feat(ms-ai-architect): Sesjon 15 — B2 K10 søsken-scope-ikke-overlapp

- Refaktor: overlap-kjerne flyttet til scripts/kb-eval/lib/sibling-overlap.mjs
  (bryter sirkulær import eval.mjs<->detect); detect re-eksporterer → B1-tester urørt
- K10 = søsken-scope-ikke-overlapp, deterministisk cross-skill: perSkillSiblingOverlap
  + attachSiblingOverlap i eval.mjs. combined = boundaryTension + df-vektet leksikalsk;
  per-skill verdikt = verste søskenpar; terskel 7.0 (naturlig gap 7.42→6.67)
- Empirisk (alle 5): eng+infra FAIL (7.42 mot hverandre), advisor/gov/sec PASS
  → eng↔infra-signal mater B3 merge/saner (ikke blokkering)
- Gated baseline-regen via --write (descriptions urørt → judge K1/K4/K7/K8/K9 merget
  uendret, ikke fabrikkert); rubric K1-K10
- TDD: +9 tester (tests/kb-eval/test-k10-sibling-overlap.test.mjs), kb-eval 31→40
- 0 skriving til skills/. Suiter: validate 239 · kb-update 122 · kb-integrity 192/192

scripts/kb-eval/detect-skill-lifecycle.mjs

@@ -39,6 +39,29 @@ import { fileURLToPath } from 'node:url';
 import { splitFrontmatter, extractDescription, checkK3 } from './eval.mjs';
 import { loadTaxonomy } from '../kb-update/lib/taxonomy.mjs';
 import { atomicWriteJson } from '../kb-update/lib/atomic-write.mjs';
+import {
+  FOCUS_PAIR,
+  tokenize,
+  extractTriggerSurface,
+  buildDocumentFrequency,
+  pairKey,
+  lexicalOverlap,
+  boundaryTensionMatrix,
+  computeOverlapFromInputs,
+} from './lib/sibling-overlap.mjs';
+
+// Re-export the overlap core (moved to lib/sibling-overlap.mjs in S15/B2 to
+// avoid a circular import with eval.mjs) so existing B1 callers/tests keep
+// importing it from this module unchanged.
+export {
+  tokenize,
+  extractTriggerSurface,
+  buildDocumentFrequency,
+  pairKey,
+  lexicalOverlap,
+  boundaryTensionMatrix,
+  computeOverlapFromInputs,
+};
 
 const __dirname = dirname(fileURLToPath(import.meta.url));
 const PLUGIN_ROOT = join(__dirname, '..', '..');
@@ -48,9 +71,6 @@ const TAX_DATA_DIR = join(__dirname, '..', 'kb-update', 'data');
 const PROMPTS_FILE = join(DATA_DIR, 'k1-trigger-prompts.json');
 const OUT_FILE = join(DATA_DIR, 'skill-lifecycle-report.json');
 
-// Operator-designated B1 focus boundary.
-const FOCUS_PAIR = ['ms-ai-engineering', 'ms-ai-infrastructure'];
-
 // --- S14 detector thresholds (named, documented) -------------------------
 // K3 hard body-length limit (mirrors eval.mjs K3_MAX_BODY_LINES — single source
 // of bodyLines is eval.checkK3; this constant only computes the margin).
@@ -74,121 +94,6 @@ const LAST_UPDATED_PATTERNS = [
   /\*\*Dato:\*\*\s*([\d-]+)/i,
 ];
 
