Spor B fase B1, første detektor (lag-1-analog på SKILL-granularitet). PRODUSERER KUN RAPPORT — skriver aldri til skills/ (invariant bekreftet). Detektor (scripts/kb-eval/detect-skill-lifecycle.mjs) kombinerer to deterministiske overlapp-signaler per skill-par: - grensetension: operatør-kuratert k1-trigger-prompts.json belongs_to-graf (out_of_domain = håndmerkede confusable-naboer), symmetrisk telt - df-vektet leksikalsk trigger-surface-overlapp (1/df nedvekter domene- vanlige ord som «azure»; format-boilerplate «triggers» filtrert) combined = grensetension + weightedScore. Empirisk: eng↔infra topper (combined 7.42, tension 6, delt: architecture/ azure/data/multi) — operatørens Azure-deployment-grenseinstinkt bekreftet. Surfaces som focusPair (operatør-utpekt B1-mål). CLI: default human-summary · --json · --write (rapport gitignored som de andre deteksjonsrapportene; detektor + kuraterte inputs er tracked). TDD: 8 nye tester i tests/kb-eval/ (tokenize, surface, df, lexical, pairKey, tension differensial-sjekk, full compute m/determinisme). Gate møtt. Ingen regresjon: validate 239 · kb-update 122 · kb-eval 23 (15+8) · kb-integrity 192/192. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01REiKFhP4w6xGXXqWKpPCJJ
194 lines
8 KiB
JavaScript
194 lines
8 KiB
JavaScript
// test-skill-lifecycle-detect.test.mjs — Spor B / B1 overlap-detektor.
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// TDD: written before scripts/kb-eval/detect-skill-lifecycle.mjs exists.
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//
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// The overlap detector is DETERMINISTIC and combines two signals:
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// (1) operator-curated boundary-tension graph (k1-trigger-prompts.json belongs_to)
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// (2) df-weighted lexical trigger-surface overlap (down-weights domain-common tokens)
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// It must NEVER write to skills/ — it only produces a report.
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import { test } from 'node:test';
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import assert from 'node:assert/strict';
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import { readFileSync } from 'node:fs';
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import { dirname, join } from 'node:path';
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import { fileURLToPath } from 'node:url';
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import {
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tokenize,
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extractTriggerSurface,
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buildDocumentFrequency,
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lexicalOverlap,
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pairKey,
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boundaryTensionMatrix,
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computeOverlapFromInputs,
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} from '../../scripts/kb-eval/detect-skill-lifecycle.mjs';
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const __dirname = dirname(fileURLToPath(import.meta.url));
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const PROMPTS_PATH = join(__dirname, '..', '..', 'scripts', 'kb-eval', 'data', 'k1-trigger-prompts.json');
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const promptSet = JSON.parse(readFileSync(PROMPTS_PATH, 'utf8'));
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// ---------------------------------------------------------------------------
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// tokenize
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// ---------------------------------------------------------------------------
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test('tokenize: lowercases, strips punctuation, drops short + stopwords', () => {
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const t = tokenize('Azure AI Services for the RAG-pipeline, and BCDR. Triggers on: x.');
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assert.ok(t.includes('azure'), 'keeps azure');
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assert.ok(t.includes('services'), 'keeps services');
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assert.ok(t.includes('rag'), 'splits hyphen -> rag');
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assert.ok(t.includes('pipeline'), 'splits hyphen -> pipeline');
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assert.ok(t.includes('bcdr'), 'keeps bcdr');
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assert.ok(!t.includes('ai'), 'drops len<3 token ai');
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assert.ok(!t.includes('for'), 'drops stopword for');
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assert.ok(!t.includes('the'), 'drops stopword the');
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assert.ok(!t.includes('and'), 'drops stopword and');
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assert.ok(!t.includes('triggers'), 'drops description-format word triggers');
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// all lowercase
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assert.ok(t.every((w) => w === w.toLowerCase()));
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});
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// ---------------------------------------------------------------------------
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// extractTriggerSurface
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// ---------------------------------------------------------------------------
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test('extractTriggerSurface: pulls quoted phrases + content tokens', () => {
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const desc =
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'Deep guidance for building AI. Triggers on: "RAG architecture on Azure", "Azure AI Search".';
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const s = extractTriggerSurface(desc);
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assert.ok(Array.isArray(s.phrases));
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assert.equal(s.phrases.length, 2, 'two quoted phrases');
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assert.ok(s.phrases.includes('RAG architecture on Azure'));
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assert.ok(s.tokens instanceof Set);
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assert.ok(s.tokens.has('azure'));
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assert.ok(s.tokens.has('architecture'));
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});
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// ---------------------------------------------------------------------------
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// buildDocumentFrequency
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// ---------------------------------------------------------------------------
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test('buildDocumentFrequency: counts skills containing each token', () => {
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const surfaces = [
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{ tokens: new Set(['azure', 'rag']) },
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{ tokens: new Set(['azure', 'bcdr']) },
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{ tokens: new Set(['azure', 'dpia']) },
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];
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const df = buildDocumentFrequency(surfaces);
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assert.equal(df.get('azure'), 3);
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assert.equal(df.get('rag'), 1);
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assert.equal(df.get('bcdr'), 1);
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});
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// ---------------------------------------------------------------------------
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// lexicalOverlap — df-weighting down-weights common tokens
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// ---------------------------------------------------------------------------
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test('lexicalOverlap: shared tokens, jaccard, df-weighted score', () => {
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const sA = { tokens: new Set(['azure', 'deployment', 'rag']) };
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const sB = { tokens: new Set(['azure', 'deployment', 'bcdr']) };
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const df = new Map([
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['azure', 5],
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['deployment', 2],
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['rag', 1],
