Search Strategy

What This Skill Does

This skill intelligently transforms natural language questions into targeted searches across various enterprise sources. It's designed for users who need to find specific information quickly and efficiently. The skill decomposes queries, identifies the query type, and extracts relevant search components.

When to Use

• Determine the best search strategy for a given question.
• Identify the type of query (decision, status, document, etc.).Extract keywords and entities from a search query.
• Target searches to specific sources based on query type.
• Find people working on a specific project.
• Locate the policy on a specific topic.

Key Features

• Classifies the user's question to determine search strategy.
• Extracts keywords, entities, and intent signals from the query.
• Decomposes queries into source-specific searches.
• Prioritizes sources based on query type.
• Identifies constraints such as time ranges and source hints.
• Transforms natural language into targeted searches.

> 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 workspace

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:

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:<@User ID> aurora"

Filter mapping:

~~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:

Result Ranking

Relevance Scoring

Score each result on these factors (weighted by query type):

Authority Hierarchy

Depends on query type:

For factual/policy questions: Wiki/Official docs > Shared documents > Email announcements > Chat messages

For "what happened" / decision questions: Meeting notes > Thread conclusions > Email confirmations > Chat messages

For status questions: Task tracker > Recent chat > Status docs > Email updates

Handling 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: "Postgre SQL migration Q2 timeline decision" Broader: "Postgre SQL 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]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 s