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https://github.com/claude-code-best/claude-code.git
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feat: 添加 skill learning 技能学习闭环系统
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
unraid
@@ -1,18 +1,328 @@
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// Auto-generated stub — replace with real implementation
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import type { Attachment } from '../../utils/attachments.js'
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import type { Message } from '../../types/message.js'
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import type { ToolUseContext } from '../../Tool.js'
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import type { DiscoverySignal } from './signals.js'
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import { isSkillSearchEnabled } from './featureCheck.js'
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import {
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getSkillIndex,
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searchSkills,
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type SearchResult,
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} from './localSearch.js'
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import { normalizeQueryIntent } from './intentNormalize.js'
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import { logForDebugging } from '../../utils/debug.js'
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import { readFile } from 'node:fs/promises'
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import { join } from 'node:path'
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import { parseFrontmatter } from '../../utils/frontmatterParser.js'
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export const startSkillDiscoveryPrefetch: (
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const discoveredThisSession = new Set<string>()
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const recordedGapSignals = new Set<string>()
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const AUTO_LOAD_MIN_SCORE = Number(
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process.env.SKILL_SEARCH_AUTOLOAD_MIN_SCORE ?? '0.30',
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)
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const AUTO_LOAD_LIMIT = Number(process.env.SKILL_SEARCH_AUTOLOAD_LIMIT ?? '2')
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const AUTO_LOAD_MAX_CHARS = Number(
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process.env.SKILL_SEARCH_AUTOLOAD_MAX_CHARS ?? '12000',
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)
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export function extractQueryFromMessages(
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input: string | null,
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messages: Message[],
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): string {
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const parts: string[] = []
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if (input) parts.push(input)
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// Walk backward. In inter-turn prefetch the most recent 'user' message is
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// typically a tool_result (no text block), so we must keep walking until we
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// find a real user utterance with string content or a text block.
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for (let i = messages.length - 1; i >= 0; i--) {
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const msg = messages[i] as Record<string, unknown>
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if (msg.type !== 'user') continue
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const content = msg.content
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if (typeof content === 'string') {
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parts.push(content.slice(0, 500))
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break
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}
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if (Array.isArray(content)) {
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let foundText = false
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for (const block of content) {
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const entry = block as Record<string, unknown>
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// Skip tool_result and other non-text blocks — they carry no discovery
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// signal and would return undefined here regardless.
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if (entry.type && entry.type !== 'text') continue
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const text = entry.text
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if (typeof text === 'string' && text.trim()) {
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parts.push(text.slice(0, 500))
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foundText = true
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break
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}
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}
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if (foundText) break
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}
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}
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return parts.join(' ')
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}
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function buildDiscoveryAttachment(
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skills: SkillDiscoveryResult[],
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signal: DiscoverySignal,
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gap?: SkillDiscoveryGap,
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): Attachment {
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return {
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type: 'skill_discovery',
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skills,
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signal,
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source: 'native',
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gap,
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} as Attachment
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}
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type SkillDiscoveryResult = {
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name: string
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description: string
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shortId?: string
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score?: number
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autoLoaded?: boolean
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content?: string
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path?: string
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}
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type SkillDiscoveryGap = {
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key: string
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status: 'pending' | 'draft' | 'active'
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draftName?: string
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draftPath?: string
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activeName?: string
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activePath?: string
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}
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async function enrichResultsForAutoLoad(
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results: SearchResult[],
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context: ToolUseContext,
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): Promise<SkillDiscoveryResult[]> {
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let loadedCount = 0
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const enriched: SkillDiscoveryResult[] = []
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for (const result of results) {
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const base: SkillDiscoveryResult = {
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name: result.name,
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description: result.description,
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score: result.score,
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}
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if (loadedCount >= AUTO_LOAD_LIMIT || result.score < AUTO_LOAD_MIN_SCORE) {
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enriched.push(base)
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continue
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}
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const loaded = await loadSkillContent(result)
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if (!loaded) {
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enriched.push(base)
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continue
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}
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loadedCount++
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await markAutoLoadedSkill(result.name, loaded.path, loaded.content, context)
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enriched.push({
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...base,
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autoLoaded: true,
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content: loaded.content,
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path: loaded.path,
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})
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}
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return enriched
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}
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async function loadSkillContent(
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result: SearchResult,
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): Promise<{ path: string; content: string } | null> {
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if (!result.skillRoot) return null
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const candidates = [
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join(result.skillRoot, 'SKILL.md'),
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join(result.skillRoot, 'skill.md'),
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]
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for (const path of candidates) {
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try {
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const raw = await readFile(path, 'utf8')
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return {
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path,
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content: parseFrontmatter(raw).content.slice(0, AUTO_LOAD_MAX_CHARS),
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}
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} catch {
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// Try next candidate.
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}
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}
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return null
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}
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async function markAutoLoadedSkill(
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name: string,
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path: string,
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content: string,
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context: ToolUseContext,
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): Promise<void> {
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try {
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const { addInvokedSkill } = await import('../../bootstrap/state.js')
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addInvokedSkill(name, path, content, context.agentId ?? null)
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} catch {
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// Best effort only.
