Дмитрий
980 lines
40 KiB
JavaScript
980 lines
40 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Transcript parser for the brain governance observer.
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* Deterministically extracts episode fields from a Claude Code session
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* transcript (JSONL). No LLM — pure parsing.
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*
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* Scope: the last turn (from the last real user prompt to end of file) —
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* one episode == one prompt→response cycle.
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*
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* Reasoning fields (triggers_matched / candidates_considered /
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* boundaries_applied) are NOT recoverable from a transcript and stay [];
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* their capture is a separate design question (ADR-011 follow-up).
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*
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* Security Guidance #40: pure parsing — no exec/execSync.
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* Per ADR-011 §6 + spec v1.1 §5.2.1.
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*/
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import { readFileSync } from 'node:fs';
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import { fileURLToPath } from 'node:url';
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import { dirname, join } from 'node:path';
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import { readRouterState, extractRouterFields, extractClassifierOutput } from './observer-state-enricher.mjs';
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import { CLASSIFIER_MODEL } from './router-config.mjs';
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import { homedir } from 'node:os';
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import { detectChoiceProvenance, detectAskUserQuestionChoice } from './observer-choice-detector.mjs';
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import { loadChainMap, chainsFor } from './observer-chain-detector.mjs';
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import { buildHookMap, resolveScriptCounts } from './observer-hook-resolver.mjs';
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// recommendNode / buildClassificationMap / buildDormancyMap были использованы
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// для слепого fallback на heuristic recommended_node — убрано 2026-05-26
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// (brain-retro #6 follow-up). Импорты сняты как dead code.
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import { loadRegistry } from './registry-load.mjs';
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import { extractV4Signals } from './observer-v4-signals.mjs';
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import { JUDGE_PER_CALL_USD } from './cost-pricing.mjs';
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const __dirname = dirname(fileURLToPath(import.meta.url));
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let CHAIN_MAP = null;
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try {
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CHAIN_MAP = loadChainMap();
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} catch {
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CHAIN_MAP = new Map(); // битый/отсутствующий JSON -> chainsFor вернёт null, observer не падает
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}
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let HOOK_MAP = null;
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function getHookMap() {
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if (HOOK_MAP) return HOOK_MAP;
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const read = (p) => { try { return JSON.parse(readFileSync(p, 'utf-8')); } catch { return {}; } };
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HOOK_MAP = buildHookMap(
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read(join(__dirname, '..', '.claude', 'settings.json')),
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read(join(homedir(), '.claude', 'settings.json'))
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);
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return HOOK_MAP;
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}
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/**
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* Whitelist of router-node names. Used by extractCandidates to filter out
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* free-form prose bullets (analysis text, procedure steps, code snippets) that
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* the regex on its own would happily slurp into candidates_considered.
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* Sources, in order:
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* - tools/observer-known-nodes.txt — bare names (brainstorming, ccpm, …)
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* - tools/observer-chain-map.json keys — incl. plugin:skill form
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* - sentinel "direct" (no-skill marker used by node_chosen)
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* Tooling IDs (#NN) and arbitrary plugin:skill forms pass via regex below.
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*/
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const KNOWN_NODES = (() => {
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const set = new Set(['direct']);
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try {
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const txt = readFileSync(join(__dirname, 'observer-known-nodes.txt'), 'utf8');
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for (const line of txt.split('\n')) {
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const t = line.replace(/#.*$/, '').trim();
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if (t) set.add(t);
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}
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} catch {
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// file missing in some test sandboxes — fall back to chain-map keys only
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}
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if (CHAIN_MAP) for (const node of CHAIN_MAP.keys()) set.add(node);
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return set;
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})();
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const TOOLING_ID_RE = /^#\d+$/;
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const NAMESPACED_SKILL_RE = /^[a-z][a-z0-9-]*:[a-z][a-z0-9-]*(?::[a-z][a-z0-9-]*)?$/;
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/**
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* Strip lightweight markdown wrappers (bold, italic, code, trailing punctuation)
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* before testing against the whitelist. Conservative — we accept that some
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* weirdly-formatted node names slip through, but free-form prose bullets do not.
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*/
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function normalizeCandidate(s) {
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let t = String(s || '').trim();
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// peel outer markdown markers: **x**, *x*, `x`, _x_
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while (
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(t.startsWith('**') && t.endsWith('**') && t.length > 4) ||
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(t.startsWith('`') && t.endsWith('`') && t.length > 2) ||
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(t.startsWith('*') && t.endsWith('*') && t.length > 2) ||
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(t.startsWith('_') && t.endsWith('_') && t.length > 2)
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) {
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if (t.startsWith('**')) t = t.slice(2, -2).trim();
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else t = t.slice(1, -1).trim();
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}
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// drop trailing punctuation (commas, periods, em-dashes) that lists often leave
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t = t.replace(/[.,;:!?—–-]+$/u, '').trim();
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return t;
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}
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function isKnownNode(raw) {
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const t = normalizeCandidate(raw);
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if (!t) return false;
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if (KNOWN_NODES.has(t)) return true;
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if (TOOLING_ID_RE.test(t)) return true;
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// namespaced plugin:skill we haven't seen yet — accept if shape matches and
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// contains no whitespace (a free-form bullet with a colon in prose won't pass).
