Recommendation
Use a CI job that reads only the PR diff, calls an LLM (Copilot CLI/official Copilot API where available, or a fallback model like GPT via ChatGPT/OpenAI), and posts human-readable findings and “suggested change” blocks as a review comment. Do NOT auto-apply fixes or give the bot push permissions—require a human to accept any suggestion.

Why this pattern
- Safe: read-only analysis + review comments avoids unwanted code changes.
- Actionable: GitHub review API supports “suggested change” blocks that reviewers can accept inline.
- Auditable: CI logs + PR thread show what the model proposed and who approved it.

Decision criteria (pick based on your org)
- Budget: calling a hosted model for every PR costs money; cheaper for small repos, expensive at scale.
- Skill level: teams comfortable with GitHub Actions + REST API can implement quickly; less experienced teams should prototype on a single repo.
- Trust level / compliance: sensitive codebases should start with flag-only mode (warnings) and whitelist directories.
- Automation level: flag-only vs suggested patches vs auto-apply. Start with flag-only.

Concrete implementation checklist
1) Opt-in & scope
- Start with opt-in PRs (label like "copilot-review-opt-in") or whitelisted paths.
2) Permissions & secrets
- Workflow permissions: set contents: read and pull-requests: write (to post reviews). Avoid repo:write/push unless explicitly desired. Use a dedicated bot/service account.
- Store model API key as a secret (e.g., EXTERNAL_LLM_KEY). If using Copilot vendor tooling, follow their auth docs.
3) CI job outline (GitHub Actions)
- Trigger: pull_request (opened/synchronize) and only on target branches you want.
- Step A: checkout + extract changed hunks (git diff --unified=0 origin/${{ github.base_ref }}...HEAD).
- Step B: sanitize hunks (strip secrets, redact env/layouts).
- Step C: call model with a deterministic prompt asking for bug risks and a patch in unified diff or suggested file content. Limit tokens and runtime.
- Step D: validate model response (simple lint/tests or pattern checks).
- Step E: create a PR review with comments containing “```suggestion

```” blocks or flag-only messages. Use GitHub REST API / actions/github-script.
4) Guardrails
- Do not auto-merge or auto-push suggestions.
- Label all bot comments (e.g., [Copilot-CI]).
- Rate-limit calls and cache similar diffs to avoid repeated calls.
- Keep audit logs of inputs/outputs for a retention period that meets policy.

Best-for / Avoid-if
- Best-for: mid-to-large engineering teams that want to scale review coverage, reduce small bug slips, and have budget for API calls.
- Avoid-if: strict compliance environments (PII/crypto keys) unless you implement redaction + enterprise LLM with on-prem/data residency guarantees.

Extra tips
- Start in “flag-only” mode for 2–4 weeks, review false positives, then enable suggested patches.
- Keep a feedback loop: allow reviewers to mark suggestions as useful/not useful and track precision.

If you want a vendor-native route, look at GitHub Copilot features for PR assistance (and Copilot CLI if your org has access). For a more model-agnostic PoC, use a ChatGPT/OpenAI integration to validate the flow before switching to a Copilot-backed integration.

Would you like a small GitHub Actions example that extracts hunks and posts a suggested-change review comment (flag-only vs suggestion) to start with?

Compare GitHub Copilot and Cursor