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}
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}
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async function maybeRecordSkillGap(
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queryText: string,
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results: SearchResult[],
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context: ToolUseContext,
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trigger: DiscoverySignal['trigger'],
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): Promise<SkillDiscoveryGap | undefined> {
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if (trigger !== 'user_input') return undefined
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if (!queryText.trim()) return undefined
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const gapSignalKey = `${trigger}:${queryText.trim().toLowerCase()}`
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if (recordedGapSignals.has(gapSignalKey)) return undefined
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recordedGapSignals.add(gapSignalKey)
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try {
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const [{ isSkillLearningEnabled }, { recordSkillGap }] = await Promise.all([
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import('../skillLearning/featureCheck.js'),
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import('../skillLearning/skillGapStore.js'),
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])
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if (!isSkillLearningEnabled()) return undefined
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const gap = await recordSkillGap({
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prompt: queryText,
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cwd:
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((context as Record<string, unknown>).cwd as string) ?? process.cwd(),
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sessionId:
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((context as Record<string, unknown>).sessionId as string) ??
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'unknown-session',
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recommendations: results,
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})
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const status = gap.status
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if (status !== 'pending' && status !== 'draft' && status !== 'active') {
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return undefined
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}
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return {
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key: gap.key,
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status,
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draftName: gap.draft?.name,
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draftPath: gap.draft?.skillPath,
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activeName: gap.active?.name,
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activePath: gap.active?.skillPath,
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}
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} catch (error) {
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logForDebugging(`[skill-search] skill gap learning error: ${error}`)
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return undefined
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}
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}
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export async function startSkillDiscoveryPrefetch(
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input: string | null,
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messages: Message[],
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toolUseContext: ToolUseContext,
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) => Promise<Attachment[]> = (async () => []);
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export const collectSkillDiscoveryPrefetch: (
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): Promise<Attachment[]> {
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if (!isSkillSearchEnabled()) return []
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const startedAt = Date.now()
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const queryText = extractQueryFromMessages(input, messages)
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if (!queryText.trim()) return []
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try {
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const cwd =
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((toolUseContext as Record<string, unknown>).cwd as string) ??
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process.cwd()
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const index = await getSkillIndex(cwd)
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const results = searchSkills(queryText, index)
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const newResults = results.filter(r => !discoveredThisSession.has(r.name))
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if (newResults.length === 0) return []
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for (const r of newResults) discoveredThisSession.add(r.name)
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const signal: DiscoverySignal = {
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trigger: 'assistant_turn',
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queryText: queryText.slice(0, 200),
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startedAt,
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durationMs: Date.now() - startedAt,
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indexSize: index.length,
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method: 'tfidf',
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}
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logForDebugging(
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`[skill-search] prefetch found ${newResults.length} skills in ${signal.durationMs}ms`,
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)
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return [
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buildDiscoveryAttachment(
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await enrichResultsForAutoLoad(newResults, toolUseContext),
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signal,
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),
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]
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} catch (error) {
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logForDebugging(`[skill-search] prefetch error: ${error}`)
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return []
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}
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}
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export async function collectSkillDiscoveryPrefetch(
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pending: Promise<Attachment[]>,
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) => Promise<Attachment[]> = (async (pending) => pending);
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export const getTurnZeroSkillDiscovery: (
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): Promise<Attachment[]> {
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try {
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return await pending
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} catch {
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return []
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}
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}
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export async function getTurnZeroSkillDiscovery(
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input: string,
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messages: Message[],
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context: ToolUseContext,
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) => Promise<Attachment | null> = (async () => null);
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): Promise<Attachment | null> {
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if (!isSkillSearchEnabled()) return null
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if (!input.trim()) return null
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const startedAt = Date.now()
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try {
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const cwd =
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((context as Record<string, unknown>).cwd as string) ?? process.cwd()
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const index = await getSkillIndex(cwd)
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// Intent normalization (feature-flagged, ASCII-only fast path, graceful
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// fallback to original). Turn-zero is the one blocking entry — acceptable
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// to add a Haiku call here since a bad match here pollutes the LLM's
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// context for the entire session.
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const searchQuery = await normalizeQueryIntent(input)
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const results = searchSkills(searchQuery, index)
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const enriched = await enrichResultsForAutoLoad(results, context)
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const gap = enriched.some(result => result.autoLoaded)
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? undefined
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: await maybeRecordSkillGap(input, results, context, 'user_input')
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if (results.length === 0 && !gap) return null
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for (const r of results) discoveredThisSession.add(r.name)
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const signal: DiscoverySignal = {
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trigger: 'user_input',
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queryText: input.slice(0, 200),
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startedAt,
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durationMs: Date.now() - startedAt,
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indexSize: index.length,
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method: 'tfidf',
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}
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logForDebugging(
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`[skill-search] turn-zero found ${results.length} skills in ${signal.durationMs}ms`,
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)
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return buildDiscoveryAttachment(enriched, signal, gap)
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} catch (error) {
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logForDebugging(`[skill-search] turn-zero error: ${error}`)
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return null
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}
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}
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