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if (NAMESPACED_SKILL_RE.test(t)) return true;
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return false;
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}
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const SUPERPOWERS_PREFIX = 'superpowers:';
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function parseLines(text) {
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const entries = [];
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let broken = 0;
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let total = 0;
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// quirk #101 root fix: Claude Code's transcript file accumulates duplicated
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// context-rebuild snapshots — the same entry is re-printed with the SAME
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// `uuid`. Without dedup, session_turn / task_size / events double-count and
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// session_turn becomes non-monotonic across episodes parsed at different
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// file-growth states. Keep the first occurrence per uuid; entries without a
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// uuid (synthetic test fixtures) pass through unchanged.
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const seenUuid = new Set();
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for (const line of String(text || '').split('\n')) {
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const trimmed = line.trim();
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if (!trimmed) continue;
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total += 1;
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let e;
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try {
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e = JSON.parse(trimmed);
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} catch {
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broken += 1; // broken line — counted for parse_gap, never thrown
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continue;
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}
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if (e && e.uuid) {
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if (seenUuid.has(e.uuid)) continue;
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seenUuid.add(e.uuid);
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}
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entries.push(e);
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}
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return { entries, broken, total };
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}
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// Synthetic user-role messages — NOT genuine prompts, must not be turn boundaries.
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// Skill invocation content, local slash-command output/invocation, interrupt markers
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// are recorded with role:'user' but carry no UserPromptSubmit hook context.
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const SYNTHETIC_PROMPT_MARKERS = [
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'Base directory for this skill:',
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'<local-command-stdout>',
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'<local-command-caveat>',
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'<command-name>',
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'[Request interrupted by user]',
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];
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function isSyntheticPrompt(text) {
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const t = String(text || '').trimStart();
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return SYNTHETIC_PROMPT_MARKERS.some((m) => t.startsWith(m));
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}
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// A genuine user prompt (turn boundary) — not a tool_result carrier nor a
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// synthetic skill/command/interrupt message.
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function isRealUserPrompt(entry) {
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const msg = entry && entry.message;
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if (!msg || msg.role !== 'user') return false;
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const c = msg.content;
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if (typeof c === 'string') {
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return c.trim().length > 0 && !isSyntheticPrompt(c);
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}
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if (Array.isArray(c)) {
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const hasToolResult = c.some((b) => b && b.type === 'tool_result');
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const hasText = c.some((b) => b && b.type === 'text');
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if (!hasText || hasToolResult) return false;
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const text = c
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.filter((b) => b && b.type === 'text')
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.map((b) => b.text || '')
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.join(' ');
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return !isSyntheticPrompt(text);
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}
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return false;
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}
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function findTurnStart(entries) {
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for (let i = entries.length - 1; i >= 0; i--) {
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if (isRealUserPrompt(entries[i])) return i;
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}
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return 0;
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}
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function stripSystemReminders(text) {
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return String(text || '').replace(/<system-reminder>[\s\S]*?<\/system-reminder>/g, '');
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}
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function promptText(entry) {
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const c = entry && entry.message && entry.message.content;
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if (typeof c === 'string') return stripSystemReminders(c);
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if (Array.isArray(c)) {
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const joined = c
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.filter((b) => b && b.type === 'text')
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.map((b) => b.text || '')
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.join(' ');
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return stripSystemReminders(joined);
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}
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return '';
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}
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export function classifyTask(text) {
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const t = String(text || '').toLowerCase();
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if (/обнови эталон|sync memory|обнови (?:память|memory|memory\.md)/.test(t)) return 'memory-sync';
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if (/обнови claude|правк[аи] pravila|update pravila|обнови psr|обнови tooling|нормативка/.test(t)) return 'regulatory-bump';
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if (/план|plan\b|спроектируй|design\b|brainstorm|обсудим/.test(t)) return 'planning';
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if (/\bpush\b|\bmerge\b|\bdeploy\b|\brelease\b|релиз|тегни/.test(t)) return 'release';
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if (/рефактор|refactor/.test(t)) return 'refactor';
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if (/баг|bug|почини|исправ|fix\b|сломан|broken/.test(t)) return 'bugfix';
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if (/фич|feature|добав|implement|реализ|создай|create|новый|new /.test(t)) return 'feature';
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if (/докум|readme|\bdocs?\b/.test(t)) return 'docs';
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if (/проанализ|анализ|оцени|review|examine|разбор|посмотри что/.test(t)) return 'analysis';
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if (/убери|удали|почисть|cleanup|очисти|drop\s/.test(t)) return 'cleanup';
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if (/^\s*статус\b|\bstatus\b|проверь состоян|health/.test(t)) return 'monitoring';
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if (/\?|как |что |почему|зачем|why|how |what /.test(t)) return 'question';
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return 'other';
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}
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function collectToolUse(entries) {
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const skills = [];
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const counts = {};
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const errors = [];
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const idToTool = {};
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// First pass — build id→tool name map (tool_results may reference tools across messages)
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for (const e of entries) {
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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') idToTool[b.id] = b.name || 'unknown';
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}
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}
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// Second pass — accumulate counts + per-error attribution
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for (const e of entries) {
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const content = e && e.message && Array.isArray(e.message.content) ? e.message.content : [];
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for (const block of content) {
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if (!block || typeof block !== 'object') continue;
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if (block.type === 'tool_use') {
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const name = block.name || 'unknown';
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counts[name] = (counts[name] || 0) + 1;
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if (name === 'Skill') {
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skills.push((block.input && block.input.skill) || 'unknown');
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}
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} else if (block.type === 'tool_result' && block.is_error === true) {
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const tool = idToTool[block.tool_use_id] || 'unknown';
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const c = block.content;
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const text = typeof c === 'string' ? c
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: (Array.isArray(c) ? c.map((b) => (b && typeof b.text === 'string') ? b.text : '').join(' ') : '');
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errors.push({ tool, summary: text.slice(0, 80) });
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}
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}
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}
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return { skills, counts, errors };
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}
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const FILE_TOOLS = new Set(['Read', 'Edit', 'Write', 'MultiEdit', 'NotebookEdit']);
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/**
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* Deterministic environment factors for the turn that starts at turnStartIdx.