-// Function words (no + en) of length >= 3. Tokens < 3 chars are dropped anyway,
-// so this list only needs the longer connectives. Domain nouns are NOT here —
-// df-weighting handles common domain vocabulary instead.
-const STOPWORDS = new Set([
-  'the', 'and', 'for', 'with', 'between', 'before', 'not', 'are', 'that', 'this',
-  'into', 'over', 'per', 'use', 'used', 'when', 'which', 'how', 'via', 'from',
-  'eller', 'som', 'til', 'med', 'mot', 'ved', 'for', 'har', 'kan', 'ikke', 'der',
-  'det', 'den', 'ein', 'eit', 'sin',
-  // description-format boilerplate (every skill ends with "Triggers on:")
-  'triggers', 'trigger',
-]);
-
-/** Lowercase, split on non-alphanumeric, drop stopwords + tokens < 3 chars. */
-export function tokenize(text) {
-  return (text.toLowerCase().match(/[a-z0-9æøå]+/g) || [])
-    .filter((w) => w.length >= 3 && !STOPWORDS.has(w));
-}
-
-/** Trigger surface of a description: quoted phrases + content-token set. */
-export function extractTriggerSurface(description) {
-  const phrases = (description.match(/"([^"]+)"/g) || []).map((p) => p.slice(1, -1));
-  return { phrases, tokens: new Set(tokenize(description)) };
-}
-
-/** token -> number of skill-surfaces that contain it. */
-export function buildDocumentFrequency(surfaces) {
-  const df = new Map();
-  for (const s of surfaces) {
-    for (const t of s.tokens) df.set(t, (df.get(t) || 0) + 1);
-  }
-  return df;
-}
-
-/** Order-independent pair key. */
-export function pairKey(a, b) {
-  return [a, b].sort().join('|');
-}
-
-/** Lexical overlap between two surfaces, df-weighted. */
-export function lexicalOverlap(surfaceA, surfaceB, df) {
-  const shared = [];
-  for (const t of surfaceA.tokens) if (surfaceB.tokens.has(t)) shared.push(t);
-  shared.sort();
-  const union = new Set([...surfaceA.tokens, ...surfaceB.tokens]).size;
-  const jaccard = union > 0 ? shared.length / union : 0;
-  let weightedScore = 0;
-  for (const t of shared) weightedScore += 1 / (df.get(t) || 1);
-  return {
-    shared,
-    jaccard: Number(jaccard.toFixed(4)),
-    weightedScore: Number(weightedScore.toFixed(4)),
-  };
-}
-
-/**
- * Symmetric boundary-tension matrix from the curated prompt set.
- * Counts out_of_domain entries whose belongs_to is one of the real skills
- * (controls / out-of-stack entries are ignored). Keyed by pairKey.
- */
-export function boundaryTensionMatrix(promptSet) {
-  const skills = Object.keys(promptSet).filter((k) => k !== '_meta');
-  const skillSet = new Set(skills);
-  const m = {};
-  for (const s of skills) {
-    for (const e of promptSet[s].out_of_domain || []) {
-      const b = e && e.belongs_to;
-      if (!skillSet.has(b) || b === s) continue;
-      const k = pairKey(s, b);
-      m[k] = (m[k] || 0) + 1;
-    }
-  }
-  return m;
-}
-
-/**
- * Pure core: given { skill -> description } and the curated prompt set, compute
- * the overlap report section. combined = boundaryTension + weightedScore
- * (operator-grounded primary signal + distinctive-lexical corroboration).
- */
-export function computeOverlapFromInputs(descriptionsBySkill, promptSet) {
-  const skills = Object.keys(descriptionsBySkill).sort();
-  const surfaces = {};
-  for (const s of skills) surfaces[s] = extractTriggerSurface(descriptionsBySkill[s]);
-  const df = buildDocumentFrequency(Object.values(surfaces));
-  const tension = boundaryTensionMatrix(promptSet);
-
-  const pairs = [];
-  for (let i = 0; i < skills.length; i++) {
-    for (let j = i + 1; j < skills.length; j++) {
-      const a = skills[i];
-      const b = skills[j];
-      const key = pairKey(a, b);
-      const lexical = lexicalOverlap(surfaces[a], surfaces[b], df);
-      const boundaryTension = tension[key] || 0;
-      const combined = Number((boundaryTension + lexical.weightedScore).toFixed(4));
-      pairs.push({ pair: [a, b], key, boundaryTension, lexical, combined });
-    }
-  }
-  // sort by combined desc, then key asc for stable ties
-  pairs.sort((x, y) => y.combined - x.combined || x.key.localeCompare(y.key));
-
-  const focusKey = pairKey(...FOCUS_PAIR);
-  const focusPair = pairs.find((p) => p.key === focusKey) || null;
-
-  return {
-    method:
-      'deterministic: (1) operator-curated boundary-tension (k1-trigger-prompts belongs_to), ' +
-      '(2) df-weighted lexical trigger-surface overlap. combined = boundaryTension + weightedScore.',
-    focusPairReason:
-      'Azure-deployment boundary engineering(build) <-> infrastructure(operate) — operator-designated B1 target.',
-    pairs,
-    focusPair,
-  };
-}
-
 // ===========================================================================
 // S14 — Detector 2: coverage/gap (in-domain) — taxonomy vs physical disk
 // ===========================================================================