Agent Templates
DevOps Assistant Agent

DevOps agents that monitor and respond to incidents on-behalf-of your engineers

Connect three real services, delegate OAuth to your users, and ship a working agent in minutes. Clone the sample, swap in your tools, and go from zero to multi-connector in a single afternoon.

DevOps Assistant Agent
Sample Agent for Acme
May 22 · 10:00 AM ·
47s
Alert me on any PRs blocking the deploy
J
Checking GitHub CI and PR status
Check GitHub CI status
github_list_check_runs
Match Linear issues
linear_search_issues
DM engineer in Slack
slack_send_message
DevOps alert: June 15
Deploy blocker (P1)
"PR #482: CI failing for 4h - auth middleware tests"
Linked to Linear issue ENG-291, DM sent to PR author
Result
2 P1 blockers, 3 stale reviews flagged
DMs sent, #engineering channel notified
Message Claude...
Trusted by teams shipping agents to production
DevOps Assistant Agent
Sample Agent for Acme
May 22 · 10:00 AM ·
47s
Alert me on any PRs blocking the deploy
J
Checking GitHub CI and PR status
Check GitHub CI status
github_list_check_runs
Match Linear issues
linear_search_issues
DM engineer in Slack
slack_send_message
DevOps alert: June 15
Deploy blocker (P1)
"PR #482: CI failing for 4h - auth middleware tests"
Linked to Linear issue ENG-291, DM sent to PR author
Result
2 P1 blockers, 3 stale reviews flagged
DMs sent, #engineering channel notified
Message Claude...

How the agent tracks PRs, CI status, and Linear issues to surface DevOps signals

A real working agent you can deploy

This repo uses a single SCALEKIT_USER_ID env var to simulate one user. In production, pass each user's real ID as the identifier on every Scalekit call, and send them an authorization link whenever their connector status is not ACTIVE.

01
Authorize, then orchestrate
main.py
Every connection is authorized once via a one-time link. Scalekit refreshes tokens before each polling cycle. main.py runs continuously, monitors GitHub CI status and PR state, and triggers the Linear + Slack pipeline whenever an alert condition is met.
main.py
import os
from scalekit import ScalekitClient
from langchain.agents import create_tool_calling_agent

client = ScalekitClient(
    env_url=os.environ["SCALEKIT_ENV_URL"],
    client_id=os.environ["SCALEKIT_CLIENT_ID"],
    client_secret=os.environ["SCALEKIT_CLIENT_SECRET"],
)

# LangChain-compatible tools scoped to this user
tools = client.actions.langchain.get_tools(
    identifier="user@example.com",
    connection_names=["github", "linear", "slack"],
)

agent = create_tool_calling_agent(llm, tools, prompt)
02
Monitor GitHub PR and CI state
watch_github.py
03
Correlate with Linear issues
check_linear.py
04
Classify alert severity
classify_alert.py
05
Notify engineer via Slack
notify_slack.py
Why choose Scalekit

Delegated identity. Not service accounts.

Credentials never touch agent code or LLM context. The agent acts as the user, not as a shared bot.
Delegated OAuth - Agent reads your calendar, your inbox — scoped to the authorizing identity, not org-wide.
Credentials outside agent runtime  -  Tokens never touch agent code or LLM context. Both failure modes covered.
Token lifecycle automatic  -  Refresh, expiry, rotation across all connectors. One SDK call. Zero management code.
200+ prebuilt connectors  -  Google, Slack, HubSpot, GitHub, Jira, Notion, Salesforce — same auth pattern everywhere.

Agents that run DevOps monitoring, without the auth plumbing

Three things you'd otherwise build: GitHub OAuth for CI + PR data, Linear token storage, Slack credentials per engineer. Handled.

OAuth flow per connector
One SDK call returns a delegated token for any connector. Google, HubSpot, Slack, same pattern across all 200+ connectors
tools = client.actions.langchain.get_tools(
    identifier=user_id,
    connection_names=["github", "linear", "slack"],
)
agent = create_tool_calling_agent(llm, tools, prompt)
Secure token vault  
Scalekit stores OAuth credentials outside agent code and outside LLM context. Both are separate failure modes. Both covered
client = ScalekitClient(
    env_url=os.environ["SCALEKIT_ENV_URL"],
    client_id=os.environ["SCALEKIT_CLIENT_ID"],
    client_secret=os.environ["SCALEKIT_CLIENT_SECRET"],
)
# Credentials never in agent code or LLM context
Token refresh logic
Token lifecycle handled automatically — expiry, rotation, re-auth — across every connector. Agent runs in 6 months. Same call works
# Day 1 or day 180 - same call works
tools = client.actions.langchain.get_tools(
    identifier=user_id,
    connection_names=["github", "linear", "slack"],
)
Try other Agent Templates

Prebuilt agents you can ship today

Each one runs on delegated identity, scoped per user.

ENGINEERING
DevOps assistant agent
Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.
ENGINEERING
Engineering standup agent
Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.
ENGINEERING
Auto-release notes agent
Group merged GitHub PRs by feature, fix, or chore and publish release notes per tag. No manual changelog grooming.
ENGINEERING
Slack workflow agent (LangGraph)
LangGraph agent that drives multi-step Slack workflows: triggers, approvals, and follow-up actions per user identity.
SUPPORT
Support triage agent
Read Zendesk tickets, fetch runbooks from Notion, and route to the right Slack channel with a drafted response.
OPS
Email-to-calendar scheduling agent
Parse scheduling intent from Gmail threads and create Google Calendar events with the right attendees and timezone.
Customize the sample

Clone it and own it with connectors you choose

Don't sweat the integration. Point a coding agent at the repo. It clones, swaps in your connectors, and adds new steps for you.

1
Install a coding agent
terminal

claude "Set up a DevOps assistant agent using GitHub, Linear, and Slack via Scalekit"

terminal

codex "Set up a DevOps assistant agent using GitHub, Linear, and Slack via Scalekit"

terminal

gh copilot suggest "Set up a DevOps assistant agent using GitHub, Linear, and Slack via Scalekit"

terminal

Open Cursor Composer (Cmd+Shift+I) Paste the prompt from the Prompt tab

terminal

npx skills add scalekit-inc/skills --skill setup-scalekit

2
Give it this prompt

Clone github.com/scalekit-inc/python-connect-demos/langchain. Set connection_names = ["github", "linear", "slack"]. Build a DevOps assistant: poll GitHub for PRs with failing CI or stale reviews, correlate with open Linear issues by branch name, classify alert severity P1-P3 with the LLM, DM the PR author via Slack and post to #engineering. Dedup alerts across polling cycles. Set SCALEKIT_ENV_URL, SCALEKIT_CLIENT_ID, SCALEKIT_CLIENT_SECRET in .env.

Build your own
multi-connector agent

Add connectors. Change the LLM. Same delegated auth pattern.