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* economy_level / parallel_session are scanned from the stringified turn;
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* model / post_compaction / session_turn from structural fields.
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*/
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export function extractEnvironment(allEntries, turnStartIdx) {
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const turn = allEntries.slice(turnStartIdx);
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const rawTurn = JSON.stringify(turn);
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const econ = rawTurn.match(/=== ECONOMY MODE:\s*(\d+)\s*%/);
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const economy_level = econ ? Number(econ[1]) : null;
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let model = null;
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for (const e of turn) {
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if (e && e.message && e.message.model) {
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model = e.message.model;
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break;
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}
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}
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// The transcript file accumulates duplicated context-rebuild snapshots
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// (repeated isCompactSummary entries — see feedback_environment quirk #101).
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// Counting prompts from i=0 inflates session_turn with those dupes. Count
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// from the LAST compaction before the turn: session_turn = real prompts
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// since it, which is monotonic ("turns since last compaction").
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let lastCompactIdx = -1;
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for (let i = 0; i < turnStartIdx && i < allEntries.length; i++) {
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if (allEntries[i] && allEntries[i].isCompactSummary === true) lastCompactIdx = i;
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}
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const post_compaction = lastCompactIdx >= 0;
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let session_turn = 0;
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for (let i = lastCompactIdx + 1; i <= turnStartIdx && i < allEntries.length; i++) {
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if (isRealUserPrompt(allEntries[i])) session_turn += 1;
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}
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// Only strong collision evidence — a bare mention of "parallel sessions" is
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// not a signal (best-effort per spec R2; prefer false-negative over false-positive).
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// Scope NARROWED to tool_result content (real command output / Bash stderr): prose
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// mentions in user prompts / assistant text — including analysis text that
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// references collision phrases — must not trigger. Fixes live FP (episode line 20).
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const parallel_session =
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/чужой staged|foreign git index|index\.lock|another git process/i.test(collectToolResultText(turn))
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|| hasPreFlightFetch(turn);
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return { economy_level, model, post_compaction, session_turn, parallel_session };
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}
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/**
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* Pravila §15.2 pre-flight signal (Task 13 PIVOT): Bash-команда turn'а
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* содержит `git fetch ... && git log HEAD..origin/main ...` — это hard-rule
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* pre-flight sync перед правкой нормативки в параллельных сессиях. Сильный
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* сигнал «заказчик ожидает параллельных сессий», аддитивный к F1 collision
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* detector (parallel_session). Не overwrite — OR-clause.
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*/
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function hasPreFlightFetch(turn) {
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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' && b.name === 'Bash' && b.input) {
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const cmd = String(b.input.command || '');
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if (/git\s+fetch[^|&;]*&&[^|&;]*git\s+log\s+HEAD\.\.origin\//i.test(cmd)) return true;
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}
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}
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}
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return false;
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}
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/**
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* Collect text content from tool_result blocks in the turn — the only surface
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* trusted for parallel_session collision evidence (see extractEnvironment).
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* Supports both string content and the structured array form
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* (`content: [{ type: 'text', text }]`).
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*/
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function collectToolResultText(turn) {
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const parts = [];
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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_result') continue;
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const c = b.content;
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if (typeof c === 'string') {
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parts.push(c);
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} else if (Array.isArray(c)) {
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for (const sub of c) {
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if (sub && typeof sub.text === 'string') parts.push(sub.text);
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}
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}
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}
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}
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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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//
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// Greenfield #3-observer: project-normative stems are config-driven (design
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// 2026-06-16-greenfield-regex-names-config-design §5). Default = the three Лидерра
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// stems → byte-identical to the previous hardcoded patterns; a greenfield project
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// supplies its own via loadConfig().normative_files → docStem (wired in observer-stop-hook).
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export const DEFAULT_NORMATIVE_STEMS = Object.freeze(['Pravila_raboty_Claude', 'Plugin_stack_rules', 'Tooling']);
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const _escRe = (s) => String(s).replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
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export function classifyFilePath(path, normativeStems = DEFAULT_NORMATIVE_STEMS) {
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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]+$/i.test(base)) return 'test';
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if (/(?:^|\/)(?:tests?|spec)\//i.test(p)) return 'test';
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// 2. normative documents. Universal docs (CLAUDE.md / Открытые_вопросы / MEMORY.md / memory
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// store) stay hardcoded; project-normative docs match config stems (default = Лидерра quintet).
