Agent Templates
Auto Release Notes Agent

Release notes agents that document every deploy on-behalf-of your engineers

Connect two 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.

Auto Release Notes Agent
Sample Agent for Acme
May 22 · 10:00 AM ·
47s
Generate release notes for v2.4.0
J
Fetching PRs merged since v2.3.0
Fetch merged PRs
github_list_pull_requests
Categorize with LLM
llm_categorize_changes
Post to Slack + GitHub
slack_send_message
Release notes: v2.4.0
Summary (34 PRs)
"8 features, 14 bug fixes, 2 breaking changes"
GitHub release updated, posted to #releases
Result
34 PRs categorized, 2 flagged for human review
Release notes posted to Slack and GitHub in <30s
Message Claude...
Trusted by teams shipping agents to production
Auto Release Notes Agent
Sample Agent for Acme
May 22 · 10:00 AM ·
47s
Generate release notes for v2.4.0
J
Fetching PRs merged since v2.3.0
Fetch merged PRs
github_list_pull_requests
Categorize with LLM
llm_categorize_changes
Post to Slack + GitHub
slack_send_message
Release notes: v2.4.0
Summary (34 PRs)
"8 features, 14 bug fixes, 2 breaking changes"
GitHub release updated, posted to #releases
Result
34 PRs categorized, 2 flagged for human review
Release notes posted to Slack and GitHub in <30s
Message Claude...

How the agent turns merged PRs into structured release notes in four steps

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 GitHub and Slack tokens automatically. main.py triggers on a GitHub release tag event (or runs on a release cron) and executes the notes pipeline for all PRs merged since the previous tag.
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", "slack"],
)

agent = create_tool_calling_agent(llm, tools, prompt)
02
Fetch merged PRs since last tag
fetch_prs.py
03
Categorize changes with LLM
categorize.py
04
Draft and post release notes
post_release.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 write release notes, without the auth plumbing

Two things you'd otherwise build: GitHub OAuth with repo read + release write scopes and Slack credentials. 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", "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", "slack"],
)
Try other Agent Templates

Prebuilt agents you can ship today

Each one runs on delegated identity, scoped per user.

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
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
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 an auto release notes agent using GitHub and Slack via Scalekit"

terminal

codex "Set up an auto release notes agent using GitHub and Slack via Scalekit"

terminal

gh copilot suggest "Set up an auto release notes agent using GitHub 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", "slack"]. Build a release notes agent: fetch all PRs merged since the previous GitHub release tag, categorize each into Features/Bug Fixes/Performance/Security/Breaking Changes with the LLM, format structured release notes, update the GitHub release description, and post to #releases in Slack. Trigger on release tag push. 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.