Search Strategy
AnthropicOfficialPublished by Anthropic
- Skill
- Meta (authoring, orchestration, planning, memory)
- Data and AI
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Search Strategy provides a structured approach for AI agents to interpret user questions and execute multi-source information retrieval. It defines a process for classifying query intent, extracting key entities and constraints, and translating natural language into source-specific syntax for chat, knowledge bases, and project trackers. The skill includes logic for ranking results based on authority and query type, handling ambiguity through clarification, and implementing fallback strategies when initial searches fail.
What it does
- Classifies user questions into types such as decision, status, document, or factual.
- Decomposes natural language queries into keywords, entities, and intent signals.
- Generates parallel, source-specific search queries for different enterprise platforms.
- Maps enterprise filters to specific syntax for chat, wiki, and project tracking tools.
- Applies relevance scoring and authority hierarchies to rank retrieved information.
- Provides systematic fallback strategies for handling empty results or unavailable sources.
When to use it
- When an agent needs to retrieve information from multiple disparate enterprise data sources.
- When a user query requires synthesis of information across chat, documentation, and project tasks.
- When an agent needs to resolve ambiguous search requests through targeted clarification.
- When search results need to be ranked according to source authority and query context.
Advantages
- Enables parallel execution to minimize total search time.
- Reduces noise by applying source-specific syntax and filters.
- Improves retrieval accuracy by tailoring search strategies to the query intent.
- Provides a consistent method for handling missing data or source unavailability.
Usage examples
- “What did we decide about the API migration timeline?”
- The agent decomposes the query to search chat for decision signals, the knowledge base for rationale, and project trackers for timeline updates, then synthesizes the findings.
- “What is the status of project aurora?”
- The agent prioritizes recent activity and task trackers, applying status-specific filters to retrieve the most current project information.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/search-strategy/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip search-strategy.zip -d .agents/skills
Documentation by Anthropic
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:
"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 workspaceThen 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 differently:
User: "Kubernetes setup"
Queries: "Kubernetes", "k8s", "cluster", "container orchestration"Source-Specific Query Translation
~~chat
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 |
~~knowledge base (Wiki)
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)~~project tracker
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" |
type:milestone | resource_subtype: "milestone" |
Result Ranking
Relevance Scoring
Score each result on these factors (weighted by query type):
| Factor | Weight (Decision) | Weight (Status) | Weight (Document) | Weight (Factual) |
|---|---|---|---|---|
| Keyword match | 0.3 | 0.2 | 0.4 | 0.3 |
| Freshness | 0.3 | 0.4 | 0.2 | 0.1 |
| Authority | 0.2 | 0.1 | 0.3 | 0.4 |
| Completeness | 0.2 | 0.3 | 0.1 | 0.2 |
Authority Hierarchy
Depends on query type:
For factual/policy questions:
Wiki/Official docs > Shared documents > Email announcements > Chat messagesFor "what happened" / decision questions:
Meeting notes > Thread conclusions > Email confirmations > Chat messagesFor status questions:
Task tracker > Recent chat > Status docs > Email updatesHandling Ambiguity
When a query is ambiguous, prefer asking one focused clarifying question over guessing:
Ambiguous: "search for the migration"
→ "I found references to a few migrations. Are you looking for:
1. The database migration (Project Phoenix)
2. The cloud migration (AWS → GCP)
3. The email migration (Exchange → O365)"Only ask for clarification when:
There are genuinely distinct interpretations that would produce very different results
The ambiguity would significantly affect which sources to search
Do NOT ask for clarification when:
The query is clear enough to produce useful results
Minor ambiguity can be resolved by returning results from multiple interpretations
Fallback Strategies
When a source is unavailable or returns no results:
Source unavailable: Skip it, search remaining sources, note the gap
No results from a source: Try broader query terms, remove date filters, try alternate keywords
All sources return nothing: Suggest query modifications to the user
Rate limited: Note the limitation, return results from other sources, suggest retrying later
Query Broadening
If initial queries return too few results:
Original: "PostgreSQL migration Q2 timeline decision"
Broader: "PostgreSQL migration"
Broader: "database migration"
Broadest: "migration"Remove constraints in this order:
Date filters (search all time)
Source/location filters
Less important keywords
Keep only core entity/topic terms
Parallel Execution
Always execute searches across sources in parallel, never sequentially. The total search time should be roughly equal to the slowest single source, not the sum of all sources.
[User query]
↓ decompose
[~~chat query] [~~email query] [~~cloud storage query] [Wiki query] [~~project tracker query]
↓ ↓ ↓ ↓ ↓
(parallel execution)
↓
[Merge + Rank + Deduplicate]
↓
[Synthesized answer]Files
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
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Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · enterprise-search/skills/search-strategy · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from enterprise-search/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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