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Building Agent Systems

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telagod
building-agent-systems

AI agent and LLM system engineering reference covering single-agent dev (ReAct, tool calling, plan-execute), multi-agent coordination (swarm, role decomposition, file locking), LLM security (prompt injection, jailbreak defense, output filtering), RAG architecture (chunking, hybrid retrieval, rerank), and prompt engineering / evaluation (RAGAS, LLM-as-Judge). Use when building AI agents, designing RAG pipelines, orchestrating multi-agent workflows, hardening LLM apps, or writing prompts.

Overview

Publishertelagod
Repositorycode-abyss
Skill namebuilding-agent-systems
Stars
242
Forks
33
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by telagod on GitHub. Read the source before you install it.

Installation

Install the Building Agent Systems AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/telagod/code-abyss/tree/main/skills/building-agent-systems
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/telagod/code-abyss.git /tmp/code-abyss
mkdir -p .claude/skills
cp -r /tmp/code-abyss/skills/building-agent-systems .claude/skills/building-agent-systems
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Building Agent Systems 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.

The model loads Building Agent Systems on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Building Agent Systems 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.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

丹鼎秘典 · Agent / LLM 工程

判断先于执行:决定「是否做 / 选什么 / 如何取舍」(栈、方案、架构、权衡)前,先读领域判断内核 skills/_kernel/ml/SKILL.md——它管 judgment,本秘典管 execution;冲突时以内核判断为准。

单 Agent 是器,多 Agent 是阵。先选规模,再选模式。

路由

意图加载核心
单 Agent 开发(工具调用、ReAct)agent-devReAct / Plan-Execute / Reflection
多 Agent 协同(>=3 文件 or >=2 并行)multi-agent-coordination蚁群仿生、文件锁、依赖图
多 Agent 协议细节(消息素、收阵报告)multi-agent-protocolCodex 原生协议、角色定义
LLM 安全(注入、越狱、输出过滤)llm-securityOWASP LLM Top 10 视角
RAG 系统(向量、检索、重排)rag-systemChunking / 混合检索 / Cohere rerank
Prompt + 评估prompt-and-evalFew-shot / CoT / RAGAS / LLM-as-Judge

规模决策

单步任务(一文件、一查询)         → 直接执行(不需要 Agent 框架)
多步任务(计划 + 工具)             → 单 Agent (ReAct)
复杂任务(>5 步、需反思)           → 单 Agent (Plan-Execute / Reflection)
独立并行任务(>=3 文件、>=2 流)    → 多 Agent (TeamCreate)
跨域协作(角色明确)                → 多 Agent (角色分工)

犹豫时优先 TeamCreate — 串行降级容易,并行升级难。

通用原则

Prompt 即代码须版控 | 输入输出皆验证 | 成本效果平衡 | 持续评估迭代 | 安全边界明确

跨场景铁律

  1. Prompt 版控 — Prompt 是代码,必须 Git;变更要走 review
  2. I/O 验证 — 输入侧防注入,输出侧防 hallucination 落地(结构化 schema、引用追溯)
  3. 评估前置 — 上线前必有 eval set;RAGAS / LLM-as-Judge 至少二选一
  4. 成本观测 — token / latency / 失败率必埋点;预算阈值自动告警
  5. 降级路径 — 多 Agent 失败 → 单 Agent;单 Agent 失败 → 直接回答 + 标记 [unverified]

多 Agent 启用判据

信号启用 TeamCreate
涉及 ≥3 独立文件✅
需 ≥2 并行流✅
总步骤 >10✅
用户明确要求✅
单一探索任务❌(用 explorer 或单 Agent)
单文件改动❌(用 worker 或直接执行)
单步任务❌(直接执行)

详细生命周期、文件锁规则、依赖感知、过载保护、降级链:multi-agent-coordination.md

与其他 skill 联动

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Building Agent Systems AI skill do?

AI agent and LLM system engineering reference covering single-agent dev (ReAct, tool calling, plan-execute), multi-agent coordination (swarm, role decomposition, file locking), LLM security (prompt injection, jailbreak defense, output filtering), RAG architecture (chunking, hybrid retrieval, rerank), and prompt engineering / evaluation (RAGAS, LLM-as-Judge). Use when building AI agents, designing RAG pipelines, orchestrating multi-agent workflows, hardening LLM apps, or writing prompts.

Why use Building Agent Systems on TypingMind?

Because you install it once and use it with any model. Building Agent Systems 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.

How do I install Building Agent Systems in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/telagod/code-abyss/tree/main/skills/building-agent-systems. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Building Agent Systems?

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.

How many AI models can I use with Building Agent Systems?

As many as you like. As long as a model supports skills, you can use Building Agent Systems with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Building Agent Systems AI skill free?

Yes. It is published on GitHub by telagod under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

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.

How are AI skills different from plugins or MCP servers?

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.

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