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if (/(?:^|\/)CLAUDE\.md$/i.test(p)) return 'norm';
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for (const stem of (normativeStems || [])) {
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if (stem && new RegExp(`(?:^|/)${_escRe(stem)}[^/]*\\.md$`, 'i').test(p)) return 'norm';
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}
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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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|
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export function extractFileTypeDistribution(files, normativeStems = DEFAULT_NORMATIVE_STEMS) {
|
||
const dist = Object.fromEntries(FILE_TYPE_CATEGORIES.map((c) => [c, 0]));
|
||
for (const f of files || []) {
|
||
dist[classifyFilePath(f, normativeStems)] += 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;
|
||
const files = 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') {
|
||
tool_calls += 1;
|
||
if (FILE_TOOLS.has(b.name) && b.input) {
|
||
const p = b.input.file_path || b.input.notebook_path;
|
||
if (p) files.add(String(p));
|
||
}
|
||
}
|
||
}
|
||
}
|
||
return { tool_calls, files_touched: files.size, files: [...files] };
|
||
}
|
||
|
||
/**
|
||
* Token-usage aggregation across all assistant messages in the turn.
|
||
*
|
||
* DESIGN: returns zero-filled object (NOT null) when no `usage` data was
|
||
* captured. Consumers cannot currently distinguish "actually 0 tokens" from
|
||
* "no usage data" — accepted trade-off because (a) every assistant message
|
||
* in real Claude Code transcripts has `usage` (verified B1 brain-retro
|
||
* 2026-05-20: 6265/6265 messages with usage, 0 partial-stream), and
|
||
* (b) `task_cost` is not yet read by analyzer/STATUS.md, so the semantic
|
||
* gap is a future-only concern. Re-evaluate when factor matrix adds cost.
|
||
*
|
||
* Captures: 4 base token fields + `iterations` (extended-thinking detector)
|
||
* + `server_tool_use.{web_search,web_fetch}_requests` counts.
|
||
* Other usage fields (cache_creation object, inference_geo, service_tier,
|
||
* speed) — out-of-scope for current analyzer.
|
||
*
|
||
* Defensive: skips entries where `usage` is not a plain object (handles
|
||
* malformed transcript edge cases like `"usage": 42`).
|
||
*/
|
||
// Normalize `usage.iterations` to a count.
|
||
// Claude Code transcripts may emit it as: a number (legacy / no extended-thinking),
|
||
// an array of step-objects (extended-thinking turns), or a plain object map.
|
||
// Coerce to a number; non-finite / unknown → 0. Prevents "0[object Object]…"
|
||
// string concatenation that previously poisoned task_cost.iterations.
|
||
function iterationsCount(v) {
|
||
if (typeof v === 'number' && Number.isFinite(v)) return v;
|
||
if (Array.isArray(v)) return v.length;
|
||
if (v && typeof v === 'object') return Object.keys(v).length;
|
||
return 0;
|
||
}
|
||
|
||
export function extractTokenUsage(turn) {
|
||
let input = 0, output = 0, cache_read = 0, cache_creation = 0;
|
||
let web_search = 0, web_fetch = 0, iterations = 0;
|
||
for (const e of turn || []) {
|
||
const u = e && e.message && e.message.usage;
|
||
if (!u || typeof u !== 'object') continue;
|
||
input += u.input_tokens || 0;
|
||
output += u.output_tokens || 0;
|
||
cache_read += u.cache_read_input_tokens || 0;
|
||
cache_creation += u.cache_creation_input_tokens || 0;
|
||
iterations += iterationsCount(u.iterations);
|
||
if (u.server_tool_use) {
|
||
web_search += u.server_tool_use.web_search_requests || 0;
|
||
web_fetch += u.server_tool_use.web_fetch_requests || 0;
|
||
}
|
||
}
|
||
return {
|
||
input_tokens: input,
|
||
output_tokens: output,
|
||
cache_read_input_tokens: cache_read,
|
||
cache_creation_input_tokens: cache_creation,
|
||
web_search_requests: web_search,
|
||
web_fetch_requests: web_fetch,
|
||
iterations,
|
||
// v4.3 LLM-agent cost fields — always zero at parse time;
|
||
// populated retroactively by controller scripts / reviewer response.
|
||
classifier_input_tokens: 0,
|
||
classifier_output_tokens: 0,
|
||
self_assessment_input_tokens: 0,
|
||
self_assessment_output_tokens: 0,
|
||
reviewer_input_tokens: 0,
|
||
reviewer_output_tokens: 0,
|
||
reviewer_subagent_usd: 0,
|
||
reviewer_direct_fallback_usd: 0,
|
||
};
|
||
}
|
||
|
||
/**
|
||
* For each AskUserQuestion toolUseResult in the turn, emit one event per question.
|
||
* answer_kind: 'option' (exact label match), 'custom' (free-text), 'no_answer' (missing/empty).
