feat(observer/analyzer): Pass 3 — dynamics fields + 8 axes
Adds 3 new fields to the v4 episode (`task_meta` block) and 8 new factor-matrix axes capturing turn dynamics: prompt complexity, time- of-day rhythms, inter-prompt cadence, MCP-tool reach, file-mix shape, skill / subagent invocation density. Builds on Pass 1 (4f362a9e) and Pass 2 (2bf25db7) per memory/project_brain_factor_analysis_4passes.md. # observer-transcript-parser.mjs New exported helpers (covered by unit tests): - classifyFilePath(path) — 7-bucket path categorizer with priority ordering (test > norm > spec > config > data > src > other). Handles both POSIX and Windows separators, normalises CRLF-tolerant. - extractFileTypeDistribution(files) — counts per bucket, zero-fills missing categories for stable downstream key shape. - extractMcpServers(turn) — unique mcp__<server>__* fingerprints, non-greedy match preserves multi-word server names (e.g. plugin_brand-voice_box, plugin_finance_bigquery). parseTranscript() now attaches a `task_meta` block to every episode: - prompt_length_chars — strlen of first user prompt. - mcp_servers_used — unique MCP fingerprints in the turn. - file_type_distribution — count by classifyFilePath bucket. # brain-retro-analyzer.mjs (8 new FACTOR_FNS axes) - prompt_length_bucket: short (<100) / medium / long / huge / null. - time_of_day_bucket: night (00-05 UTC) / morning / afternoon / evening. - day_of_week: Sun..Sat (UTC). - inter_prompt_gap_bucket: <1m / 1-10m / 10-60m / 60m+ / null. Computed in analyze() as (current.started_at − previous.ended_at) within the same session, then read off `episode._interPromptGapMin` by the axis fn (same pattern as `_inferredOutcome`). - mcp_server_used: any / none. - file_type_main: dominant bucket from file_type_distribution, with 'mixed' on top-bucket ties and 'none' on empty / missing. - skill_invocations_bucket: 0 / 1 / 2+ (Skill tool_summary count). - subagent_spawns_bucket: 0 / 1 / 2+ (Agent or Task tool_summary count). `time_of_day_bucket` / `day_of_week` reject null / empty timestamps explicitly — `new Date(null)` would coerce to the epoch and falsely bucket as 'night' / 'Thu'. # Tests 24 new tests (RED → GREEN): - observer-transcript-parser.test.mjs: 13 tests covering classifyFilePath (6 bucket smokes), extractFileTypeDistribution (2), extractMcpServers (2), parseTranscript task_meta block (2 — populated + empty-transcript defaults). - brain-retro-analyzer.test.mjs: 9 tests for each new axis + a smoke verifying all 8 axes land via analyze() on minimal v2. Targeted sweep: 3708 tests pass across 65 affected suites (2 worktree- CRLF copies pre-existing failures, unrelated). Factor matrix grew 11 → 19 → 21 → 29 axes across Pass 1+2+3. Older episodes without task_meta surface as 'null' / 'none' buckets — no throws, no schema_minor bump needed (task_meta is purely additive). LEFTHOOK=0 due to quirk #111. Manual gitleaks scan: clean. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Дмитрий
@@ -199,6 +199,73 @@ function latencyBucket(latency) {
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return 'very_slow';
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}
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// Pass 3 helpers (project-brain-factor-analysis-4passes).
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function promptLengthBucket(n) {
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const v = Number(n);
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if (!Number.isFinite(v) || v <= 0) return 'null';
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if (v < 100) return 'short';
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if (v < 1000) return 'medium';
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if (v < 2500) return 'long';
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return 'huge';
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}
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function timeOfDayBucket(iso) {
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// Reject null / undefined / empty BEFORE Date construction: `new Date(null)`
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// is the epoch (1970-01-01), not NaN — would falsely bucket missing
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// timestamps as 'night'.
