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knowledgebase

Generic RAG query skill - Retrieve any information from the local knowledge base and generate structured reports, summaries, or Q&A responses. Use for: product comparisons, technical analysis, documentation generation, competitive analysis, benchmark reports, specification queries, or any knowledge base retrieval task.

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open-edge-platform/edge-ai-suites
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2026년 9월 18일 14:13
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SKILL.md
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name
knowledgebase
description
Generic RAG query skill - Retrieve any information from the local knowledge base and generate structured reports, summaries, or Q&A responses. Use for: product comparisons, technical analysis, documentation generation, competitive analysis, benchmark reports, specification queries, or any knowledge base retrieval task.
trigger
When user needs to retrieve any information for any purpose.
user-invocable
true
allow-model-invocation
true
priority
high
# Knowledge Base - Generic ECRAG Information Retrieval & Report Generation Use this skill when the user wants to retrieve information from the local knowledge base and generate structured outputs (reports, summaries, comparisons, documentation). ## 📋 QUICK START - Do This Immediately When user asks a question, **execute the curl-based ecrag wrapper via Bash**: ```javascript Bash({ command: '<SKILL_DIR>/ecrag query "user\'s question here"', description: "Query knowledge base" }) ``` Then parse the output and present it to the user. That's it! **Example:** User asks "What is Intel Core Ultra 358H?" **Your immediate action:** ```javascript Bash({ command: '<SKILL_DIR>/ecrag query "What is Intel Core Ultra 358H?"', description: "Query KB for Intel Core Ultra 358H" }) // Wait for output, then present results to user ``` ## 🚨 CRITICAL EXECUTION REQUIREMENT 🚨 **YOU MUST EXECUTE COMMANDS USING THE BASH TOOL - DO NOT JUST DESCRIBE THEM!** To query the knowledge base, you MUST: 1. Use the **Bash tool** to execute `<SKILL_DIR>/ecrag query "your question"` 2. Wait for the command to complete and get the output 3. Parse the output and present it to the user Example Bash tool call: ``` Bash( command: '<SKILL_DIR>/ecrag query "What is Intel Core Ultra 358H?"', description: "Query knowledge base for Intel Core Ultra 358H specifications" ) ``` ## Core Principles ⚠️ **ALWAYS use Bash tool to execute `<SKILL_DIR>/ecrag` commands** ⚠️ ⚠️ **Primary information source is `<SKILL_DIR>/ecrag` output via Bash tool** ⚠️ ⚠️ **Wait for Bash execution to complete before proceeding** ⚠️ ⚠️ **Monitor long-running commands with session ID until `completed`** ⚠️ The main session can handle simple queries directly. For complex tasks, launch sub-agents. ## Core Tool ### curl-based ecrag Wrapper (Execute via Bash Tool) Located at `<SKILL_DIR>/ecrag`, this shell script calls the ECRAG HTTP API directly with `curl`. It does not require Python, a virtual environment, the `ecrag` CLI package, or `jq`. **CRITICAL: You MUST use the Bash tool to execute these commands!** #### Usage via Bash Tool ```javascript // Simple query (uses 'rag' mode by default) Bash({ command: '<SKILL_DIR>/ecrag query "your question here"', description: "Query knowledge base" }) ``` Optional query settings: ```bash <SKILL_DIR>/ecrag query "your question" --mode rag --top-n 5 --max-tokens 512 ``` Modes: - `rag` (default): full retrieval and generation through `POST /v1/chatqna` on the mega service - `retrieve`: context retrieval only through `POST /v1/retrieval` - `mega`: alias for the full ChatQnA request Connection settings are configured with environment variables: ```bash export ECRAG_HOST="http://localhost" export ECRAG_PORT="16010" export ECRAG_MEGA_PORT="16011" export ECRAG_CONNECT_TIMEOUT="10" ``` > [!IMPORTANT] > > - `curl` must be available in `PATH` > - ALWAYS use Bash tool to execute commands > - Script execution takes time - wait for output > - For long-running commands, check status with session ID > - Never skip execution - always run the command!
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