|
||
*/
|
||
/** Collect concatenated text from all assistant text blocks in the turn. */
|
||
function assistantTextOfTurn(turn) {
|
||
const parts = [];
|
||
for (const e of turn || []) {
|
||
if (!e || !e.message || e.message.role !== 'assistant') continue;
|
||
const content = Array.isArray(e.message.content) ? e.message.content : [];
|
||
for (const b of content) {
|
||
if (b && b.type === 'text' && typeof b.text === 'string') parts.push(b.text);
|
||
}
|
||
}
|
||
return parts.join('\n');
|
||
}
|
||
|
||
const TRIGGER_PATTERNS = [
|
||
/\bPravila\s+§\d+(?:\.\d+)?/g,
|
||
/\bADR-\d+/g,
|
||
/\bPSR_v1\s+R\d+(?:\.\d+)?/g,
|
||
/\brouting-off-phase\s+L\d+/g,
|
||
/\bL\d+\s+chain/g,
|
||
/\bhard-(?:floor|rule)\b/gi,
|
||
];
|
||
|
||
/** Heuristic triggers from assistant text. Conservative-broad — false positives OK. */
|
||
export function extractTriggers(turn) {
|
||
const text = assistantTextOfTurn(turn);
|
||
const out = new Set();
|
||
for (const re of TRIGGER_PATTERNS) {
|
||
const matches = text.match(re);
|
||
if (matches) for (const m of matches) {
|
||
const norm = /^L\d+\s+chain$/.test(m) ? `routing-off-phase ${m.split(/\s+/)[0]}` : m;
|
||
out.add(norm);
|
||
}
|
||
}
|
||
return [...out];
|
||
}
|
||
|
||
const CANDIDATE_NUMBERED_RE = /^\s*\d+[.\)]\s+([^\n]+)$/gm;
|
||
const CANDIDATE_BULLET_RE = /^\s*[-*]\s+([^\n]+)$/gm;
|
||
|
||
/**
|
||
* Heuristic candidates: ≥2 numbered (preferred) or bulleted items, filtered to
|
||
* router-node identifiers (see isKnownNode). Free-form prose bullets are
|
||
* rejected — they belong in the assistant's narrative, not in
|
||
* primary_rationale.candidates_considered. The opt-in <!-- reasoning --> tag
|
||
* (parseReasoningTag) bypasses this filter; that channel is authoritative.
|
||
*/
|
||
export function extractCandidates(turn) {
|
||
const text = assistantTextOfTurn(turn);
|
||
const numbered = [...text.matchAll(CANDIDATE_NUMBERED_RE)].map((m) => m[1].trim());
|
||
if (numbered.length >= 2) {
|
||
const filtered = numbered.map(normalizeCandidate).filter(isKnownNode);
|
||
if (filtered.length >= 2) return filtered;
|
||
}
|
||
const bulleted = [...text.matchAll(CANDIDATE_BULLET_RE)].map((m) => m[1].trim());
|
||
if (bulleted.length >= 2) {
|
||
const filtered = bulleted.map(normalizeCandidate).filter(isKnownNode);
|
||
if (filtered.length >= 2) return filtered;
|
||
}
|
||
return [];
|
||
}
|
||
|
||
const BOUNDARY_PATTERNS = [
|
||
/\bADR-\d+(?:\s+§\d+(?:\.\d+)?)?/g,
|
||
/\bPSR_v1\s+R\d+(?:\.\d+)?/g,
|
||
/\bPravila\s+§\d+(?:\.\d+)?/g,
|
||
/\brouting-off-phase\s+L\d+/g,
|
||
/\bL\d+\s+chain/g,
|
||
];
|
||
|
||
/** Heuristic boundaries — overlaps with triggers, dedup per-array only. */
|
||
export function extractBoundaries(turn) {
|
||
const text = assistantTextOfTurn(turn);
|
||
const out = new Set();
|
||
for (const re of BOUNDARY_PATTERNS) {
|
||
const matches = text.match(re);
|
||
if (matches) for (const m of matches) {
|
||
const norm = /^L\d+\s+chain$/.test(m) ? `routing-off-phase ${m.split(/\s+/)[0]}` : m;
|
||
out.add(norm);
|
||
}
|
||
}
|
||
return [...out];
|
||
}
|
||
|
||
export function extractAskUserQuestionEvents(turn) {
|
||
const events = [];
|
||
for (const e of turn || []) {
|
||
const tur = e && e.toolUseResult;
|
||
if (!tur || !Array.isArray(tur.questions) || !tur.answers) continue;
|
||
const qCount = tur.questions.length;
|
||
for (const q of tur.questions) {
|
||
const labels = (q.options || []).map((o) => o && o.label).filter((l) => typeof l === 'string');
|
||
const answer = tur.answers[q.question];
|
||
let answer_kind;
|
||
if (typeof answer !== 'string' || answer.length === 0) answer_kind = 'no_answer';
|
||
else if (labels.some((l) => l.trim() === answer.trim())) answer_kind = 'option';
|
||
else answer_kind = 'custom';
|
||
events.push({ kind: 'ask_user_question', question_count: qCount, answer_kind });
|
||
}
|
||
}
|
||
return events;
|
||
}
|
||
|
||
/** Classify the opening user-prompt sentiment (per spec §6 / gap-resolution 1). */
|
||
export function classifyPromptSignal(text) {
|
||
const t = String(text || '').toLowerCase().trim();
|
||
if (
|
||
/не совсем|другое|другая|не сходится|wrong direction|не то\b|не так\b|переделай|отбой|\bстоп\b|почему ты|неверно|не верно|это не |не работает|не правильн|сломал|опять|снова не|всё ещё|все ещё|все еще|верни как|откат|\brevert\b|\bundo\b|still not|doesn'?t work|does not work|\bwrong\b/.test(
|
||
t
|
||
)
|
||
) {
|
||
return 'correction';
|
||
}
|
||
if (/^(ок|окей|ok|спасибо|супер|отлично|готово|дальше|идеально|класс|хорошо|принято|well done|\bnice\b)([,\s]|$)/.test(t)) {
|
||
return 'approval';
|
||
}
|
||
if (/^(?:теперь|далее|следующее)(?=\s|[,.!?:;]|$)|^next\b|^now\b/.test(t)) return 'new_task';