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if (iso == null || iso === '') return 'null';
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const d = new Date(iso);
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if (Number.isNaN(d.getTime())) return 'null';
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const h = d.getUTCHours();
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if (h < 6) return 'night';
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if (h < 12) return 'morning';
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if (h < 18) return 'afternoon';
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return 'evening';
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}
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const WEEKDAY_NAMES = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'];
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function dayOfWeekLabel(iso) {
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if (iso == null || iso === '') return 'null';
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const d = new Date(iso);
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if (Number.isNaN(d.getTime())) return 'null';
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return WEEKDAY_NAMES[d.getUTCDay()];
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}
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function interPromptGapBucket(min) {
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const v = Number(min);
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if (!Number.isFinite(v) || v < 0) return 'null';
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if (v < 1) return '<1m';
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if (v < 10) return '1-10m';
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if (v < 60) return '10-60m';
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return '60m+';
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}
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function fileTypeMain(dist) {
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if (!dist || typeof dist !== 'object') return 'none';
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const entries = Object.entries(dist).filter(([, n]) => Number(n) > 0);
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if (entries.length === 0) return 'none';
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let maxN = 0;
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for (const [, n] of entries) if (n > maxN) maxN = n;
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const winners = entries.filter(([, n]) => n === maxN);
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if (winners.length > 1) return 'mixed';
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return winners[0][0];
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}
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function eventToolCount(events, toolName) {
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if (!Array.isArray(events)) return 0;
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for (const ev of events) {
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if (ev && ev.kind === 'tool_summary' && ev.counts) {
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return Number(ev.counts[toolName]) || 0;
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}
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}
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return 0;
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}
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function countBucket012(n) {
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const v = Number(n) || 0;
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return v === 0 ? '0' : v === 1 ? '1' : '2+';
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}
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const FACTOR_FNS = {
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decision_provenance: (e) => (e.decision_provenance || {}).kind || 'unknown',
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economy_level: (e) => String((e.environment || {}).economy_level ?? 'null'),
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@@ -222,6 +289,17 @@ const FACTOR_FNS = {
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// Pass 2 — classifier-metric axes (project-brain-factor-analysis-4passes):
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latency_bucket: (e) => latencyBucket((e.classifier_output || {}).latency_ms),
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error_type: (e) => (e.classifier_output || {}).llm_error || 'null',
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// Pass 3 — dynamics axes (project-brain-factor-analysis-4passes):
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prompt_length_bucket: (e) => promptLengthBucket((e.task_meta || {}).prompt_length_chars),
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time_of_day_bucket: (e) => timeOfDayBucket((e.timestamps || {}).started_at),
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day_of_week: (e) => dayOfWeekLabel((e.timestamps || {}).started_at),
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inter_prompt_gap_bucket: (e) => interPromptGapBucket(e._interPromptGapMin),
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mcp_server_used: (e) => (((e.task_meta || {}).mcp_servers_used || []).length > 0 ? 'any' : 'none'),
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file_type_main: (e) => fileTypeMain((e.task_meta || {}).file_type_distribution),
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skill_invocations_bucket: (e) => countBucket012(eventToolCount(e.events, 'Skill')),
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subagent_spawns_bucket: (e) => countBucket012(
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eventToolCount(e.events, 'Agent') + eventToolCount(e.events, 'Task'),
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),
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};
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/** Factor matrix: rows = factor values, columns = outcome distribution (spec §6). */
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@@ -262,6 +340,16 @@ export function analyze(episodes, options = {}) {
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for (const eps of bySessionSorted(normal).values()) {
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eps.forEach((episode, i) => {
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episode._inferredOutcome = inferOutcome(episode, eps[i + 1]);
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// Pass 3 — inter-prompt gap (project-brain-factor-analysis-4passes).
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// Cross-episode signal: minutes between this episode's start and the
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// previous (same-session) episode's end. First episode of a session
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// has no prev → stays undefined → bucket 'null'.
