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airweave-search

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npx machina-cli add skill airweave-ai/claude-plugin/airweave-search --openclaw
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SKILL.md
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Airweave Search

Use this skill to effectively search and retrieve context from Airweave collections, whether answering questions or gathering context to complete tasks.

When to Search

Search when the user:

  • Asks about data in their connected apps ("What did we discuss in Slack about...")
  • Needs to find documents, messages, issues, or records
  • Asks factual questions about their workspace ("Who is responsible for...", "What's our policy on...")
  • References specific tools by name ("in Notion", "on GitHub", "in Jira")
  • Needs recent information you don't have in your training
  • Needs you to check app data for context to complete a task ("check our Notion docs", "look at the Jira ticket", "see what we decided in Slack")

Don't search when:

  • User asks general knowledge questions (use your training)
  • User is asking how to SET UP Airweave (use airweave-setup skill instead)
  • User already provided all needed context in the conversation
  • The question is about Airweave itself, not data within it

Query Formulation

Extract Key Concepts

Turn user intent into effective search queries:

User SaysSearch Query
"What did Sarah say about the launch?""Sarah product launch"
"Find the API documentation""API documentation"
"Any bugs reported this week?""bug report issues"
"What's our refund policy?""refund policy customer"

Query Tips

  1. Use natural language - Airweave uses semantic search, not keyword matching
  2. Include context - "pricing feedback" is better than just "pricing"
  3. Be specific but not too narrow - Start moderately specific, broaden if no results
  4. Avoid filler words - Skip "please find", "can you search for"

Parameter Selection

Choose parameters based on user intent:

User IntentParameters
Recent updates/conversationsrecency_bias: 0.7-0.9
Finding a specific documentsearch_method: "keyword" or "hybrid"
General topic explorationsearch_method: "hybrid", higher limit
High-quality results onlyenable_reranking: true
Quick direct answerresponse_type: "completion"
Browse/see all matchesresponse_type: "raw", limit: 20-50

Parameter Quick Reference

ParameterValuesWhen to Use
recency_bias0-1Higher = favor recent. Use 0.7+ for "recent", "latest", "this week"
search_methodhybrid/neural/keywordkeyword for exact terms, neural for concepts, hybrid for both
response_typeraw/completioncompletion for direct answers, raw to show sources
limit1-1000Lower (5-10) for quick answers, higher (20-50) for exploration
enable_rerankingbooleantrue for better relevance (slightly slower)
expansion_strategyauto/llm/no_expansionauto for most cases, no_expansion for exact queries

See PARAMETERS.md for detailed guidance.

Handling Results

Interpreting Scores

ScoreMeaningAction
0.85+Highly relevantUse confidently
0.70-0.85Likely relevantUse with context
0.50-0.70Possibly relevantMention uncertainty
Below 0.50Weak matchConsider rephrasing query

Synthesizing Answers

When presenting results to users:

  1. Lead with the answer - Don't start with "I found 5 results"
  2. Cite sources - Mention where info came from ("According to your Slack conversation...")
  3. Synthesize, don't dump - Combine relevant parts into coherent response
  4. Acknowledge gaps - If results don't fully answer, say so

Handling No/Poor Results

If search returns no results or low-quality matches:

  1. Broaden the query - Remove specific terms, use more general concepts
  2. Try different phrasing - Rephrase using synonyms or related terms
  3. Increase limit - Fetch more results to find relevant matches
  4. Check source availability - The data source might not be connected
  5. Ask for clarification - User might have more context to share

Finding the Search Tool

Airweave MCP tools follow the naming pattern search-{collection-name}. Look for tools matching this pattern in your available MCP tools.

Examples:

  • search-acmes-slack-k8v2x1
  • search-acmes-notion-p3m9q7
  • search-acmes-jira-w5n4r2

If no Airweave search tool is available:

  • The user may not have Airweave MCP configured
  • Ask if they have Airweave set up and connected to their AI assistant
  • Suggest using the airweave-setup skill for configuration help

Multiple collections: If multiple search-* tools are available, choose based on the collection name and the user's request. If unclear which to use, ask the user or try the most general-sounding one first.

Calling the Search Tool

Use the search-{collection} MCP tool with your chosen parameters:

search-acmes-slack-k8v2x1({
  query: "customer feedback pricing",
  recency_bias: 0.7,
  limit: 10
})
search-acmes-notion-p3m9q7({
  query: "API authentication docs",
  search_method: "hybrid",
  enable_reranking: true
})
search-acmes-jira-w5n4r2({
  query: "What is our refund policy?",
  response_type: "completion"
})

Examples

See EXAMPLES.md for complete conversation examples showing effective search patterns.

Source

git clone https://github.com/airweave-ai/claude-plugin/blob/main/skills/airweave-search/SKILL.mdView on GitHub

Overview

Airweave-search lets you locate documents, messages, issues, and records stored in Airweave collections across connected apps like Slack, GitHub, Notion, Jira, Confluence, Google Drive, Salesforce, and databases. It empowers you to answer questions using your company data and to gather context needed to complete tasks.

How This Skill Works

The skill translates user intent into semantic search queries against Airweave collections, applying parameters such as recency_bias, search_method, and limit to balance relevance and scope. It then returns matched items along with source citations, and you present a concise answer synthesized from the results.

When to Use It

  • You need information from data in connected apps (e.g., Slack conversations about a launch).
  • You want to locate documents, messages, issues, or records across Airweave collections.
  • You need factual information about your workspace or policy, derived from company data.
  • You must reference a specific tool by name (Notion, GitHub, Jira) to locate context.
  • You require recent information not present in training or to check app data for context to complete a task.

Quick Start

  1. Step 1: Identify the data you need from connected apps (Slack, Notion, Jira, etc.).
  2. Step 2: Phrase a natural-language query and set parameters (recency_bias, limit, response_type).
  3. Step 3: Review the cited sources and synthesize a concise, source-backed answer.

Best Practices

  • Turn user intent into effective search queries rather than generic terms.
  • Include context in the prompt, e.g., 'pricing policy in Slack' rather than just 'pricing'.
  • Use natural language and avoid filler words to improve semantic search results.
  • Choose parameters thoughtfully (recency_bias, search_method, limit) to balance freshness and depth.
  • Lead with a concise answer and cite sources from Airweave results.

Example Use Cases

  • What did we discuss in Slack about the Q3 launch? (Airweave returns relevant Slack messages with citations.)
  • Show me the latest Notion doc on onboarding.
  • What's our refund policy for customers? (Searches company docs and cites the source.)
  • Find the API documentation for our integration in GitHub.
  • See who is responsible for the security policy in Confluence or related Jira tickets.

Frequently Asked Questions

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