|
||
if (classifyTask(t) !== 'other' && t.length > 15) return 'new_task';
|
||
return 'neutral';
|
||
}
|
||
|
||
const TIME_BURN_THRESHOLD_MS = 900000; // 15 min — turn wall-clock above this = time_burn
|
||
const PARSE_GAP_RATIO = 0.1; // >10% unparseable lines = parse_gap
|
||
|
||
/** Heuristic retry count: an errored tool whose name is used again later in the turn. */
|
||
function detectRetries(turn) {
|
||
const idToName = {};
|
||
const uses = [];
|
||
turn.forEach((entry, idx) => {
|
||
const content = entry && entry.message && Array.isArray(entry.message.content) ? entry.message.content : [];
|
||
for (const b of content) {
|
||
if (b && b.type === 'tool_use') {
|
||
idToName[b.id] = b.name;
|
||
uses.push({ name: b.name, idx });
|
||
}
|
||
}
|
||
});
|
||
const errors = [];
|
||
turn.forEach((entry, idx) => {
|
||
const content = entry && entry.message && Array.isArray(entry.message.content) ? entry.message.content : [];
|
||
for (const b of content) {
|
||
if (b && b.type === 'tool_result' && b.is_error === true) {
|
||
errors.push({ name: idToName[b.tool_use_id] || null, idx });
|
||
}
|
||
}
|
||
});
|
||
let retries = 0;
|
||
for (const err of errors) {
|
||
if (err.name && uses.some((u) => u.name === err.name && u.idx > err.idx)) retries += 1;
|
||
}
|
||
return retries;
|
||
}
|
||
|
||
/**
|
||
* Process events for the turn: hook_fired (summary), interrupt, retry,
|
||
* time_burn, parse_gap. broken/total/durationMs are computed by the caller.
|
||
*/
|
||
export function extractProcessEvents(turn, broken, total, durationMs) {
|
||
const events = [];
|
||
|
||
const hookCounts = {};
|
||
let hookErrors = 0;
|
||
for (const e of turn) {
|
||
const att = e && e.attachment;
|
||
if (att && (att.type === 'hook_success' || att.type === 'hook_error')) {
|
||
const name = att.hookName || 'unknown';
|
||
hookCounts[name] = (hookCounts[name] || 0) + 1;
|
||
if (att.type === 'hook_error') hookErrors += 1;
|
||
}
|
||
}
|
||
if (Object.keys(hookCounts).length > 0) {
|
||
const scripts = resolveScriptCounts(hookCounts, getHookMap());
|
||
events.push({ kind: 'hook_fired', counts: hookCounts, scripts, errors: hookErrors });
|
||
}
|
||
|
||
for (const e of turn) {
|
||
const content = e && e.message && Array.isArray(e.message.content) ? e.message.content : [];
|
||
const isUser = e && e.message && e.message.role === 'user';
|
||
if (
|
||
isUser &&
|
||
content.some((b) => b && b.type === 'text' && String(b.text || '').includes('[Request interrupted by user]'))
|
||
) {
|
||
events.push({ kind: 'interrupt' });
|
||
}
|
||
}
|
||
|
||
const retries = detectRetries(turn);
|
||
for (let i = 0; i < retries; i++) events.push({ kind: 'retry' });
|
||
|
||
if (durationMs > TIME_BURN_THRESHOLD_MS) {
|
||
events.push({ kind: 'time_burn', duration_ms: durationMs });
|
||
}
|
||
|
||
if (total > 0 && broken / total > PARSE_GAP_RATIO) {
|
||
events.push({ kind: 'parse_gap', broken, total });
|
||
}
|
||
|
||
// unrecovered_error: emitted iff the LAST tool_result in the turn was
|
||
// is_error=true. Distinguishes "turn ended on failure" from "errors that
|
||
// were retried away" (e.g., TDD red→green, expected-fail commands). The
|
||
// analyzer uses this event to flag `blocked` instead of raw error/retry
|
||
// count — see brain-retro-analyzer.inferOutcome (A-1 fix).
|
||
let lastToolResultIsError = null;
|
||
outer: for (let i = turn.length - 1; i >= 0; i--) {
|
||
const content =
|
||
turn[i] && turn[i].message && Array.isArray(turn[i].message.content) ? turn[i].message.content : [];
|
||
for (let j = content.length - 1; j >= 0; j--) {
|
||
const b = content[j];
|
||
if (b && b.type === 'tool_result') {
|
||
lastToolResultIsError = b.is_error === true;
|
||
break outer;
|
||
}
|
||
}
|
||
}
|
||
if (lastToolResultIsError === true) {
|
||
events.push({ kind: 'unrecovered_error' });
|
||
}
|
||
|
||
return events;
|
||
}
|
||
|
||
const ROUTING_TAG_RE =
|
||
/<!--\s*routing:\s*provenance=([\w_]+)\s+node=(\S+)\s+counterfactual=(\S+)\s*-->/;
|
||
|
||
/** Find the routing tag Claude prints when a method was user-directed (spec §4.2). */
|
||
export function parseRoutingTag(turn) {
|
||
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 === 'text' && typeof b.text === 'string') {
|
||
const m = b.text.match(ROUTING_TAG_RE);
|
||
if (m) return { kind: m[1], node: m[2], claude_would_have_chosen: m[3] };
|
||
}
|
||
}
|
||
}
|
||
return null;
|
||
}
|
||
|
||
/**
|
||
* Per-Agent-tool_use event (Task 12) — surfaces subagent dispatches in the
|
||
* episode `events[]`. Captures subagent_type / model (if explicit in input)
|
||
* / first 80 chars of description.