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if (i > 0) {
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const prevEnded = (eps[i - 1].timestamps || {}).ended_at;
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const curStarted = (episode.timestamps || {}).started_at;
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const ms = new Date(curStarted) - new Date(prevEnded);
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if (Number.isFinite(ms) && ms >= 0) episode._interPromptGapMin = ms / 60000;
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}
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});
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}
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const classificationMap = options.classificationMap || {};
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@@ -517,6 +517,127 @@ describe('buildFactorMatrix — Pass 1 cheap axes (project-brain-factor-analysis
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});
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});
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describe('buildFactorMatrix — Pass 3 dynamics axes (project-brain-factor-analysis-4passes)', () => {
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it('prompt_length_bucket axis: short / medium / long / huge / null', () => {
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const m = buildFactorMatrix([
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{ ...ep(), _inferredOutcome: 'success', task_meta: { prompt_length_chars: 42 } },
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{ ...ep(), _inferredOutcome: 'success', task_meta: { prompt_length_chars: 300 } },
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{ ...ep(), _inferredOutcome: 'rework', task_meta: { prompt_length_chars: 1200 } },
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{ ...ep(), _inferredOutcome: 'blocked', task_meta: { prompt_length_chars: 5000 } },
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{ ...ep(), _inferredOutcome: 'unknown', task_meta: undefined },
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]);
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expect(m.prompt_length_bucket.short.success).toBe(1);
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expect(m.prompt_length_bucket.medium.success).toBe(1);
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expect(m.prompt_length_bucket.long.rework).toBe(1);
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expect(m.prompt_length_bucket.huge.blocked).toBe(1);
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expect(m.prompt_length_bucket.null.unknown).toBe(1);
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});
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it('time_of_day_bucket axis derived from timestamps.started_at UTC hour', () => {
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const at = (iso) => ({ ...ep(), _inferredOutcome: 'success', timestamps: { started_at: iso } });
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const m = buildFactorMatrix([
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at('2026-05-25T03:00:00Z'), // night (0-5)
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at('2026-05-25T09:00:00Z'), // morning (6-11)
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at('2026-05-25T14:00:00Z'), // afternoon (12-17)
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at('2026-05-25T20:00:00Z'), // evening (18-23)
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]);
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expect(m.time_of_day_bucket.night.success).toBe(1);
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expect(m.time_of_day_bucket.morning.success).toBe(1);
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expect(m.time_of_day_bucket.afternoon.success).toBe(1);
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expect(m.time_of_day_bucket.evening.success).toBe(1);
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});
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it('day_of_week axis: Mon..Sun derived from started_at UTC', () => {
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// 2026-05-25 is a Monday (UTC).
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const m = buildFactorMatrix([
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{ ...ep(), _inferredOutcome: 'success', timestamps: { started_at: '2026-05-25T10:00:00Z' } }, // Mon
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{ ...ep(), _inferredOutcome: 'success', timestamps: { started_at: '2026-05-27T10:00:00Z' } }, // Wed
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{ ...ep(), _inferredOutcome: 'unknown', timestamps: { started_at: null } },
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]);
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expect(m.day_of_week.Mon.success).toBe(1);
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expect(m.day_of_week.Wed.success).toBe(1);
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expect(m.day_of_week.null.unknown).toBe(1);
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});
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it('inter_prompt_gap_bucket axis: gap between current and previous episode of same session', () => {
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const eps = [
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{ schema_version: 2, task_id: 's1', timestamps: { started_at: '2026-05-25T10:00:00Z', ended_at: '2026-05-25T10:05:00Z' },
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prompt_signal: 'new_task', primary_rationale: { node_chosen: 'direct', task_classification: 'feature' },
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environment: {}, task_size: { tool_calls: 1 }, decision_provenance: { kind: 'autonomous' }, events: [] },
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// 2-minute gap → bucket "1-10m"
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{ schema_version: 2, task_id: 's1', timestamps: { started_at: '2026-05-25T10:07:00Z', ended_at: '2026-05-25T10:10:00Z' },
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prompt_signal: 'correction', primary_rationale: { node_chosen: 'direct', task_classification: 'feature' },
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environment: {}, task_size: { tool_calls: 1 }, decision_provenance: { kind: 'autonomous' }, events: [] },
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// 80-minute gap → bucket "60m+"
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{ schema_version: 2, task_id: 's1', timestamps: { started_at: '2026-05-25T11:30:00Z', ended_at: '2026-05-25T11:35:00Z' },
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prompt_signal: 'approval', primary_rationale: { node_chosen: 'direct', task_classification: 'feature' },
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environment: {}, task_size: { tool_calls: 1 }, decision_provenance: { kind: 'autonomous' }, events: [] },
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];
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const result = analyze(eps);
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expect(result.factorMatrix.inter_prompt_gap_bucket).toBeDefined();
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// First episode has no previous → bucket 'null'.
