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将复杂问题拆解为多源检索策略，并汇总高相关结果与备选路径。
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请帮我安装 askskill 上的 "search-strategy" 技能： 1. 下载 https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/enterprise-search/skills/search-strategy/SKILL.md 2. 保存为 ~/.claude/skills/search-strategy/SKILL.md 3. 装好后重载技能，告诉我可以用了
请把“调研三家海外 AI 笔记产品的定价、核心功能和用户评价”拆解成多轮检索计划，分别说明适合搜索的来源、每个来源的检索语句，以及信息不足时的备选搜索策略。
一份结构化检索方案，包含问题拆解、按来源定制的查询语句、结果优先级和回退策略。
我想了解“RAG 在企业知识库中的评测方法”，请将问题拆成适合论文库、技术博客、产品文档的搜索子任务，并给出每类来源的关键词与筛选条件。
按来源分类的检索子任务列表，附带查询表达式、筛选建议和结果判断标准。
用户只说“帮我找最新的模型安全规范”，请设计一个能处理歧义的搜索策略：先识别可能含义，再分别生成监管文件、行业标准和厂商最佳实践的搜索方案，并说明找不到结果时怎么回退。
一套先澄清再检索的策略，覆盖歧义分支、来源选择、查询语法与无结果时的替代路径。
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
The core intelligence behind enterprise search. Transforms a single natural language question into parallel, source-specific searches and produces ranked, deduplicated results.
Turn this:
"What did we decide about the API migration timeline?"
Into targeted searches across every connected source:
~~chat: "API migration timeline decision" (semantic) + "API migration" in:#engineering after:2025-01-01
~~knowledge base: semantic search "API migration timeline decision"
~~project tracker: text search "API migration" in relevant workspace
Then synthesize the results into a single coherent answer.
Classify the user's question to determine search strategy:
| Query Type | Example | Strategy |
|---|---|---|
| Decision | "What did we decide about X?" | Prioritize conversations (~~chat, email), look for conclusion signals |
| Status | "What's the status of Project Y?" | Prioritize recent activity, task trackers, status updates |
| Document | "Where's the spec for Z?" | Prioritize Drive, wiki, shared docs |
| Person | "Who's working on X?" | Search task assignments, message authors, doc collaborators |
| Factual | "What's our policy on X?" | Prioritize wiki, official docs, then confirmatory conversations |
| Temporal | "When did X happen?" | Search with broad date range, look for timestamps |
| Exploratory | "What do we know about X?" | Broad search across all sources, synthesize |
From the query, extract:
For each available source, create one or more targeted queries:
Prefer semantic search for:
Prefer keyword search for:
Generate multiple query variants when the topic might be referred to differently:
User: "Kubernetes setup"
Queries: "Kubernetes", "k8s", "cluster", "container orchestration"
Semantic search (natural language questions):
query: "What is the status of project aurora?"
Keyword search:
query: "project aurora status update"
query: "aurora in:#engineering after:2025-01-15"
query: "from:<@UserID> aurora"
Filter mapping:
| Enterprise filter | ~~chat syntax |
|---|---|
from:sarah | from:sarah or from:<@USERID> |
in:engineering | in:engineering |
after:2025-01-01 | after:2025-01-01 |
before:2025-02-01 | before:2025-02-01 |
type:thread | is:thread |
type:file | has:file |
Semantic search — Use for conceptual queries:
descriptive_query: "API migration timeline and decision rationale"
Keyword search — Use for exact terms:
query: "API migration"
query: "\"API migration timeline\"" (exact phrase)
Task search:
text: "API migration"
workspace: [workspace_id]
completed: false (for status queries)
assignee_any: "me" (for "my tasks" queries)
Filter mapping:
| Enterprise filter | ~~project tracker parameter |
|---|---|
from:sarah | assignee_any or created_by_any |
after:2025-01-01 | modified_on_after: "2025-01-01" |
…
围绕客户问题进行多来源调研与溯源，快速整理背景并支持准确回复。
针对技术、市场与竞品等主题开展多源深度调研并输出综合洞察。