|
||
*
|
||
* Not the full subagent trace (that lives in ~/.claude/projects/.../subagents/);
|
||
* just visibility from the parent Claude's perspective.
|
||
*/
|
||
export function extractAgentInvocations(turn) {
|
||
const out = [];
|
||
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' && b.name === 'Agent') {
|
||
const inp = b.input || {};
|
||
out.push({
|
||
kind: 'subagent_invoked',
|
||
subagent_type: inp.subagent_type || 'unknown',
|
||
model: inp.model || null,
|
||
description: typeof inp.description === 'string' ? inp.description.slice(0, 80) : '',
|
||
});
|
||
}
|
||
}
|
||
}
|
||
return out;
|
||
}
|
||
|
||
const REASONING_TAG_RE =
|
||
/<!--\s*reasoning:\s*triggers="([^"]*)"\s+candidates="([^"]*)"\s+boundaries="([^"]*)"\s*-->/;
|
||
|
||
/**
|
||
* Opt-in reasoning tag (Task 11). Claude may emit at most one such comment
|
||
* per turn to declare triggers / candidates / boundaries explicitly. Values
|
||
* are semicolon-separated. When present, parser merges them into the
|
||
* heuristic-derived arrays via Set-dedupe.
|
||
*/
|
||
export function parseReasoningTag(turn) {
|
||
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 === 'text' && typeof b.text === 'string') {
|
||
const m = b.text.match(REASONING_TAG_RE);
|
||
if (m) {
|
||
const split = (s) => s.split(';').map((x) => x.trim()).filter(Boolean);
|
||
return { triggers: split(m[1]), candidates: split(m[2]), boundaries: split(m[3]) };
|
||
}
|
||
}
|
||
}
|
||
}
|
||
return null;
|
||
}
|
||
|
||
/** Text of the last real user prompt — used by the Stop-hook routing-gate (Task 5). */
|
||
export function extractLastUserPromptText(transcriptText) {
|
||
const { entries } = parseLines(transcriptText);
|
||
const start = findTurnStart(entries);
|
||
return promptText(entries[start]);
|
||
}
|
||
|
||
/**
|
||
* Content of the last assistant message strictly before the turn start —
|
||
* the message that may have offered options to the user (spec §11.5).
|
||
*/
|
||
function extractLastAssistantContent(entries, turnStartIdx) {
|
||
for (let i = turnStartIdx - 1; i >= 0; i--) {
|
||
const e = entries[i];
|
||
if (e && e.message && e.message.role === 'assistant') {
|
||
const content = e.message.content;
|
||
if (Array.isArray(content)) return content;
|
||
if (typeof content === 'string') return content;
|
||
}
|
||
}
|
||
return null;
|
||
}
|
||
|
||
/**
|
||
* Parse a transcript JSONL string into an observer episode (schema v2).
|
||
* @param {string} transcriptText - Raw JSONL transcript contents.
|
||
* @param {string|null} fallbackSessionId - Used when the transcript has no sessionId.
|
||
* @returns {object} v2 episode.
|
||
*/
|
||
export function parseTranscript(transcriptText, fallbackSessionId = null, options = {}) {
|
||
const { entries, broken, total } = parseLines(transcriptText);
|
||
|
||
const withSession = entries.find((e) => e && e.sessionId);
|
||
const sessionId =
|
||
(withSession && withSession.sessionId) || fallbackSessionId || `unknown-${Date.now()}`;
|
||
|
||
const routerStateBaseDir = options.routerStateBaseDir;
|
||
const routerState = readRouterState(sessionId, routerStateBaseDir ? { baseDir: routerStateBaseDir } : {});
|
||
const routerFields = extractRouterFields(routerState);
|
||
|
||
const start = findTurnStart(entries);
|
||
const turn = entries.slice(start);
|
||
|
||
const stamps = turn.map((e) => e && e.timestamp).filter(Boolean);
|
||
const started_at = stamps[0] || new Date().toISOString();
|
||
const ended_at = stamps[stamps.length - 1] || started_at;
|
||
const durationMs = new Date(ended_at) - new Date(started_at);
|
||
|
||
const _v4 = extractV4Signals(sessionId, {
|
||
startMs: new Date(started_at).getTime(),
|
||
endMs: new Date(ended_at).getTime(),
|
||
baseDir: options.runtimeBaseDir || routerStateBaseDir,
|
||
});
|
||
|
||
const { skills, counts, errors } = collectToolUse(turn);
|
||
|
||
const events = [];
|
||
for (const skill of skills) events.push({ kind: 'skill_invoked', skill });
|
||
if (Object.keys(counts).length > 0) events.push({ kind: 'tool_summary', counts });
|
||
for (const err of errors) {
|
||
events.push({ kind: 'error', tool: err.tool, summary: err.summary });
|
||
}
|
||
events.push(...extractProcessEvents(turn, broken, total, durationMs));
|
||
events.push(...extractAskUserQuestionEvents(turn));
|
||
events.push(...extractAgentInvocations(turn));
|
||
|
||
const usedSuperpowers = skills.some((s) => String(s).startsWith(SUPERPOWERS_PREFIX));
|
||
const prompt = promptText(entries[start]);
|
||
|
||
const lastAsstContent = extractLastAssistantContent(entries, start);
|
||
const choice = detectChoiceProvenance(prompt, lastAsstContent) || detectAskUserQuestionChoice(turn);
|
||
let decision_provenance;
|
||
if (choice) {
|
||
decision_provenance = choice;
|
||
} else {
|
||
const tag = parseRoutingTag(turn);
|
||
decision_provenance =
|
||
tag && tag.kind === 'user_directed_method'
|
||
? { kind: 'user_directed_method', claude_would_have_chosen: tag.claude_would_have_chosen }
|
||
: { kind: 'autonomous', claude_would_have_chosen: null };
|
||
}
|
||
|
||
// Phase 2 Task 15 — schema v4.0. Adds classifier_output (LLM-first decision
|
||
// record), degraded_mode (LLM→regex fallback flag), and
|
||
// environment.classifier_model. Phase 3 (Tasks 16-20) will bump schema_minor
|
||
// for execution_trace, self_assessment, embedding etc.