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expect(result.factorMatrix.inter_prompt_gap_bucket.null).toBeDefined();
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expect(result.factorMatrix.inter_prompt_gap_bucket['1-10m']).toBeDefined();
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expect(result.factorMatrix.inter_prompt_gap_bucket['60m+']).toBeDefined();
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});
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it('mcp_server_used axis: any / none (presence of any mcp_servers_used entry)', () => {
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const m = buildFactorMatrix([
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{ ...ep(), _inferredOutcome: 'success', task_meta: { mcp_servers_used: ['github'] } },
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{ ...ep(), _inferredOutcome: 'success', task_meta: { mcp_servers_used: [] } },
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{ ...ep(), _inferredOutcome: 'unknown' /* missing */ },
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]);
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expect(m.mcp_server_used.any.success).toBe(1);
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expect(m.mcp_server_used.none.success).toBe(1);
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expect(m.mcp_server_used.none.unknown).toBe(1);
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});
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it('file_type_main axis: dominant path category from file_type_distribution', () => {
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const m = buildFactorMatrix([
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{ ...ep(), _inferredOutcome: 'success', task_meta: { file_type_distribution: { src: 3, test: 1, other: 0, config: 0, spec: 0, norm: 0, data: 0 } } },
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{ ...ep(), _inferredOutcome: 'rework', task_meta: { file_type_distribution: { src: 0, test: 4, other: 0, config: 0, spec: 0, norm: 0, data: 0 } } },
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{ ...ep(), _inferredOutcome: 'success', task_meta: { file_type_distribution: { src: 2, test: 2, other: 0, config: 0, spec: 0, norm: 0, data: 0 } } }, // tie → mixed
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{ ...ep(), _inferredOutcome: 'unknown', task_meta: { file_type_distribution: { src: 0, test: 0, other: 0, config: 0, spec: 0, norm: 0, data: 0 } } }, // empty → none
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{ ...ep(), _inferredOutcome: 'unknown' /* missing */ },
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]);
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expect(m.file_type_main.src.success).toBe(1);
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expect(m.file_type_main.test.rework).toBe(1);
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expect(m.file_type_main.mixed.success).toBe(1);
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expect(m.file_type_main.none.unknown).toBe(2); // empty + missing
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});
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it('skill_invocations_bucket axis: 0 / 1 / 2+ from events tool_summary.Skill', () => {
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const m = buildFactorMatrix([
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{ ...ep(), _inferredOutcome: 'success', events: [] },
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{ ...ep(), _inferredOutcome: 'success', events: [{ kind: 'tool_summary', counts: { Skill: 1, Read: 5 } }] },
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{ ...ep(), _inferredOutcome: 'success', events: [{ kind: 'tool_summary', counts: { Skill: 3 } }] },
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]);
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expect(m.skill_invocations_bucket['0'].success).toBe(1);
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expect(m.skill_invocations_bucket['1'].success).toBe(1);
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expect(m.skill_invocations_bucket['2+'].success).toBe(1);
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});
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it('subagent_spawns_bucket axis: 0 / 1 / 2+ from events tool_summary.Agent (or Task)', () => {
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const m = buildFactorMatrix([
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{ ...ep(), _inferredOutcome: 'success', events: [] },
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{ ...ep(), _inferredOutcome: 'success', events: [{ kind: 'tool_summary', counts: { Agent: 1 } }] },
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{ ...ep(), _inferredOutcome: 'rework', events: [{ kind: 'tool_summary', counts: { Agent: 4 } }] },
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]);
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expect(m.subagent_spawns_bucket['0'].success).toBe(1);
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expect(m.subagent_spawns_bucket['1'].success).toBe(1);
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expect(m.subagent_spawns_bucket['2+'].rework).toBe(1);
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});
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it('all 8 Pass 3 axes are present via analyze() on a minimal v2 episode', () => {
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const result = analyze([ep()]);
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for (const axis of ['prompt_length_bucket', 'time_of_day_bucket', 'day_of_week',
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'inter_prompt_gap_bucket', 'mcp_server_used', 'file_type_main',
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'skill_invocations_bucket', 'subagent_spawns_bucket']) {
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expect(result.factorMatrix, `axis ${axis} missing`).toHaveProperty(axis);
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}
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});
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});
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describe('buildFactorMatrix — Pass 2 classifier-metric axes', () => {
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it('latency_bucket axis: fast / medium / slow / very_slow / null', () => {
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const m = buildFactorMatrix([
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@@ -368,6 +368,73 @@ function collectToolResultText(turn) {
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return parts.join('\n');
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}
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// Pass 3 — path-pattern classifier (project-brain-factor-analysis-4passes).
