Initial commit: OpenClaw Skill Collection

6 custom skills (assign-task, dispatch-webhook, daily-briefing,
task-capture, qmd-brain, tts-voice) with technical documentation.
Compatible with Claude Code, OpenClaw, Codex CLI, and OpenCode.

skills/qmd-brain/handler.ts

@@ -0,0 +1,196 @@
+/**
+ * qmd-brain skill
+ * 第二大腦知識庫：BM25 全文搜尋 + PostgreSQL pgvector 語意搜尋
+ *
+ * 依賴：
+ *   - qmd CLI (npm install -g @tobilu/qmd)
+ *   - embed_to_pg.py (Python venv at /home/selig/apps/qmd-pg/)
+ */
+
+import { execSync, exec } from 'child_process';
+import { promisify } from 'util';
+
+const execAsync = promisify(exec);
+
+const QMD_CMD = '/home/selig/.nvm/versions/node/v24.13.1/bin/qmd';
+const EMBED_PY = '/home/selig/apps/qmd-pg/venv/bin/python3 /home/selig/apps/qmd-pg/embed_to_pg.py';
+const MAX_SEARCH_LEN = 1500; // 回覆中搜尋結果最大字數
+
+interface SearchResult {
+  source: string;
+  chunk: number;
+  text: string;
+  similarity?: number;
+}
+
+interface QmdResult {
+  path: string;
+  text?: string;
+  score?: number;
+}
+
+/** 執行 qmd BM25 全文搜尋 */
+async function qmdSearch(query: string, topK = 5): Promise<string> {
+  try {
+    const { stdout } = await execAsync(
+      `${QMD_CMD} search ${JSON.stringify(query)} --output markdown --limit ${topK}`,
+      { timeout: 15000, env: { ...process.env, HOME: '/home/selig' } }
+    );
+    return stdout.trim() || '（無結果）';
+  } catch (e: any) {
+    return `qmd search 錯誤: ${e.message?.split('\n')[0]}`;
+  }
+}
+
+/** 執行 PostgreSQL 向量語意搜尋 */
+async function pgSearch(query: string, topK = 5): Promise<SearchResult[]> {
+  try {
+    const { stdout } = await execAsync(
+      `${EMBED_PY} search ${JSON.stringify(query)} --top-k ${topK} --json`,
+      { timeout: 20000 }
+    );
+    return JSON.parse(stdout) as SearchResult[];
+  } catch (e: any) {
+    return [];
+  }
+}
+
+/** 格式化 pgvector 搜尋結果 */
+function formatPgResults(results: SearchResult[]): string {
+  if (!results.length) return '（向量庫無相關結果）';
+  return results.map((r, i) => {
+    const fname = r.source.split('/').pop() || r.source;
+    const snippet = r.text.slice(0, 200).replace(/\n/g, ' ');
+    const score = r.similarity ? `${(r.similarity * 100).toFixed(1)}%` : '';
+    return `**[${i + 1}] ${fname}** ${score}\n> ${snippet}...`;
+  }).join('\n\n');
+}
+
+/** 觸發向量索引更新 */
+async function triggerEmbed(): Promise<string> {
+  try {
+    // 背景執行，不等待完成
+    exec(
+      `${QMD_CMD} embed 2>&1 >> /tmp/qmd-embed.log & ${EMBED_PY} embed 2>&1 >> /tmp/qmd-embed.log &`,
+      { env: { ...process.env, HOME: '/home/selig' } }
+    );
+    return '✅ 索引更新已在背景啟動，約需 1-5 分鐘完成。';
+  } catch (e: any) {
+    return `❌ 索引啟動失敗: ${e.message}`;
+  }
+}
+
+/** 取得向量庫統計 */
+async function getStats(): Promise<string> {
+  const results: string[] = [];
+
+  // qmd collection list
+  try {
+    const { stdout } = await execAsync(
+      `${QMD_CMD} collection list`,
+      { timeout: 5000, env: { ...process.env, HOME: '/home/selig' } }
