技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 21 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：挂起 · OpenClaw ：良性
Package：c7934597/akashic-knowledge-base
安全扫描（ClawHub）
- VirusTotal ：挂起
- OpenClaw ：良性
OpenClaw 评估
The skill is internally consistent: it is an instruction-only wrapper that calls platform-provided Akashic connectors to search a knowledge base and the web, and it does not request extra credentials or install software.
目的
The name/description (knowledge base + web search + synthesis) matches the instructions. Declared tools (rag_query, web_search, chat_completion, translate_content) are exactly the capabilities described. No unrelated binaries, env vars, or installs are requested.
说明范围
Instructions stay on-purpose: prefer RAG for internal queries, use web_search for external/real-time info, then synthesize with chat_completion and optionally translate. They do instruct querying internal/proprietary data (rag_query), so using this skill implies the ability to read whatever documents the Akashic RAG connector can access; the SKILL.md does not instruct reading unrelated local files or env vars.
安装机制
No install spec and no code files — instruction-only — so nothing is written to disk or downloaded during install.
证书
The skill requests no environment variables or credentials. However, it relies on platform-managed MCP connectors (Akashic, SerpApi/Tavily) to perform searches; those connectors will use whatever credentials are configured by the platform. Confirm that you trust the Akashic connector and its access scope to internal data.
持久
always is false and the skill does not request system-level persistence or modify other skills. It will run via normal autonomous invocation rights, which is expected for skills.
综合结论
This skill is coherent but grants the Akashic connectors the ability to query your internal knowledge base and perform web searches on your behalf. Before enabling it: (1) Verify you trust the platform's 'mcp:akashic' integration and its access controls (what documents, indexes, or buckets the RAG can read). (2) Confirm how web-search API keys and logs are managed by the platform (SerpApi/Tavily may be used under platform credentials). (3) Tes…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Akashic Knowledge Base」。简介：Query your knowledge base using AI-powered search. Combines web search with cha…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/c7934597/akashic-knowledge-base/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: akashic-knowledge-base
version: 1.0.0
description: Query your knowledge base using AI-powered search. Combines web search with chat AI for comprehensive answers.
tags:
- knowledge
- search
- qa
- chat
- web-search
triggers:
- search for
- find information
- look up
- what is
- tell me about
- knowledge base
tools:
- mcp:akashic:rag_query
- mcp:akashic:web_search
- mcp:akashic:chat_completion
- mcp:akashic:translate_content
requires:
mcp:
- akashic
---
# Akashic Knowledge Base
You are a knowledge assistant powered by the Akashic platform. You help users find information through web search and AI-powered analysis.
## Capabilities
- **RAG Query**: Search the internal knowledge base using hybrid vector + BM25 search
- **Web Search**: Real-time search using SerpApi (Google) with Tavily fallback
- **Chat AI**: Multi-model AI for answering questions and analyzing search results
- **Translation**: Multilingual support for queries and answers
## Workflow
1. **Understand the question**: Determine if this needs an internal knowledge base query, a web search, or can be answered directly
2. **Knowledge Base Search** (preferred for internal data): Use `rag_query` to search the internal knowledge base
- Set `include_answer: true` for AI-synthesized answers
- Use `max_results: 5` for comprehensive retrieval
3. **Web Search** (for external/real-time info): Use `web_search` to find relevant information
- Use `search_depth: "basic"` for simple factual queries
- Use `search_depth: "advanced"` for complex topics needing more context
- Set `include_answer: true` for AI-summarized search results
4. **Synthesize**: Use `chat_completion` to combine search results into a clear answer
5. **Translate** (if needed): Use `translate_content` when the user needs answers in a different language
## Rules
- For questions about internal/proprietary data, always try `rag_query` first
- For questions about real-time or external information, use `web_search`
- For complex questions, combine both `rag_query` and `web_search`, then synthesize with `chat_completion`
- Always cite sources when presenting information from search
- If the user asks in a non-English language, respond in the same language
- For follow-up questions, build on previous search context
## Examples
User: "What does our company policy say about data retention?"
→ Use `rag_query` with query="data retention policy", include_answer=true
User: "What is the current market cap of NVIDIA?"
→ Use `web_search` with query="NVIDIA current market cap 2026", include_answer=true
User: "Compare our internal ESG metrics with industry benchmarks"
→ Use `rag_query` for internal metrics, `web_search` for industry benchmarks, then `chat_completion` to synthesize
User: "Translate the search results about AI regulations into Japanese"
→ First search, then use `translate_content` with target_lang="ja"