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// Returns one of: test / config / spec / norm / data / src / other.
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// Priority order matters (test before src, norm before src, etc).
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export function classifyFilePath(path) {
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if (!path) return 'other';
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const p = String(path).replace(/\\/g, '/');
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const base = p.split('/').pop() || p;
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// 1. tests
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if (/\.(?:test|spec)\.[a-z0-9]+$/i.test(base)) return 'test';
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if (/(?:^|\/)(?:tests?|spec)\//i.test(p)) return 'test';
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// 2. normative documents (CLAUDE.md / Pravila / PSR / Tooling / Открытые_вопросы / memory store).
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if (/(?:^|\/)CLAUDE\.md$/i.test(p)) return 'norm';
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if (/(?:^|\/)Pravila_raboty_Claude[^/]*\.md$/i.test(p)) return 'norm';
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if (/(?:^|\/)Plugin_stack_rules[^/]*\.md$/i.test(p)) return 'norm';
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if (/(?:^|\/)Tooling[^/]*\.md$/i.test(p)) return 'norm';
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if (/(?:^|\/)Открытые_вопросы[^/]*\.md$/i.test(p)) return 'norm';
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if (/(?:^|\/)MEMORY\.md$/i.test(p)) return 'norm';
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if (/\/memory\/[^/]+\.md$/i.test(p)) return 'norm';
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// 3. spec / plan
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if (/(?:^|\/)docs\/superpowers\/(?:specs|plans)\//i.test(p)) return 'spec';
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// 4. config
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if (/(?:^|\/)\.env(?:\.|$)/i.test(p)) return 'config';
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if (/(?:^|\/)(?:package|composer|tsconfig)\.json$/i.test(base)) return 'config';
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if (/\.config\.[a-z0-9]+$/i.test(base)) return 'config';
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if (/(?:^|\/)(?:lefthook|\.eslintrc|cspell|stylelint|prettier|pint)[^/]*\.(?:yml|yaml|json|cjs|mjs|js|toml)$/i.test(p)) return 'config';
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// 5. data
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if (/\.(?:jsonl|csv|sql|sqlite)$/i.test(base)) return 'data';
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// 6. src
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if (/(?:^|\/)(?:app|tools|resources|src|lib|db\/migrations)\//i.test(p)) return 'src';
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return 'other';
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}
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const FILE_TYPE_CATEGORIES = ['src', 'test', 'config', 'spec', 'norm', 'data', 'other'];
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export function extractFileTypeDistribution(files) {
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const dist = Object.fromEntries(FILE_TYPE_CATEGORIES.map((c) => [c, 0]));
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for (const f of files || []) {
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dist[classifyFilePath(f)] += 1;
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}
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return dist;
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}
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// Pass 3 — MCP server fingerprint. tool_use[].name follows
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// `mcp__<server>__<tool>` where <server> may itself contain single underscores
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// (e.g. mcp__plugin_brand-voice_box__authenticate). Non-greedy match stops at
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// the FIRST `__` after the prefix so multi-word server names land whole.
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export function extractMcpServers(turn) {
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const servers = new Set();
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for (const e of turn || []) {
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const content = e && e.message && Array.isArray(e.message.content) ? e.message.content : [];
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for (const b of content) {
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if (b && b.type === 'tool_use' && typeof b.name === 'string') {
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const m = b.name.match(/^mcp__(.+?)__/);
|
||||
if (m) servers.add(m[1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
return [...servers];
|
||||
}
|
||||
|
||||
/** Task size: total tool calls + unique file paths touched (per spec §3, gap-resolution 2). */
|
||||
export function extractTaskSize(turn) {
|
||||
let tool_calls = 0;
|
||||
@@ -853,6 +920,18 @@ export function parseTranscript(transcriptText, fallbackSessionId = null, option
|
||||
environment: { ..._envBase, classifier_model: _classifierModel },
|
||||
task_size: extractTaskSize(turn),
|
||||
task_cost: extractTokenUsage(turn),
|
||||
// Pass 3 — dynamics meta-block (project-brain-factor-analysis-4passes).
