PR context before the review starts

Tagger gives reviewers a fast first read on scope, complexity, and risk as soon as a pull request opens. It is built for teams that want better triage and cleaner review queues without introducing AI cost or another workflow to manage.

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Tagger

Pull request review signal without AI cost

100% FreeAlways free, no hidden costs

The problem

Teams often open a PR with no quick way to tell whether it is routine, risky, documentation-only, or likely to need deeper review. That slows triage, spreads reviewer attention thinly, and makes queues harder to manage.

The solution

Tagger analyses the diff algorithmically and posts immediate review context: complexity score, risk level, change classification, and file-level summary. Reviewers can decide where to spend time before reading every line of the diff. It stays free, with no token budgets, prompts, or API spend to think about.

Key details

  • Complexity scoring: algorithmic scoring based on files changed, line counts, and change spread.
  • Risk assessment: clear low, medium, and high review signals for faster triage.
  • Change classification: automatic tags for features, fixes, docs, refactors, tests, and more.
  • Docs-only detection: separate documentation changes from behavioural changes.
  • GitHub-native output: comments and badges land directly on the PR where reviewers already work.
  • Always free: no AI calls, no token accounting, no pricing tier decisions.

Good fit for

Engineering leads: who need a fast read on review load and PR risk.
Reviewer-heavy teams: who want a quick triage layer before opening every diff.
Code reviewers: who want clearer first-pass signal on scope and change shape.
Busy repositories: where many PRs compete for limited review attention.
Open-source maintainers: who need lightweight review context without added cost.

How it works

1. PR event: Tagger runs when a pull request is opened, updated, reopened, or edited.
2. Diff analysis: It inspects files changed, additions, deletions, and file types.
3. Review signal: It calculates complexity, risk, and likely change category.
4. PR comment: It posts the result back to GitHub as a review-ready summary.

Operating model

  • Minimal setup: install it and let pull request events drive the workflow.
  • No external AI dependency: deterministic analysis without prompts or model costs.
  • Low overhead: lightweight enough for routine repository use.
  • Privacy aware: focused on PR metadata and diff structure rather than storing source code.
  • Language agnostic: useful across mixed-language repositories.
  • Fresh on every update: analysis refreshes when the PR changes.

Why teams keep it installed

Fast triage

See which PRs need attention first without manual sorting.

No cost surprises

Free usage with no tokens, seats, or inference billing.

Clearer reviews

Reviewers start with context instead of reconstructing it.

Simple rollout

Easy to add across repositories without creating process drag.

Start improving PR triage now

Install Tagger if you want better review signal without changing how the team already works in GitHub. It stays free.

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