knowledge-query

name	knowledge-query
description	Query CA knowledge graph before answering knowledge questions. ONLY for concept explanations. MUST extract core topic (2-10 words), NOT entire sentences. MUST NOT use for commands, code, debugging, tasks.

knowledge-query

Query CA (Curious Agent) knowledge graph to check confidence level before answering knowledge questions.

⚠️ CRITICAL: Topic Extraction Rules

YOU MUST extract the CORE topic, NOT the full user sentence.

User Message	WRONG Topic ❌	RIGHT Topic ✅
"什么是agent上下文管理系统？"	"什么是agent上下文管理系统？"	"agent上下文管理系统"
"解释一下transformer attention机制"	"解释一下transformer attention机制"	"transformer attention"
"agent记忆机制是怎么实现的？"	"agent记忆机制是怎么实现的？"	"agent记忆机制"
"帮我看看RAG和vector search的区别"	"帮我看看RAG和vector search的区别"	"RAG" 或 "vector search"

Topic Format Requirements:

• Length: 2-10 words (noun phrases, not questions)
• Language: Chinese or English keywords only
• No question words: 不要包含"什么是"、"解释"、"怎么"、"帮我"等
• No command words: 不要包含"检查"、"commit"、"push"、"debug"等

✅ WHEN TO USE (Must Invoke)

Invoke this skill ONLY when user asks about:

1. 概念解释: "什么是 X", "X 是什么", "解释 X", "X 的原理"
2. 技术对比: "X 和 Y 的区别", "比较 X 和 Y"
3. 机制说明: "X 是怎么工作的", "X 的实现原理"
4. 名词询问: 用户提到不熟悉的技术术语

Example valid triggers:

• "什么是 FlashAttention？"
• "agent memory 有哪些类型？"
• "RAG 和 semantic search 有什么区别？"

❌ MUST NOT USE (Skip This Skill)

NEVER invoke this skill for:

Category	Examples	Reason
命令执行	"ls", "git status", "npm install", "重启服务"	These are operations, not knowledge
代码编写	"写一个函数", "帮我写代码", "实现 X 功能"	Code generation, not explanation
问题调试	"为什么报错", "debug 这个问题", "检查一下"	Troubleshooting, not knowledge
任务执行	"commit", "push", "创建 PR", "部署"	Task execution, not explanation
日常对话	"好的", "谢谢", "继续"	No knowledge query needed
文件操作	"读一下这个文件", "修改配置"	File operations, not knowledge

If user's intent is ANY of the above → SKIP this skill entirely.

How to Use

Step 1: Extract Core Topic

Before calling the script, extract the core topic from user's message:

# 思考过程示例:
# User: "什么是agent上下文管理系统？"
# 1. 剔除问题词: "什么是" → 剩余 "agent上下文管理系统"
# 2. 检查长度: 7个字 → 合格 (2-10 words)
# 3. 确认是名词短语 → 合格
# → Topic = "agent上下文管理系统"

Step 2: Run Query Script

python3 /root/.openclaw/skills/knowledge-query/scripts/query.py "<extracted_topic>"

Script path alternatives (if not installed):

• CA project: /root/dev/curious-agent/openclaw-skills/knowledge-query/scripts/query.py

Step 3: Process Output

Script returns JSON:

{
  "success": true,
  "output": "[KG Context — Expert (85%)]\n话题: agent上下文管理系统\n置信度: 0.85\nKG 有完整知识，可直接回答。",
  "metadata": {"topic": "...", "confidence": 0.85, "level": "expert", "gaps": []}
}

Read the output field and follow guidance:

Level	Confidence	Your Action
🟢 Expert	≥85%	Answer from KG knowledge directly, cite sources
🟡 Intermediate	60-85%	Use KG knowledge + supplement with web search
🟠 Beginner	30-60%	Search first, then answer with combined knowledge
🔴 Novice	<30%	Answer from LLM, optionally inject topic to CA for exploration

Execution Checklist

Before invoking, verify ALL conditions:

[ ] User asks about a concept/explanation/mechanism (NOT command/code/debug)
[ ] Extracted topic is 2-10 words (noun phrase, no question words)
[ ] Topic is NOT a command keyword (git, commit, ls, debug, etc.)

If ANY condition fails → SKIP this skill and proceed directly.

Requirements

• CA API running on localhost:4848 (env CA_API_URL overrides)
• Script handles errors gracefully (returns warning if API down)
• Timeout: 2 seconds (non-blocking)

Error Handling

If success: false:

• Log: [KG Context — 不可用] CA API 无响应，跳过知识查询。
• Proceed with normal answer (do NOT retry, do NOT block)