feat: 添加 skill learning 技能学习闭环系统

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/services/skillSearch/intentNormalize.ts

@@ -0,0 +1,149 @@
+/**
+ * Intent Normalization Layer for Skill Search
+ *
+ * Problem: TF-IDF bag-of-words loses meaning when the user query is in Chinese
+ * and most skill descriptions are English. CJK bi-grams get DF=1 (language
+ * mismatch, not true rarity), producing IDF values that promote spurious
+ * matches like `prompt-optimizer` for `帮我优化代码的性能`.
+ *
+ * Fix: Before handing the query to `searchSkills()`, ask Haiku to normalize it
+ * into 3-6 English task/object keywords. Concatenate the normalized form with
+ * the original so TF-IDF sees both — English keywords carry real matching
+ * signal, the original text stays as a fallback.
+ *
+ * Design:
+ * - Turn-zero only (blocking on user input): one Haiku call per session-unique
+ *   query. Not called in inter-turn prefetch (which repeats per tool loop).
+ * - Process-level cache: identical queries within a session reuse the result.
+ * - Graceful fallback: Haiku failure / timeout / empty → return original query.
+ * - ASCII-only fast path: queries without CJK characters skip the LLM entirely.
+ * - Feature-flagged: `SKILL_SEARCH_INTENT_ENABLED=1` to opt in.
+ */
+
+import { queryHaiku } from '../api/claude.js'
+import { asSystemPrompt } from '../../utils/systemPromptType.js'
+import { logForDebugging } from '../../utils/debug.js'
+
+const INTENT_SYSTEM_PROMPT = `You are a query normalizer for a skill-search index.
+
+Given a user's natural-language request (often Chinese, possibly long), extract 3-6 English keywords that capture:
+1. TASK VERB (optimize, review, debug, refactor, test, deploy, analyze, write, audit, design, research, cleanup, implement)
+2. OBJECT (code, prompt, test, UI, API, database, documentation, performance, security, architecture)
+3. CONTEXT/DOMAIN when clear (frontend, backend, mobile, python, go, rust, typescript)
+
+Output ONLY space-separated lowercase English keywords. No prose, no JSON, no punctuation, no code fences.
+
+Examples:
+- "帮我优化代码的性能" -> optimize code performance refactor
+- "研究当前代码的实现然后分析优化思路" -> analyze code research refactor architecture
+- "优化 prompt 的表达" -> optimize prompt refine writing
+- "帮我做 code review" -> code review audit
+- "清理代码里的 TODO" -> cleanup refactor dead-code
+- "重构这个模块的代码" -> refactor code modularize
+- "帮我写个 Go 单元测试" -> write test golang unit
+
+Output ONLY keywords. Nothing else.`
+
+const DEFAULT_TIMEOUT_MS = 6_000
+const MAX_QUERY_CHARS = 500
+const MAX_KEYWORDS_CHARS = 120
+
+/** Process-level cache. Keyed by the original (trimmed) query. */
+const cache = new Map<string, string>()
+
+export function isIntentNormalizeEnabled(): boolean {
+  return process.env.SKILL_SEARCH_INTENT_ENABLED === '1'
+}
+
+/** Only reset between tests. */
+export function clearIntentNormalizeCache(): void {
+  cache.clear()
+}
+
+/**
+ * Normalize a user query so TF-IDF sees English task keywords.
+ * Returns `<original> <keywords>` on success, or the original string on any
+ * failure path. Never throws.
+ */
+export async function normalizeQueryIntent(query: string): Promise<string> {
+  const trimmed = query.trim()
+  if (!trimmed) return trimmed
+  if (!isIntentNormalizeEnabled()) return trimmed
+
+  // ASCII-only queries are already in the right shape for the index.
+  if (!/[\u4e00-\u9fff]/.test(trimmed)) return trimmed
+
+  const cached = cache.get(trimmed)
+  if (cached !== undefined) return cached
+
+  const capped = trimmed.slice(0, MAX_QUERY_CHARS)
+  const keywords = await callHaiku(capped)
+  const result = keywords ? `${trimmed} ${keywords}` : trimmed
+  cache.set(trimmed, result)
+  logForDebugging(
+    `[skill-search] intent normalized: "${trimmed.slice(0, 40)}" -> "${keywords}"`,
+  )
+  return result
+}
+
+async function callHaiku(query: string): Promise<string> {
+  const timeoutMs = getTimeoutMs()
+  const controller = new AbortController()
+  const timer = setTimeout(() => controller.abort(), timeoutMs)
+
+  try {
+    const response = await queryHaiku({
+      systemPrompt: asSystemPrompt([INTENT_SYSTEM_PROMPT]),
+      userPrompt: query,
+      signal: controller.signal,
+      options: {
+        querySource: 'skill_search_intent',
+        enablePromptCaching: true,
+        agents: [],
+        isNonInteractiveSession: true,
+        hasAppendSystemPrompt: false,
+        mcpTools: [],
+      },
+    })
+    const text = extractResponseText(response?.message?.content)
+    return sanitizeKeywords(text)
+  } catch (error) {
+    logForDebugging(`[skill-search] intent normalize failed: ${error}`)
+    return ''
+  } finally {
+    clearTimeout(timer)
+  }
+}
+
+function getTimeoutMs(): number {
+  const raw = process.env.SKILL_SEARCH_INTENT_TIMEOUT_MS
+  if (!raw) return DEFAULT_TIMEOUT_MS
+  const parsed = Number(raw)
+  if (!Number.isFinite(parsed) || parsed <= 0) return DEFAULT_TIMEOUT_MS
+  return parsed
+}
+
+function extractResponseText(content: unknown): string {
+  if (!Array.isArray(content)) return ''
+  const parts: string[] = []
+  for (const block of content) {
+    if (!block || typeof block !== 'object') continue
+    const record = block as Record<string, unknown>
+    if (record.type !== 'text') continue
+    if (typeof record.text === 'string') parts.push(record.text)
+  }
+  return parts.join('').trim()
+}
+
+function sanitizeKeywords(raw: string): string {
+  if (!raw) return ''
+  // Strip anything that's not a keyword character. Keep ascii letters, digits,
+  // hyphens, and spaces. Collapse whitespace.
+  const cleaned = raw
+    .toLowerCase()
+    .replace(/[^a-z0-9\- ]+/g, ' ')
+    .replace(/\s+/g, ' ')
+    .trim()
+  if (!cleaned) return ''
+  return cleaned.slice(0, MAX_KEYWORDS_CHARS)
+}