Extract searchable entities and a concise summary from a document for EverRoom knowledge routing.
| Publisher | NxcoreAI |
| Repository | EverRoom |
| Skill name | entity-extraction |
| Stars | 1.5K |
| Forks | 220 |
| Bundled files | Instructions only |
| Links |
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Everything the model needs lives in the instructions — no extra files to sync.
Published by NxcoreAI on GitHub. Read the source before you install it.
Install the Entity Extraction AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.
TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.
Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.
git clone --depth 1 https://github.com/NxcoreAI/EverRoom.git /tmp/EverRoom
mkdir -p .claude/skills
cp -r /tmp/EverRoom/agents/knowledge/skills/entity-extraction .claude/skills/entity-extractionEnable Entity Extraction in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.
AI skills are plain Markdown instructions rather than provider-specific code, so Entity Extraction is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.
This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.
从资料中抽取人物、项目、主题、长期目标、议题和事件，并概括资料内容。同一实体只保留一个规范叫法，通用词不算实体。kind 只能是指定六类，salience 为 0 到 1，evidence 必须是资料中的短句，最多返回 10 个实体；没有实体时返回空数组。
同时抽取事实记忆：描述实体属性或实体之间关系的明确陈述，必须来自资料原文，不得编造；type 只能是「属性」（单一实体的性质、状态、归属等）或「关系」（两个及以上实体间的关系）；entities 引用本次抽取实体的规范名（最多 4 个）。最多返回 10 条事实；资料中没有明确陈述时返回空数组。
只输出：{"summary":"不超过300字的资料概括","entities":[{"name":"...","kind":"人物|项目|主题|长期目标|议题|事件","salience":0.9,"evidence":"..."}],"facts":[{"content":"...","type":"属性|关系","entities":["..."]}]}。
Extract searchable entities and a concise summary from a document for EverRoom knowledge routing.
Because you install it once and use it with any model. Entity Extraction is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.
Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NxcoreAI/EverRoom/tree/main/agents/knowledge/skills/entity-extraction. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.
Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.
As many as you like. As long as a model supports skills, you can use Entity Extraction with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.
It is published on GitHub by NxcoreAI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.
An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.
A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.
Receiving Code Review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…
Requesting Code Review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Subagent Driven Development
Use when executing implementation plans with independent tasks in the current session
Systematic Debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
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