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['bcdr', 1],
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]);
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const o = lexicalOverlap(sA, sB, df);
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assert.deepEqual(o.shared, ['azure', 'deployment'], 'shared sorted');
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assert.equal(o.jaccard, 0.5, '2 shared / 4 union');
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// weighted = 1/5 (azure) + 1/2 (deployment) = 0.7
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assert.ok(Math.abs(o.weightedScore - 0.7) < 1e-9, `weightedScore=${o.weightedScore}`);
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});
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test('lexicalOverlap: a high-df token contributes less than a distinctive one', () => {
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const df = new Map([['common', 5], ['rare', 2]]);
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const a = { tokens: new Set(['common', 'rare']) };
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const onlyCommon = lexicalOverlap(a, { tokens: new Set(['common']) }, df);
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const onlyRare = lexicalOverlap(a, { tokens: new Set(['rare']) }, df);
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assert.ok(onlyRare.weightedScore > onlyCommon.weightedScore, 'rare token weighs more');
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});
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// ---------------------------------------------------------------------------
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// pairKey — order-independent, stable
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// ---------------------------------------------------------------------------
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test('pairKey: order-independent', () => {
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assert.equal(pairKey('b', 'a'), pairKey('a', 'b'));
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assert.equal(pairKey('a', 'b'), 'a|b');
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});
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// ---------------------------------------------------------------------------
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// boundaryTensionMatrix — differential check vs independent reducer
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// ---------------------------------------------------------------------------
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test('boundaryTensionMatrix: symmetric, matches independent count, ignores non-skill belongs_to', () => {
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const skills = Object.keys(promptSet).filter((k) => k !== '_meta');
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const m = boundaryTensionMatrix(promptSet);
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// independent reimplementation of symmetric tension
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const skillSet = new Set(skills);
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const expected = {};
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for (const s of skills) {
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for (const e of promptSet[s].out_of_domain || []) {
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const b = e.belongs_to;
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if (!skillSet.has(b)) continue; // controls / out-of-stack are not skills
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const k = pairKey(s, b);
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expected[k] = (expected[k] || 0) + 1;
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}
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}
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for (const [k, v] of Object.entries(expected)) {
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assert.equal(m[k], v, `tension ${k}`);
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}
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// anchored known value tied to current curated set (S11)
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assert.equal(m[pairKey('ms-ai-engineering', 'ms-ai-infrastructure')], 6, 'eng<->infra Azure-deployment boundary = 6');
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});
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// ---------------------------------------------------------------------------
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// computeOverlapFromInputs — full report section
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// ---------------------------------------------------------------------------
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function realDescriptions() {
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// Minimal stand-ins are not enough; use the real five descriptions via the
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// same extractor eval.mjs uses, so the test exercises real data.
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const root = join(__dirname, '..', '..');
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const names = [
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'ms-ai-advisor',
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'ms-ai-engineering',
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'ms-ai-governance',
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'ms-ai-infrastructure',
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'ms-ai-security',
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];
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// lazy import to avoid top-level coupling
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return import('../../scripts/kb-eval/eval.mjs').then((m) => {
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const out = {};
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for (const n of names) {
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const c = readFileSync(join(root, 'skills', n, 'SKILL.md'), 'utf8');
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out[n] = m.extractDescription(m.splitFrontmatter(c).frontmatter);
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}
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return out;
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});
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}
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test('computeOverlapFromInputs: 10 pairs, eng<->infra focusPair, sorted, deterministic', async () => {
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const descs = await realDescriptions();
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const r1 = computeOverlapFromInputs(descs, promptSet);
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const r2 = computeOverlapFromInputs(descs, promptSet);
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// 5 skills -> C(5,2) = 10 unordered pairs
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assert.equal(r1.pairs.length, 10);
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// each pair carries both signals
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for (const p of r1.pairs) {
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assert.ok(Array.isArray(p.pair) && p.pair.length === 2);
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assert.equal(typeof p.boundaryTension, 'number');
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assert.ok(p.lexical && Array.isArray(p.lexical.shared));
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assert.equal(typeof p.combined, 'number');
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}
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// sorted by combined descending
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for (let i = 1; i < r1.pairs.length; i++) {
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assert.ok(r1.pairs[i - 1].combined >= r1.pairs[i].combined, 'pairs sorted desc by combined');
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}
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// focusPair = eng<->infra (operator-designated Azure-deployment boundary)
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assert.ok(r1.focusPair, 'focusPair present');
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assert.deepEqual(
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[...r1.focusPair.pair].sort(),
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['ms-ai-engineering', 'ms-ai-infrastructure'],
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);
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assert.equal(r1.focusPair.boundaryTension, 6);
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// determinism: identical output across runs
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assert.deepEqual(r1, r2);
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});
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