|
||
const _state = readRouterState(sessionId);
|
||
const _classifierOutput = extractClassifierOutput(_state);
|
||
const _degraded = _state?.classification?.degraded === true;
|
||
const _envBase = extractEnvironment(entries, start);
|
||
const _classifierModel = _classifierOutput?.source === 'llm' ? CLASSIFIER_MODEL : null;
|
||
|
||
return {
|
||
schema_version: 4,
|
||
schema_minor: 4,
|
||
task_id: sessionId,
|
||
task_ref: sessionId,
|
||
timestamps: { started_at, ended_at },
|
||
path_type: usedSuperpowers ? 'regulated' : 'improvised',
|
||
outcome: 'unknown',
|
||
// v4.3: reviewed outcome — always null at write time, filled by /brain-retro reviewer.
|
||
outcome_reviewed: null,
|
||
outcome_reviewed_source: null,
|
||
// v4.3: embedding of first user prompt — null at parse time (sync parser cannot
|
||
// await model load); populated asynchronously by the Stop-hook after parseTranscript.
|
||
prompt_embedding_base64: null,
|
||
prompt_signal: classifyPromptSignal(prompt),
|
||
decision_provenance,
|
||
environment: { ..._envBase, classifier_model: _classifierModel },
|
||
task_size: extractTaskSize(turn),
|
||
// A1 (2026-05-26): merge router-state.task_cost (classifier LLM tokens) on top of
|
||
// extractTokenUsage (assistant per-turn tokens). State-file fields win for the
|
||
// classifier_/self_assessment_/reviewer_ block; assistant input_tokens/output_tokens
|
||
// come from extractTokenUsage and stay intact.
|
||
// NB: routerState (line 855) honours routerStateBaseDir option; _state at line 898
|
||
// does not (always default dir). Use routerState here so tests with custom temp dir
|
||
// see the merged values.
|
||
task_cost: {
|
||
...extractTokenUsage(turn),
|
||
...((routerState && routerState.task_cost) || {}),
|
||
judge_spend_usd: _v4.judge_calls * JUDGE_PER_CALL_USD,
|
||
},
|
||
v4_signals: _v4,
|
||
// 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, options.normativeStems),
|
||
};
|
||
})(),
|
||
classifier_output: _classifierOutput,
|
||
degraded_mode: _degraded,
|
||
primary_rationale: (() => {
|
||
const tag = parseReasoningTag(turn);
|
||
const merge = (heur, fromTag) => [...new Set([...heur, ...fromTag])];
|
||
// recommended_node ОБЯЗАН отражать реальный сигнал классификатора:
|
||
// - routerFields.recommended_node (state-файл от LLM / regex fallback / prefilter inherited),
|
||
// - либо null, если классификатор не сработал.
|
||
// Старый classifMapNode fallback на keyword-regex (`recommendNode(classifyTask, ...)`) запекал
|
||
// ложные «рекомендации» в episode JSONL — brain-retro #6 показала 60-70% false-positive в анализе
|
||
// «direct_ignored_rec». Убрано 2026-05-26.
|
||
return {
|
||
step: 1,
|
||
node_chosen: skills.length > 0 ? skills[0] : 'direct',
|
||
chain_ref: chainsFor(skills.length > 0 ? skills[0] : 'direct', CHAIN_MAP),
|
||
triggers_matched: merge(extractTriggers(turn), tag ? tag.triggers : []),
|
||
candidates_considered: merge(extractCandidates(turn), tag ? tag.candidates : []),
|
||
boundaries_applied: merge(extractBoundaries(turn), tag ? tag.boundaries : []),
|
||
hard_floor: usedSuperpowers
|
||
? { invoked: true, rules: ['Pravila §12'] }
|
||
: { invoked: false, rules: [] },
|
||
task_classification: classifyTask(prompt),
|
||
recommended_node: routerFields.recommended_node,
|
||
recommended_chain: routerFields.recommended_chain,
|
||
chain_progress: routerFields.chain_progress,
|
||
chain_completed: routerFields.chain_completed,
|
||
};
|
||
})(),
|
||
events,
|
||
};
|
||
}
|