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>

tools/observer-transcript-parser.mjs

@@ -368,6 +368,73 @@ function collectToolResultText(turn) {
   return parts.join('\n');
 }
 
+// Pass 3 — path-pattern classifier (project-brain-factor-analysis-4passes).
+// Returns one of: test / config / spec / norm / data / src / other.
+// Priority order matters (test before src, norm before src, etc).
+export function classifyFilePath(path) {
+  if (!path) return 'other';
+  const p = String(path).replace(/\\/g, '/');
+  const base = p.split('/').pop() || p;
+
+  // 1. tests
+  if (/\.(?:test|spec)\.[a-z0-9]+$/i.test(base)) return 'test';
+  if (/(?:^|\/)(?:tests?|spec)\//i.test(p)) return 'test';
+
+  // 2. normative documents (CLAUDE.md / Pravila / PSR / Tooling / Открытые_вопросы / memory store).
+  if (/(?:^|\/)CLAUDE\.md$/i.test(p)) return 'norm';
+  if (/(?:^|\/)Pravila_raboty_Claude[^/]*\.md$/i.test(p)) return 'norm';
+  if (/(?:^|\/)Plugin_stack_rules[^/]*\.md$/i.test(p)) return 'norm';
+  if (/(?:^|\/)Tooling[^/]*\.md$/i.test(p)) return 'norm';
+  if (/(?:^|\/)Открытые_вопросы[^/]*\.md$/i.test(p)) return 'norm';
+  if (/(?:^|\/)MEMORY\.md$/i.test(p)) return 'norm';
+  if (/\/memory\/[^/]+\.md$/i.test(p)) return 'norm';
+
+  // 3. spec / plan
+  if (/(?:^|\/)docs\/superpowers\/(?:specs|plans)\//i.test(p)) return 'spec';
+
+  // 4. config
+  if (/(?:^|\/)\.env(?:\.|$)/i.test(p)) return 'config';
+  if (/(?:^|\/)(?:package|composer|tsconfig)\.json$/i.test(base)) return 'config';
+  if (/\.config\.[a-z0-9]+$/i.test(base)) return 'config';
+  if (/(?:^|\/)(?:lefthook|\.eslintrc|cspell|stylelint|prettier|pint)[^/]*\.(?:yml|yaml|json|cjs|mjs|js|toml)$/i.test(p)) return 'config';
+
+  // 5. data
+  if (/\.(?:jsonl|csv|sql|sqlite)$/i.test(base)) return 'data';
+
+  // 6. src
+  if (/(?:^|\/)(?:app|tools|resources|src|lib|db\/migrations)\//i.test(p)) return 'src';
+
+  return 'other';
+}
+
+const FILE_TYPE_CATEGORIES = ['src', 'test', 'config', 'spec', 'norm', 'data', 'other'];
+
+export function extractFileTypeDistribution(files) {
+  const dist = Object.fromEntries(FILE_TYPE_CATEGORIES.map((c) => [c, 0]));
+  for (const f of files || []) {
+    dist[classifyFilePath(f)] += 1;
+  }
+  return dist;
+}
+
+// Pass 3 — MCP server fingerprint. tool_use[].name follows
+// `mcp__<server>__<tool>` where <server> may itself contain single underscores
+// (e.g. mcp__plugin_brand-voice_box__authenticate). Non-greedy match stops at
+// the FIRST `__` after the prefix so multi-word server names land whole.
+export function extractMcpServers(turn) {
+  const servers = new Set();
+  for (const e of turn || []) {
+    const content = e && e.message && Array.isArray(e.message.content) ? e.message.content : [];
+    for (const b of content) {
+      if (b && b.type === 'tool_use' && typeof b.name === 'string') {
+        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: (() => {