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.
Best use case
search-strategy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using search-strategy should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/search-strategy/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How search-strategy Compares
| Feature / Agent | search-strategy | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Search Strategy
> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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:
| 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 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:
1. **Source unavailable**: Skip it, search remaining sources, note the gap
2. **No results from a source**: Try broader query terms, remove date filters, try alternate keywords
3. **All sources return nothing**: Suggest query modifications to the user
4. **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:
1. Date filters (search all time)
2. Source/location filters
3. Less important keywords
4. 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]
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