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query-writing
Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
Productionize LangGraph & ADK agents.
Self-hosted. Open source. No vendor lock-in.
Cloud · Try the demos · Quickstart · Docs · Discord · Book a demo
⭐ If you find this useful, please star the repo. It helps others discover the project.
Why Idun? Teams building agents face a tradeoff: build the production wrapper yourself (FastAPI + traces + guardrails + admin UI — slow), or adopt a SaaS like LangGraph Cloud or LangSmith (sovereignty trade-off). Idun is the third path: pip install a self-sufficient FastAPI process that bundles your agent with chat UI, admin, traces, and guardrails — all open source, all on your infrastructure.
Quick start
Prerequisites: Python 3.12 or 3.13.
Create a new directory and install the engine:
mkdir my-agent && cd my-agent
pip install idun-agent-engine langgraph langchain-google-genai
Save the three files below inside my-agent/.
my-agent/agent.py
from typing import Annotated, TypedDict
from idun_agent_engine.mcp import get_langchain_tools_sync
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
class State(TypedDict):
messages: Annotated[list, add_messages]
tools = get_langchain_tools_sync()
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash").bind_tools(tools)
def chatbot(state: State):
return {"messages": [llm.invoke(state["messages"])]}
graph = StateGraph(State)
graph.add_node("chatbot", chatbot)
graph.add_node("tools", ToolNode(tools))
graph.add_edge(START, "chatbot")
graph.add_conditional_edges("chatbot", tools_condition)
graph.add_edge("tools", "chatbot")
my-agent/config.yaml
server:
api:
port: 8000
agent:
type: LANGGRAPH
config:
name: "my-agent"
graph_definition: "./agent.py:graph"
checkpointer:
type: sqlite
db_url: "sqlite:///conversations.db"
mcp_servers:
- name: idun-docs
transport: streamable_http
url: https://docs.idun-group.com/mcp
my-agent/.env — get a Gemini key at aistudio.google.com/apikey:
GEMINI_API_KEY=your-key-here
From inside my-agent/, run:
idun init
graph_definition: "./agent.py:graph"is resolved against the directory where you runidun init. Stay insidemy-agent/when you launch it, or use an absolute path.
A browser opens at http://localhost:8000 for the chat UI. The agent already has tools from the Idun docs MCP wired in. Visit /admin to configure more MCP servers, managed prompts, guardrails, observability, messaging integrations, and SSO. Visit /admin/traces for the trace store.
What
pip install idun-agent-enginedeliversA single wheel that bundles three Python packages:
idun_agent_engine— the engine SDK (FastAPI app factory, MCP registry, observability, guardrails).idun_agent_standalone— theidunCLI, the admin REST API, and the chat / admin / traces UIs.idun_agent_schema— the Pydantic config schemas.The
iduncommand on your$PATH(setup,serve,init,hash-password,agent serve) is provided by the bundled standalone — it is not a separately published package. You won't seeidun-agent-standalonein the engine's declared PyPI dependencies because it ships co-installed inside the wheel, not as a transitive dep.See docs.idun-group.com/architecture for the full layering.
What's inside
Observability
Langfuse · Arize Phoenix · LangSmith · GCP Trace · GCP Logging
Trace every agent run. Connect multiple providers at the same time through config.
Guardrails
PII detection · Toxic language · Ban lists · Topic restriction · Bias checks · NSFW · 9 more
Apply policies per agent on input, output, or both. Powered by Guardrails AI.
MCP tool governance
Register MCP servers and control which tools each agent can access. Supports stdio, SSE, streamable HTTP, and WebSocket.
Memory and persistence
PostgreSQL · SQLite · In-memory · Vertex AI · ADK Database
Conversations persist across restarts. Pick a backend per agent.
Prompt management
Versioned templates with Jinja2 variables. Assign prompts to agents from the UI or API.
[!NOTE] AG-UI streaming — Every agent gets a standards-based streaming API, compatible with CopilotKit clients. Built-in chat playground for testing.
Architecture
Idun ships as one process: idun-agent-standalone. It bundles the engine SDK, a Next.js chat UI, an admin panel, and a traces viewer — your agent runs inside this process, configured from a YAML file and re-loaded live from the admin REST.
flowchart LR
subgraph Idun["idun-agent-standalone (one process)"]
direction TB
UI["Chat UI / Admin / Traces"] --> ENG["Engine SDK"]
ENG --> DB[(Postgres / SQLite)]
end
Users --
// HOW IT'S BUILT
KEY FILES