AI AGENTS
Detect overloaded GitHub issues and re-file as estimated Linear children
When a GitHub issue accumulates too many task-list checkboxes, an agent treats it as an oversized epic, splits each unchecked task into an estimated Linear child issue.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub issue editedGitHub
- LogicExit unless task list overflows threshold
- ActionRead GitHub issue body and task listGitHub
- ActionOpenAI maps tasks to estimated issuesOpenAI
- ActionCreate Linear epic and child issuesLinear
- OutputComment links back on GitHub issueGitHub
What it does
When a GitHub issue grows a long task list, the agent reads its checkboxes, turns each open task into a properly scoped, estimated Linear issue under a parent epic, and links both directions so the GitHub thread and Linear backlog stay in sync.
When to use it
Use when engineers triage in GitHub but plan in Linear, and catch-all issues pile up unchecked task lists that never get estimated. This converts the overflow into trackable, pointed Linear work without manual re-entry.
How it works
- 1A GitHub webhook fires when an issue is edited.
- 2A logic step counts open task-list items and exits unless the issue exceeds the overflow threshold.
- 3A GitHub action reads the full issue body and task list.
- 4OpenAI maps each open task to a Linear child issue with an estimate and acceptance criteria.
- 5A Linear action creates a parent epic and the estimated children.
- 6A GitHub comment posts links to the new Linear issues and the parent epic.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Datadog Bill Spike Attribution Agent
When a daily Datadog cost check detects a spend jump, an agent attributes the increase to the specific services and metric types driving it and posts a ranked breakdown to Slack.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.