(coder): add model resolution core + multi-batch matcher

Model resolution (from oh-my-openagent/model-core): 6-step priority
resolution pipeline (UI select -> user config -> category default ->
user fallback -> policy chain -> system default), provider fallback
chains, fuzzy model matching, error classification, provider-specific
model ID transforms. 14 files, zero runtime deps.

Multi-batch matcher (from boocontext-audit): 6 batch types
(Observational, Actionable, PreviouslyApplied, Disambiguation,
ResponseAnalysis, LowCriticality) for behavioral guideline evaluation.
RelationalResolver with iterative convergence (DEPENDS_ON,
PRIORITIZES, ENTAILS, TAG_ALL, TAG_PRIORITIZES). SchematicGenerator
abstract class with retry and execution plans. 4 files.

apps/coder/src/services/behavioral/generation.ts

@@ -0,0 +1,204 @@
+/**
+ * Schematic generator for behavioral guideline batches.
+ *
+ * Port of boocontext-audit/src/generation.ts — abstract LLM batch caller
+ * with temperature retry and structured output per batch type.
+ */
+
+import { type GenerationInfo } from './matching.js';
+
+// ─── Output types per batch ───
+
+export interface ObservationalOutput {
+  checks: {
+    guideline_id: string;
+    condition: string;
+    rationale: string;
+    applies: boolean;
+  }[];
+}
+
+export interface ActionableOutput {
+  checks: {
+    guideline_id: string;
+    condition: string;
+    action: string;
+    rationale: string;
+    applies: boolean;
+  }[];
+}
+
+export interface PreviouslyAppliedOutput {
+  checks: {
+    guideline_id: string;
+    condition: string;
+    action_segment: string;
+    rationale: string;
+    is_still_applicable: boolean;
+  }[];
+}
+
+export interface DisambiguationOutput {
+  source_guideline_id: string;
+  rationale: string;
+  enriched_action: string;
+  targets: string[];
+}
+
+export interface ResponseAnalysisOutput {
+  guideline_id: string;
+  condition: string;
+  was_followed: boolean;
+  rationale: string;
+}
+
+// ─── Batch output map ───
+
+export interface BatchOutputMap {
+  observational: ObservationalOutput;
+  actionable: ActionableOutput;
+  previously_applied: PreviouslyAppliedOutput;
+  disambiguation: DisambiguationOutput;
+  response_analysis: ResponseAnalysisOutput;
+}
+
+export type BatchTypeKey = keyof BatchOutputMap;
+
+export type OutputForBatch<T extends BatchTypeKey> = BatchOutputMap[T];
+
+// ─── SchematicGenerator ───
+
+export abstract class SchematicGenerator<TSchema> {
+  constructor(public modelName: string) {}
+
+  abstract generate(
+    prompt: string,
+    hints?: Record<string, unknown>,
+  ): Promise<{
+    content: TSchema;
+    info: GenerationInfo;
+  }>;
+}
+
+/**
+ * Default stub implementation that returns empty results.
+ * Replace with a real LLM caller in production.
+ */
+export class DefaultSchematicGenerator
+  implements SchematicGenerator<unknown>
+{
+  constructor(
+    public modelName: string,
+    public defaultTemperature = 0.7,
+  ) {}
+
+  async generate(
+    _prompt: string,
+    hints?: Record<string, unknown>,
+  ): Promise<{ content: unknown; info: GenerationInfo }> {
+    const temperature = (hints?.temperature as number) ?? this.defaultTemperature;
+    return {
+      content: {},
+      info: {
+        model: this.modelName,
+        duration: 0,
+        tokens: 0,
+        temperature,
+      },
+    };
+  }
+}
+
+// ─── Execution plans ───
+
+export interface BatchExecutionPlan {
+  batchType: BatchTypeKey;
+  guidelines: { id: string; condition: string; action?: string | null }[];
+  priority: number;
+  independent: boolean;
+}
+
+/**
+ * Create an ordered execution plan from categorized guideline collections.
+ * Groups are sorted by priority: previously_applied (fastest) first,
+ * then observational, actionable, disambiguation, low-criticality last.
+ */
+export function createExecutionPlan(
+  observational: { id: string; condition: string }[],
+  actionable: { id: string; condition: string; action: string }[],
+  previouslyApplied: { id: string; condition: string; action?: string | null }[],
+  disambiguationGroups: { source: string; targets: string[]; enrichedAction: string }[],
+  lowCriticality: { id: string; condition: string }[],
+): BatchExecutionPlan[] {
+  const plans: BatchExecutionPlan[] = [];
+
+  if (observational.length > 0) {
+    plans.push({
+      batchType: 'observational',
+      guidelines: observational.map((g) => ({ id: g.id, condition: g.condition })),
+      priority: 1,
+      independent: true,
+    });
+  }
+
+  if (actionable.length > 0) {
+    plans.push({
+      batchType: 'actionable',
+      guidelines: actionable.map((g) => ({
+        id: g.id,
+        condition: g.condition,
+        action: g.action,
+      })),
+      priority: 2,
+      independent: true,
+    });
+  }
+
+  if (previouslyApplied.length > 0) {
+    plans.push({
+      batchType: 'previously_applied',
+      guidelines: previouslyApplied.map((g) => ({
+        id: g.id,
+        condition: g.condition,
+        action: g.action,
+      })),
+      priority: 0,
+      independent: true,
+    });
+  }
+
+  if (disambiguationGroups.length > 0) {
+    plans.push({
+      batchType: 'disambiguation',
+      guidelines: disambiguationGroups.map((g) => ({
+        id: g.source,
+        condition: g.enrichedAction,
+      })),
+      priority: 3,
+      independent: true,
+    });
+  }
+
+  if (lowCriticality.length > 0) {
+    plans.push({
+      batchType: 'observational',
+      guidelines: lowCriticality.map((g) => ({ id: g.id, condition: g.condition })),
+      priority: 10,
+      independent: true,
+    });
+  }
+
+  return plans.sort((a, b) => a.priority - b.priority);
+}
+
+/**
+ * Compute retry temperatures: base + 0.2 * attempt.
+ * Provides progressive temperature increases for failed calls.
+ */
+export function getRetryTemperatures(baseTemp: number, maxAttempts = 3): number[] {
+  const temps: number[] = [];
+  for (let i = 0; i < maxAttempts; i++) {
+    temps.push(baseTemp + i * 0.2);
+  }
+  return temps;
+}