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- 310 registry:skill items, 1528 files, 7 categories (hub tags) - registry.json generated by @kortix/registry buildRegistry - nested SKILL.md in sn-da-* renamed SUBSKILL.md (no phantom skills) - provenance + licenses: SOURCES.md
223 lines
7.0 KiB
Markdown
223 lines
7.0 KiB
Markdown
---
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name: search-strategy
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description: 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.
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user-invocable: false
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---
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# Search Strategy
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> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
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The core intelligence behind enterprise search. Transforms a single natural language question into parallel, source-specific searches and produces ranked, deduplicated results.
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## The Goal
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Turn this:
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```
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"What did we decide about the API migration timeline?"
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```
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Into targeted searches across every connected source:
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```
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~~chat: "API migration timeline decision" (semantic) + "API migration" in:#engineering after:2025-01-01
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~~knowledge base: semantic search "API migration timeline decision"
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~~project tracker: text search "API migration" in relevant workspace
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```
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Then synthesize the results into a single coherent answer.
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## Query Decomposition
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### Step 1: Identify Query Type
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Classify the user's question to determine search strategy:
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| Query Type | Example | Strategy |
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|-----------|---------|----------|
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| **Decision** | "What did we decide about X?" | Prioritize conversations (~~chat, email), look for conclusion signals |
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| **Status** | "What's the status of Project Y?" | Prioritize recent activity, task trackers, status updates |
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| **Document** | "Where's the spec for Z?" | Prioritize Drive, wiki, shared docs |
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| **Person** | "Who's working on X?" | Search task assignments, message authors, doc collaborators |
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| **Factual** | "What's our policy on X?" | Prioritize wiki, official docs, then confirmatory conversations |
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| **Temporal** | "When did X happen?" | Search with broad date range, look for timestamps |
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| **Exploratory** | "What do we know about X?" | Broad search across all sources, synthesize |
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### Step 2: Extract Search Components
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From the query, extract:
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- **Keywords**: Core terms that must appear in results
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- **Entities**: People, projects, teams, tools (use memory system if available)
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- **Intent signals**: Decision words, status words, temporal markers
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- **Constraints**: Time ranges, source hints, author filters
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- **Negations**: Things to exclude
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### Step 3: Generate Sub-Queries Per Source
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For each available source, create one or more targeted queries:
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**Prefer semantic search** for:
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- Conceptual questions ("What do we think about...")
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- Questions where exact keywords are unknown
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- Exploratory queries
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**Prefer keyword search** for:
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- Known terms, project names, acronyms
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- Exact phrases the user quoted
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- Filter-heavy queries (from:, in:, after:)
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**Generate multiple query variants** when the topic might be referred to differently:
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```
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User: "Kubernetes setup"
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Queries: "Kubernetes", "k8s", "cluster", "container orchestration"
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```
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## Source-Specific Query Translation
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### ~~chat
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**Semantic search** (natural language questions):
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```
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query: "What is the status of project aurora?"
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```
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**Keyword search:**
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```
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query: "project aurora status update"
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query: "aurora in:#engineering after:2025-01-15"
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query: "from:<@UserID> aurora"
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```
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**Filter mapping:**
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| Enterprise filter | ~~chat syntax |
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|------------------|--------------|
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| `from:sarah` | `from:sarah` or `from:<@USERID>` |
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| `in:engineering` | `in:engineering` |
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| `after:2025-01-01` | `after:2025-01-01` |
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| `before:2025-02-01` | `before:2025-02-01` |
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| `type:thread` | `is:thread` |
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| `type:file` | `has:file` |
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### ~~knowledge base (Wiki)
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**Semantic search** — Use for conceptual queries:
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```
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descriptive_query: "API migration timeline and decision rationale"
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```
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**Keyword search** — Use for exact terms:
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```
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query: "API migration"
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query: "\"API migration timeline\"" (exact phrase)
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```
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### ~~project tracker
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**Task search:**
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```
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text: "API migration"
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workspace: [workspace_id]
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completed: false (for status queries)
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assignee_any: "me" (for "my tasks" queries)
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```
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**Filter mapping:**
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| Enterprise filter | ~~project tracker parameter |
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|------------------|----------------|
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| `from:sarah` | `assignee_any` or `created_by_any` |
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| `after:2025-01-01` | `modified_on_after: "2025-01-01"` |
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| `type:milestone` | `resource_subtype: "milestone"` |
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## Result Ranking
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### Relevance Scoring
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Score each result on these factors (weighted by query type):
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| Factor | Weight (Decision) | Weight (Status) | Weight (Document) | Weight (Factual) |
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|--------|-------------------|------------------|--------------------|-------------------|
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| Keyword match | 0.3 | 0.2 | 0.4 | 0.3 |
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| Freshness | 0.3 | 0.4 | 0.2 | 0.1 |
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| Authority | 0.2 | 0.1 | 0.3 | 0.4 |
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| Completeness | 0.2 | 0.3 | 0.1 | 0.2 |
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### Authority Hierarchy
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Depends on query type:
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**For factual/policy questions:**
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```
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Wiki/Official docs > Shared documents > Email announcements > Chat messages
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```
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**For "what happened" / decision questions:**
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```
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Meeting notes > Thread conclusions > Email confirmations > Chat messages
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```
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**For status questions:**
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```
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Task tracker > Recent chat > Status docs > Email updates
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```
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## Handling Ambiguity
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When a query is ambiguous, prefer asking one focused clarifying question over guessing:
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```
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Ambiguous: "search for the migration"
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→ "I found references to a few migrations. Are you looking for:
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1. The database migration (Project Phoenix)
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2. The cloud migration (AWS → GCP)
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3. The email migration (Exchange → O365)"
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```
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Only ask for clarification when:
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- There are genuinely distinct interpretations that would produce very different results
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- The ambiguity would significantly affect which sources to search
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Do NOT ask for clarification when:
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- The query is clear enough to produce useful results
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- Minor ambiguity can be resolved by returning results from multiple interpretations
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## Fallback Strategies
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When a source is unavailable or returns no results:
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1. **Source unavailable**: Skip it, search remaining sources, note the gap
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2. **No results from a source**: Try broader query terms, remove date filters, try alternate keywords
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3. **All sources return nothing**: Suggest query modifications to the user
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4. **Rate limited**: Note the limitation, return results from other sources, suggest retrying later
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### Query Broadening
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If initial queries return too few results:
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```
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Original: "PostgreSQL migration Q2 timeline decision"
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Broader: "PostgreSQL migration"
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Broader: "database migration"
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Broadest: "migration"
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```
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Remove constraints in this order:
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1. Date filters (search all time)
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2. Source/location filters
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3. Less important keywords
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4. Keep only core entity/topic terms
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## Parallel Execution
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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.
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```
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[User query]
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↓ decompose
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[~~chat query] [~~email query] [~~cloud storage query] [Wiki query] [~~project tracker query]
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↓ ↓ ↓ ↓ ↓
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(parallel execution)
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↓
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[Merge + Rank + Deduplicate]
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↓
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[Synthesized answer]
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```
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