GitHub Issue Triage Workflow

Every new issue labeled, prioritized, deduped, and routed to the right owner within 15 minutes of submission.

Updated 2026-04-18
Tycoon solution

AI CTO triages every new GitHub issue within 15 minutes of submission. Categorizes (bug / feature / question / docs / security), labels appropriately, checks for duplicates (semantic search across past issues), requests a repro if missing, assigns to the right maintainer, and posts a first-response that makes the reporter feel heard. You wake up to a triaged backlog.

How it runs

  1. Classify on submission

    GitHub webhook fires within seconds of a new issue. AI CTO reads title + body and classifies: bug, feature request, question, docs issue, security report (the latter two escalated immediately). Applies labels from your repo's label taxonomy.

  2. Check for duplicates

    Semantic search against past 12 months of issues (open + closed). Finds 'stale issue from 2024 about the same thing' even if the wording differs. Posts a comment linking duplicates, asks if reporter's issue is truly different. ~15-25% of inbound gets deduped.

  3. Request repro if missing

    For bug reports without steps to reproduce, AI CTO posts a comment asking for: exact version, OS, reproduction steps, expected vs actual, screenshots or logs. Auto-closes after 14 days of no response (with friendly reminder at day 7). Stops 'it's broken' drive-by reports from clogging the queue.

  4. Assess severity and priority

    Bugs get severity (P0: outage, P1: broken for many, P2: broken for some, P3: minor). Feature requests get priority via a rubric (strategic fit + effort + customer demand). Security issues get P0 + privileged routing (issue gets converted to GitHub Security Advisory). Labels applied automatically.

  5. Assign to owner

    Based on file paths touched (CODEOWNERS) or issue category, AI CTO assigns to the right maintainer. If maintainer is on PTO or overloaded (>5 open issues), routes to backup. Auto-pings assignee in Slack with context summary.

  6. First response within 15 minutes

    Every new issue gets a first response: acknowledging receipt, summarizing what's been done (labeled, triaged, duplicate check result), and what happens next (reviewed by @owner within 48 hours). Reporters feel heard even if it's 2am PST when they filed.

  7. Weekly backlog health report

    Every Monday, AI CTO posts a repo health summary: issues opened / closed / net change last week, oldest untriaged issue, P0/P1 count, stale issues aging past threshold, top 3 requested features. Maintainers know where to focus without scrolling.

Who runs it

  • hire/ai-cto
  • hire/ai-customer-support

What you get

  • Issues triaged within 15 minutes, 24/7
  • Duplicate rate drops 60-80% via semantic deduplication
  • No-repro drive-bys auto-closed with friendly follow-up
  • Security issues escalated immediately to correct channel
  • Every reporter gets a first response, not a void
  • Maintainers wake up to a prioritized backlog, not a chaotic inbox
  • Repo health tracked weekly — no 'oh, we have 800 open issues?' surprise
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Our open-source project has 12,000 open issues. Can this help dig out of that hole?

Yes, via a backlog cleanup phase. AI CTO runs a one-time pass on the existing backlog: identifies true duplicates (typically 15-25% of old issues), stale issues with no activity in >12 months (auto-closes with a 'please reopen if still relevant' comment), and issues that are actually resolved (feature shipped, bug fixed but issue never closed). Typical cleanup on a 12K-issue repo: 3-4K issues auto-closed, 1-2K auto-merged to dupes, leaving 6-8K that truly need triage. Takes 1-2 weeks of AI running in low-priority mode; then ongoing triage keeps it clean.

We're a private repo, not open-source. Does this still apply?

Yes, adapted. Private repos usually have lower volume (internal bug reports, stakeholder feature requests) but the triage problems are similar: classification, deduplication, routing. For internal: AI CTO triages against your internal priorities + roadmap, assigns to engineers on the owning team, and routes stakeholder feature requests to product managers for review. First-response SLAs are different (24-48 hours instead of 15 minutes), but the workflow shape is the same.

What about spam / low-quality issues from random GitHub accounts?

Spam filtering is the easy part: AI CTO flags issues from accounts with no profile, no repos, first-time contribution to your project, and body patterns that match known spam templates (crypto promotions, SEO link building). Flagged issues get auto-closed with a spam label. False positives are rare (<1%) because the signals are strong. For the borderline cases (real but low-quality, e.g., 'please add feature X' with no context), the 'request repro' workflow applies — if they don't engage, it closes naturally.

Can it distinguish a security-relevant bug from a regular bug?

Yes, and this is where automation matters most. AI CTO looks for security signals: mentions of auth, session, token, XSS, CSRF, SQL injection, unauthorized access, data leakage, credential exposure. Suspected security issues get: (1) immediate private conversion to GitHub Security Advisory, (2) original issue closed with a redirect note, (3) urgent Slack ping to security-on-call. False positive rate is low because most security reports use recognizable language. The rare false positive is better than a public security bug sitting in the open queue.

How does it handle feature requests — does it auto-close low-priority ones?

No auto-closing for feature requests (that frustrates users fast). Instead, AI CTO routes low-priority requests to a 'future consideration' label and posts a friendly comment: 'Thanks for this idea. We're not prioritizing it right now but would love community upvotes to gauge interest.' Upvotes get tracked automatically. Requests with >25 upvotes escalate to the active backlog. Low-engagement requests age out naturally without you needing to say no. Community feels heard; backlog stays actionable.

Related resources

Role

AI CTO | Hire Your AI CTO Today

Hire an AI CTO that owns product direction, code review, infra decisions, and ships features. Direct by chat. For founders who aren't engineers.

Role

AI Customer Support | Hire Your AI Support Agent

Hire an AI Customer Support agent that handles tickets 24/7, flags retention risks, and escalates cleanly. Direct by chat. Real CSAT, not canned replies.

Workflow

Bug Triage on Autopilot with AI | Tycoon Workflows

Every error report, Sentry alert, and customer complaint triaged in under 10 minutes — severity scored, reproducer written, routed to a fix.

Workflow

Feature Request Triage with AI | Tycoon Workflows

Every feature ask — from tweets, Intercom, Discord, Reddit — aggregated, scored by revenue impact, and routed to the roadmap.

Workflow

Support Ticket Categorization | Tycoon Workflows

Every inbound ticket tagged, prioritized, routed, and first-responded — before the support queue gets messy.

Workflow

Changelog Maintenance on Autopilot | Tycoon Workflows

A SemVer-correct, customer-readable changelog that updates on every release — no more v2.3.1 bug fixes and improvements.

Workflow

API Docs Maintenance on Autopilot | Tycoon Workflows

OpenAPI spec auto-generated from code, docs pages synced on every deploy, code samples tested continuously — docs that never lie.

Run your company with humans and AI agents.

Hire your AI team in 30 seconds. Start for free.

Sign up / Sign inLearn the category

Free to start · No credit card required · Set up in 30 seconds