Agent skill · data analytics · anthropics
search-strategy
Query decomposition and multi-source search orchestration. Breaks natural language questions into targeted searches per source, translates queries into source-specific syntax, ranks results by relevance, and handles ambiguity and fallback strategies.
Why this skill is useful
Adds a comprehensive framework for query decomposition and multi-source search orchestration that enhances the AI's ability to synthesize information from diverse sources.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
Search Strategy 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. The Goal Turn this: Into targeted searches across every connected source: Then synthesize the results into a single coherent answer. Query Decomposition Step 1: Identify Query Type 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 Step 2: Extract Search Components From the query, extract: Keywords: Core terms that must appear in results Entities: People, projects, teams, tools (use memory system if available) Intent signals: Decision words, status words, temporal markers Constraints: Time ranges, source hints, author filters Negations: Things to exclude Step 3: Generate Sub-Queries Per Source For each available source, create one or more targeted queries: Prefer semantic search for: Conceptual questions ("What do we think about...") Questions where exact keywords are unknown Exploratory queries Prefer keyword search for: Known terms, project names, acronyms Exact phrases the user quoted Filter-heavy queries (from:, in:, after:) Generate multiple query variants when the topic might be referred to …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/search-strategy