+    );
+    results.push(`**qmd Collections:**\n\`\`\`\n${stdout.trim()}\n\`\`\``);
+  } catch (e: any) {
+    results.push(`qmd: ${e.message?.split('\n')[0]}`);
+  }
+
+  // pgvector stats
+  try {
+    const { stdout } = await execAsync(
+      `${EMBED_PY} stats`,
+      { timeout: 10000 }
+    );
+    results.push(`**PostgreSQL pgvector:**\n\`\`\`\n${stdout.trim()}\n\`\`\``);
+  } catch (e: any) {
+    results.push(`pgvector: ${e.message?.split('\n')[0]}`);
+  }
+
+  return results.join('\n\n');
+}
+
+/** 判斷使用者意圖 */
+function detectIntent(message: string): 'search' | 'embed' | 'stats' | 'vsearch' {
+  const lower = message.toLowerCase();
+
+  if (lower.match(/更新|重新索引|rebuild|index|embed|掃描/)) return 'embed';
+  if (lower.match(/統計|stat|幾個|多少|collection/)) return 'stats';
+  if (lower.match(/語意|向量|vsearch|概念|類似/)) return 'vsearch';
+  return 'search';
+}
+
+/** 從訊息提取搜尋關鍵字 */
+function extractQuery(message: string): string {
+  return message
+    .replace(/^(搜尋|查找|找資料|幫我找|查一下|搜一下|找到|有沒有|之前說過|我之前|recall|brain search|qmd)[：:：]?\s*/i, '')
+    .replace(/(請|幫我|的資料|的內容|相關|嗎|？|\?)/g, '')
+    .trim() || message.trim();
+}
+
+// ─── 主 handler ─────────────────────────────────────────────────────────────
+
+export async function handler(ctx: any) {
+  const message = ctx.message?.text || ctx.message?.content || '';
+  const intent = detectIntent(message);
+  const query = extractQuery(message);
+
+  // 更新索引
+  if (intent === 'embed') {
+    const result = await triggerEmbed();
+    return { reply: `🧠 **第二大腦索引更新**\n\n${result}` };
+  }
+
+  // 統計
+  if (intent === 'stats') {
+    const stats = await getStats();
+    return { reply: `📊 **知識庫統計**\n\n${stats}` };
+  }
+
+  // 語意搜尋（只用 pgvector）
+  if (intent === 'vsearch') {
+    if (!query) {
+      return { reply: '請提供搜尋關鍵字，例如：「語意搜尋 Telegram 機器人設定」' };
+    }
+    const results = await pgSearch(query, 5);
+    const formatted = formatPgResults(results);
+    return {
+      reply: `🔍 **語意搜尋**：${query}\n\n${formatted}`,
+      metadata: { query, results_count: results.length, engine: 'pgvector' },
+    };
+  }
+
+  // 混合搜尋（BM25 + 向量）
+  if (!query) {
+    return { reply: '請提供搜尋關鍵字，例如：「搜尋 nginx 設定」' };
+  }
+
+  // 並行執行兩種搜尋
+  const [bm25Result, vectorResults] = await Promise.all([
+    qmdSearch(query, 3),
+    pgSearch(query, 3),
+  ]);
+
+  const vectorFormatted = formatPgResults(vectorResults);
+  const hasVector = vectorResults.length > 0 && !vectorFormatted.includes('無相關結果');
+
+  let reply = `🧠 **第二大腦搜尋**：${query}\n\n`;
+  reply += `### 全文搜尋 (BM25)\n${bm25Result}`;
+
+  if (hasVector) {
+    reply += `\n\n### 語意搜尋 (向量)\n${vectorFormatted}`;
+  }
+
+  // 截斷過長回覆
+  if (reply.length > MAX_SEARCH_LEN) {
+    reply = reply.slice(0, MAX_SEARCH_LEN) + '\n\n...(結果已截斷，輸入「語意搜尋 [關鍵字]」可專注向量搜尋)';
+  }
+
+  return {
+    reply,
+    metadata: {
+      query,
+      bm25_chars: bm25Result.length,
+      vector_results: vectorResults.length,
+    },
+  };
+}