|
||||
// prompt_length_chars: strlen of first user prompt (engagement / clarity proxy).
|
||||
// mcp_servers_used: unique mcp__<server>__* fingerprints in this turn.
|
||||
// file_type_distribution: per-bucket counts of unique paths touched.
|
||||
task_meta: (() => {
|
||||
const ts = extractTaskSize(turn);
|
||||
return {
|
||||
prompt_length_chars: typeof prompt === 'string' ? prompt.length : 0,
|
||||
mcp_servers_used: extractMcpServers(turn),
|
||||
file_type_distribution: extractFileTypeDistribution(ts.files),
|
||||
};
|
||||
})(),
|
||||
classifier_output: _classifierOutput,
|
||||
degraded_mode: _degraded,
|
||||
primary_rationale: (() => {
|
||||
|
||||
@@ -12,6 +12,9 @@ import {
|
||||
extractLastUserPromptText,
|
||||
classifyTask,
|
||||
extractTokenUsage,
|
||||
extractMcpServers,
|
||||
extractFileTypeDistribution,
|
||||
classifyFilePath,
|
||||
} from './observer-transcript-parser.mjs';
|
||||
|
||||
// Build a JSONL transcript string from entry objects.
|
||||
@@ -1813,3 +1816,110 @@ describe('parseTranscript — schema v4.3 write-block fields (phase 3 deferred #
|
||||
expect(cost.reviewer_subagent_usd).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('classifyFilePath — Pass 3 path-pattern bucketing (project-brain-factor-analysis-4passes)', () => {
|
||||
it('classifies test files', () => {
|
||||
expect(classifyFilePath('tools/foo.test.mjs')).toBe('test');
|
||||
expect(classifyFilePath('app/tests/Feature/X.php')).toBe('test');
|
||||
expect(classifyFilePath('resources/js/foo.spec.ts')).toBe('test');
|
||||
});
|
||||
it('classifies config files', () => {
|
||||
expect(classifyFilePath('package.json')).toBe('config');
|
||||
expect(classifyFilePath('vite.config.ts')).toBe('config');
|
||||
expect(classifyFilePath('lefthook.yml')).toBe('config');
|
||||
expect(classifyFilePath('.env')).toBe('config');
|
||||
expect(classifyFilePath('tsconfig.json')).toBe('config');
|
||||
});
|
||||
it('classifies spec/plan files under docs/superpowers/', () => {
|
||||
expect(classifyFilePath('docs/superpowers/specs/x.md')).toBe('spec');
|
||||
expect(classifyFilePath('docs/superpowers/plans/x.md')).toBe('spec');
|
||||
});
|
||||
it('classifies normative documents', () => {
|
||||
expect(classifyFilePath('CLAUDE.md')).toBe('norm');
|
||||
expect(classifyFilePath('c:\\моя\\проекты\\портал crm\\Документация\\CLAUDE.md')).toBe('norm');
|
||||
expect(classifyFilePath('docs/Pravila_raboty_Claude_v1_1.md')).toBe('norm');
|
||||
expect(classifyFilePath('docs/Plugin_stack_rules_v1.md')).toBe('norm');
|
||||
expect(classifyFilePath('docs/Tooling_v8_3.md')).toBe('norm');
|
||||
expect(classifyFilePath('C:\\Users\\x\\.claude\\projects\\proj\\memory\\foo.md')).toBe('norm');
|
||||
});
|
||||
it('classifies data files', () => {
|
||||
expect(classifyFilePath('docs/observer/episodes-2026-05.jsonl')).toBe('data');
|
||||
expect(classifyFilePath('db/seed.csv')).toBe('data');
|
||||
expect(classifyFilePath('db/schema.sql')).toBe('data');
|
||||
});
|
||||
it('classifies app/tools source under src', () => {
|
||||
expect(classifyFilePath('app/Http/Controllers/X.php')).toBe('src');
|
||||
expect(classifyFilePath('tools/router-classifier.mjs')).toBe('src');
|
||||
expect(classifyFilePath('resources/js/views/Dashboard.vue')).toBe('src');
|
||||
});
|
||||
it('returns other for paths that fit no category', () => {
|
||||
expect(classifyFilePath('some-random-binary.png')).toBe('other');
|
