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name: search-strategy description: > Plan and execute a multi-source search strategy for complex queries that require searching across multiple enterprise tools with different query syntaxes, filters, and relevance models. Decomposes broad questions into targeted sub-queries and merges results into a ranked answer set. TRIGGER when: user asks to plan a search, needs help finding something complex, wants a search strategy, asks "how should I search for X", or has a multi-faceted research question. argument-hint: " [--sources SOURCE1,SOURCE2]" user-invocable: true

Multi-Source Search Strategy

Plan, execute, and refine search strategies for complex enterprise queries that span multiple tools and require iterative refinement.

Strategy Planning Process

Step 1: Analyze the Query

Break down the user's question into searchable components:

Component	Description	Example
Core concepts	Primary topics or entities	"authentication", "SSO"
Qualifiers	Narrowing attributes	"for mobile app", "in Q3"
Entity types	People, projects, teams, dates	"by security team", "Project X"
Intent	What the user actually needs	Decision history, how-to, status
Time constraints	Recency requirements	"last month", "current"

Step 2: Select Target Sources

Evaluate which sources are most likely to contain relevant results:

Source Type	Best For	Query Style
Email	Decisions, approvals, external communication	People + topic
Chat / Slack	Quick decisions, links, informal context	Channel + keywords
Documents	Specs, proposals, formal write-ups	Title + content
Wiki	Processes, architecture, onboarding, policies	Topic + category
Tickets / Issues	Implementation details, bugs, requirements	Labels + text
Code	Technical implementation, comments, configs	Symbol + file path
Calendar	Meeting context, attendees, scheduling	People + date range

Source selection matrix:

Query Intent	Primary Sources	Secondary Sources
"How does X work?"	Wiki, Docs, Code	Tickets, Chat
"What was decided?"	Email, Chat, Docs	Tickets, Wiki
"Who is responsible?"	Tickets, Wiki, Chat	Email, Calendar
"What is the status?"	Tickets, Chat, Email	Docs, Calendar
"When did X happen?"	Email, Chat, Tickets	Docs, Calendar
"Why was X done?"	Email, Chat, Docs	Tickets, Code

Step 3: Formulate Sub-Queries

Transform the original query into source-specific sub-queries:

Original: "Why did we switch from Redis to DynamoDB for session storage?"

Sub-queries:
1. Wiki     → "session storage" OR "DynamoDB" OR "Redis migration"
2. Docs     → "session" AND ("DynamoDB" OR "Redis") — type:proposal OR type:design-doc
3. Email    → "session storage" OR "Redis replacement" — from:engineering-leads
4. Chat     → "DynamoDB sessions" OR "Redis sessions" — channels:#backend,#architecture
5. Tickets  → labels:infrastructure "session" AND ("Redis" OR "DynamoDB")
6. Code     → path:*session* OR path:*config* "dynamodb" — recent commits

Step 4: Define Execution Plan

# Search Execution Plan

## Query: [original query]
## Decomposition:
- Concept A: [term variations and synonyms]
- Concept B: [term variations and synonyms]
- Filter: [time range, people, teams]

## Execution Order:
1. [High-priority source] — [specific query] — Expected: [what we hope to find]
2. [Medium-priority source] — [specific query] — Expected: [what we hope to find]
3. [Lower-priority source] — [specific query] — Expected: [what we hope to find]

## Refinement Triggers:
- If Step 1 finds [X], narrow Step 2 to [Y]
- If Step 1 finds nothing, broaden Step 2 to [Z]
- If conflicting results, add Step 4: [verification query]

Step 5: Execute and Merge Results

Result Ranking Framework

Score each result on four dimensions:

Dimension	Weight	Criteria
Relevance	40%	How directly it answers the query
Authority	25%	Source reliability, author expertise, formality level
Recency	20%	How current the information is
Uniqueness	15%	Does it add information not found in other results

Composite score: (Relevance * 0.4) + (Authority * 0.25) + (Recency * 0.2) + (Uniqueness * 0.15)

Output Format

# Search Strategy Report
Query: [original question]
Sources searched: [N] | Results found: [N] | Top results: [N]

## Search Plan Executed
| Step | Source   | Query Used                    | Results |
|------|----------|-------------------------------|---------|
| 1    | [Source] | [query]                       | [N]     |
| 2    | [Source] | [query]                       | [N]     |

## Top Results (Ranked)
### 1. [Result Title] — [Source] — Score: [X/10]
- **Relevance**: [why this matches]
- **Key excerpt**: "[relevant quote]"
- **Date**: [date] | **Author**: [author]

### 2. [Result Title] — [Source] — Score: [X/10]
- **Relevance**: [why this matches]
- **Key excerpt**: "[relevant quote]"
- **Date**: [date] | **Author**: [author]

## Answer Summary
[Synthesized answer based on top results]

## Search Refinement Suggestions
- To find more: try [broader query suggestion]
- To narrow down: add [filter suggestion]
- Unexplored sources: [sources not yet queried and why they might help]

Query Optimization Techniques

Technique	When to Use	Example
Synonym expansion	Initial search yields few results	"auth" → "authentication", "login"
Phrase matching	Too many irrelevant results	"session storage" (exact phrase)
Author filtering	Known domain experts exist	from:jane.doe OR from:john.smith
Date narrowing	Topic changed over time	after:2025-01-01
Negative filtering	Known false positives	NOT "session recording"
Channel scoping	Known relevant channels	in:#backend-eng
Label/tag filtering	Structured metadata available	label:architecture, tag:approved

Iterative Refinement

After initial results, determine if refinement is needed:

• Too many results (>50): Add filters, use phrase matching, narrow date range
• Too few results (<3): Expand synonyms, broaden date range, add sources
• Wrong results: Analyze why results are off-topic, add negative filters
• Partial answer: Identify the gap and formulate a targeted follow-up query

Edge Cases

• Ambiguous queries: Ask the user to clarify before executing; present 2-3 interpretations
• No results anywhere: Suggest the information may not exist in connected sources; recommend who to ask
• Cross-language content: Note if sources contain content in multiple languages; expand queries accordingly
• Acronyms and jargon: Expand acronyms in queries and search for both forms
• Stale indexes: If a source's index is known to be delayed, warn about potential missing recent items

Quality Checklist

• Query decomposed into at least 2 sub-queries
• At least 3 sources considered
• Results ranked by composite score
• Conflicting information flagged
• Knowledge gaps identified
• Refinement suggestions provided