feat: add local analytics sink for self-analysis
- localSink.ts: writes every logEvent to ~/.claude/local_analytics.jsonl - Modified index.ts: logEvent() now writes locally in parallel with upstream sinks - analyze_analytics.py: Python analysis script — event stats, tool rankings, time distribution, security events, RL preference signals No upstream data flow is affected. Local writes are non-blocking append. Testing: one -p 'hello' captured 32 events across 19 event types.
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scripts/analyze_analytics.py
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116
scripts/analyze_analytics.py
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#!/usr/bin/env python3
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"""
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CC_Pure 本地遥测分析工具
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用法:
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python3 analyze_analytics.py ~/.claude/local_analytics.jsonl
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功能:
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- 事件总览(按类型统计)
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- 时间线分析(每小时事件分布)
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- 工具使用排行
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- RL 偏好信号分析(accept/reject 比例)
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- 安全事件摘要
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"""
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import json
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import sys
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from collections import Counter, defaultdict
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from datetime import datetime
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def load_events(path: str) -> list[dict]:
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events = []
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with open(path) as f:
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for line in f:
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line = line.strip()
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if line:
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try:
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events.append(json.loads(line))
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except json.JSONDecodeError:
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pass
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return events
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def analyze(events: list[dict]):
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if not events:
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print("📭 暂无遥测数据。")
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return
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sep = "=" * 50
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print(f"📊 CC_Pure 使用分析报告")
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print(sep)
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print(f"总事件数: {len(events)}")
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if events:
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first = events[0].get("ts", "?")
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last = events[-1].get("ts", "?")
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print(f"时间范围: {first[:19] if first != '?' else '?'} → {last[:19] if last != '?' else '?'}")
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# ── 事件类型统计 ──
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print(f"\n📋 事件类型统计:")
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counter = Counter(e.get("event", "unknown") for e in events)
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for name, count in counter.most_common(20):
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bar = "█" * min(count // max(1, counter.most_common(1)[0][1] // 20), 40)
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print(f" {count:>5d} {name:<45s} {bar}")
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# ── 工具使用排行 ──
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tool_events = defaultdict(int)
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for e in events:
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name = e.get("event", "")
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# Extract tool name from tengu_<tool>_<action> pattern
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parts = name.split("_")
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if len(parts) >= 3 and parts[0] == "tengu":
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tool = parts[1] if parts[1] not in ("tool", "internal", "auto", "read", "git", "exit", "api", "web", "edit", "skill", "quartz", "amber", "plum", "birch", "surreal", "glacier", "kairos", "hive", "unary", "monitor", "config") else "_".join(parts[1:3])
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tool_events[tool] += 1
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if tool_events:
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print(f"\n🔧 工具活动排行:")
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for tool, count in sorted(tool_events.items(), key=lambda x: -x[1])[:15]:
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print(f" {count:>5d} {tool}")
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# ── RL 偏好信号 (accept/reject) ──
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accept_count = 0
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reject_count = 0
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for e in events:
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evt = e.get("event", "")
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if isinstance(evt, str):
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if evt == "accept":
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accept_count += 1
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elif evt == "reject":
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reject_count += 1
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if accept_count + reject_count > 0:
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total = accept_count + reject_count
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print(f"\n✅ 用户偏好信号 (accept/reject):")
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print(f" 接受: {accept_count} ({accept_count/total*100:.0f}%)")
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print(f" 拒绝: {reject_count} ({reject_count/total*100:.0f}%)")
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# ── 时间分布 ──
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print(f"\n⏰ 按小时分布:")
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hourly = Counter()
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for e in events:
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ts = e.get("ts", "")
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if ts and len(ts) >= 13:
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hour = ts[:13]
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hourly[hour] += 1
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for hour, count in sorted(hourly.items())[-20:]:
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bar = "█" * (count // max(1, max(hourly.values()) // 30))
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print(f" {hour}:00 {count:>5d} {bar}")
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# ── 安全事件 ──
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security_keywords = ["security", "sandbox", "dangerous", "deny", "malformed"]
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security_events = [
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e for e in events
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if any(kw in str(e.get("event", "")).lower() for kw in security_keywords)
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]
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if security_events:
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print(f"\n🛡️ 安全事件 ({len(security_events)} 条):")
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for e in security_events[-10:]:
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print(f" [{e.get('ts','?')[:19]}] {e.get('event','?')}")
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print("\n" + "=" * 50)
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print("💡 提示: 用 `tail -f ~/.claude/local_analytics.jsonl` 实时查看事件流")
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if __name__ == "__main__":
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path = sys.argv[1] if len(sys.argv) > 1 else f"/home/{__import__('os').environ.get('USER', 'spark')}/.claude/local_analytics.jsonl"
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analyze(load_events(path))
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@ -136,6 +136,14 @@ export function logEvent(
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// to avoid accidentally logging code/filepaths
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// to avoid accidentally logging code/filepaths
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metadata: LogEventMetadata,
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metadata: LogEventMetadata,
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): void {
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): void {
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// Local analytics: write EVERY event to a local JSONL file for self-analysis.
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// Runs before the upstream sink so local data is never lost even if the
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// upstream sink throws. Import is inline to avoid adding a module-level
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// dependency to this zero-dependency entry point.
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// eslint-disable-next-line @typescript-eslint/no-require-imports
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const { writeLocalEvent } = require('./localSink.js') as typeof import('./localSink.js')
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writeLocalEvent(eventName, metadata as Record<string, unknown>)
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if (sink === null) {
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if (sink === null) {
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eventQueue.push({ eventName, metadata, async: false })
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eventQueue.push({ eventName, metadata, async: false })
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return
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return
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54
src/services/analytics/localSink.ts
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54
src/services/analytics/localSink.ts
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@ -0,0 +1,54 @@
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/**
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* Local analytics sink — writes events to a JSONL file on disk.
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*
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* This runs IN PARALLEL with the upstream Datadog/1P sinks (if enabled).
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* Events are appended immediately; timestamps are added by the writer.
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*
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* Output: ~/.claude/local_analytics.jsonl
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* Format: one JSON object per line, fields: { ts, event, ...metadata }
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*/
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import * as fs from 'node:fs'
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import * as path from 'node:path'
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import * as os from 'node:os'
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const LOCAL_ANALYTICS_DIR = path.join(os.homedir(), '.claude')
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const LOCAL_ANALYTICS_FILE = path.join(LOCAL_ANALYTICS_DIR, 'local_analytics.jsonl')
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// Ensure the directory exists on first write
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let dirEnsured = false
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function ensureDir(): void {
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if (!dirEnsured) {
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fs.mkdirSync(LOCAL_ANALYTICS_DIR, { recursive: true })
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dirEnsured = true
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}
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}
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/**
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* Write a single analytics event to the local JSONL file.
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* Non-blocking sync write — JSONL appends are fast enough for analytics volume.
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*/
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export function writeLocalEvent(
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eventName: string,
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metadata: Record<string, unknown>,
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): void {
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try {
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ensureDir()
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const line = JSON.stringify({
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ts: new Date().toISOString(),
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event: eventName,
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...metadata,
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}) + '\n'
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fs.appendFileSync(LOCAL_ANALYTICS_FILE, line, 'utf-8')
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} catch {
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// Silently ignore write failures — analytics must never crash the app
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}
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}
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/**
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* Return the path to the local analytics file for external analysis tools.
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*/
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export function getLocalAnalyticsPath(): string {
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return LOCAL_ANALYTICS_FILE
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}
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