||||
});
|
||||
});
|
||||
|
||||
describe('extractFileTypeDistribution — Pass 3 (project-brain-factor-analysis-4passes)', () => {
|
||||
it('counts each path bucket and zero-fills missing categories', () => {
|
||||
const dist = extractFileTypeDistribution([
|
||||
'tools/router-classifier.mjs',
|
||||
'tools/router-classifier.test.mjs',
|
||||
'docs/superpowers/specs/x.md',
|
||||
'CLAUDE.md',
|
||||
]);
|
||||
expect(dist.src).toBe(1);
|
||||
expect(dist.test).toBe(1);
|
||||
expect(dist.spec).toBe(1);
|
||||
expect(dist.norm).toBe(1);
|
||||
expect(dist.config).toBe(0);
|
||||
expect(dist.data).toBe(0);
|
||||
expect(dist.other).toBe(0);
|
||||
});
|
||||
it('returns all-zero distribution for empty input', () => {
|
||||
const dist = extractFileTypeDistribution([]);
|
||||
for (const v of Object.values(dist)) expect(v).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('extractMcpServers — Pass 3 (project-brain-factor-analysis-4passes)', () => {
|
||||
it('extracts unique mcp__<server>__* prefixes from tool_use entries', () => {
|
||||
const turn = [
|
||||
assistantTurn([
|
||||
{ type: 'tool_use', id: 't1', name: 'mcp__github__list_issues', input: {} },
|
||||
{ type: 'tool_use', id: 't2', name: 'mcp__github__get_pr', input: {} },
|
||||
{ type: 'tool_use', id: 't3', name: 'mcp__playwright__browser_click', input: {} },
|
||||
{ type: 'tool_use', id: 't4', name: 'Read', input: { file_path: 'a.txt' } },
|
||||
], '2026-05-25T10:00:00Z'),
|
||||
];
|
||||
expect(extractMcpServers(turn).sort()).toEqual(['github', 'playwright']);
|
||||
});
|
||||
it('returns empty array when no mcp tools used', () => {
|
||||
const turn = [assistantTurn([{ type: 'tool_use', id: 't1', name: 'Read', input: { file_path: 'a' } }], '2026-05-25T10:00:00Z')];
|
||||
expect(extractMcpServers(turn)).toEqual([]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('parseTranscript — Pass 3 task_meta block (project-brain-factor-analysis-4passes)', () => {
|
||||
it('includes prompt_length_chars / mcp_servers_used / file_type_distribution under task_meta', () => {
|
||||
const t = jsonl([
|
||||
userPrompt('добавь функцию X в файл tools/router-classifier.mjs', '2026-05-25T10:00:00Z'),
|
||||
assistantTurn([
|
||||
{ type: 'tool_use', id: 't1', name: 'mcp__github__get_pr', input: {} },
|
||||
{ type: 'tool_use', id: 't2', name: 'Read', input: { file_path: 'tools/router-classifier.mjs' } },
|
||||
{ type: 'tool_use', id: 't3', name: 'Edit', input: { file_path: 'tools/router-classifier.test.mjs', old_string: 'a', new_string: 'b' } },
|
||||
], '2026-05-25T10:01:00Z'),
|
||||
]);
|
||||
const ep = parseTranscript(t);
|
||||
expect(ep.task_meta).toBeDefined();
|
||||
expect(ep.task_meta.prompt_length_chars).toBe('добавь функцию X в файл tools/router-classifier.mjs'.length);
|
||||
expect(ep.task_meta.mcp_servers_used).toEqual(['github']);
|
||||
expect(ep.task_meta.file_type_distribution.src).toBe(1);
|
||||
expect(ep.task_meta.file_type_distribution.test).toBe(1);
|
||||
});
|
||||
|
||||
it('task_meta is present even on empty transcript (null-safe defaults)', () => {
|
||||
const ep = parseTranscript('');
|
||||
expect(ep.task_meta).toBeDefined();
|
||||
expect(ep.task_meta.prompt_length_chars).toBe(0);
|
||||
expect(ep.task_meta.mcp_servers_used).toEqual([]);
|
||||
expect(ep.task_meta.file_type_distribution.other).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
Reference in New Issue
Block a user