In this blog post Parallel Code Review with GitHub Copilot CLI for Faster PRs we will walk through a practical way to run several AI-assisted review passes at the same time, directly from your terminal. The goal is simple: reduce review bottlenecks, raise code quality, and help your team spend human attention where it matters most.
High level, “parallel code review” means you don’t ask one reviewer (human or AI) to look at everything in a single linear pass. Instead, you split the review into focused lenses—correctness, security, performance, readability, and tests—and run those lenses simultaneously. GitHub Copilot CLI makes this approachable because it can analyze your local changes and provide feedback before you even push a commit. It’s not a replacement for peer review; it’s a force multiplier that helps humans review better and faster.
What is GitHub Copilot CLI and what technology powers it
GitHub Copilot CLI is a terminal experience that connects your prompts and your local code context to an AI “agent” that can answer questions and analyze changes. GitHub describes it as an AI agent you can use interactively (a session in your terminal) or programmatically (single-shot prompts). It is currently in public preview and subject to change. ([docs.github.com](https://docs.github.com/copilot/concepts/agents/about-copilot-cli?utm_source=openai))
Under the hood, the main technology is a large language model (LLM)-powered agent that can read and reason about code and diffs, then produce structured feedback. The CLI also supports a permissions model (what tools it can run and what it can access) so you can keep control of your environment. GitHub’s docs note features like context management and a dedicated /review command to analyze code changes prior to committing. ([docs.github.com](https://docs.github.com/copilot/how-tos/use-copilot-agents/use-copilot-cli?utm_source=openai))
Why parallel reviews work (especially for busy teams)
- Faster feedback loops: developers get actionable comments while they still have the code open.
- Less reviewer fatigue: humans focus on architecture, product intent, and tricky edge cases.
- More consistent standards: each “lens” can enforce your conventions repeatedly.
- Better risk coverage: security and performance concerns don’t get lost in styling comments.
Prereqs and setup
You have a couple of options to run Copilot from your terminal. GitHub CLI can download and run the Copilot CLI via gh copilot (preview behavior). ([cli.github.com](https://cli.github.com/manual/gh_copilot?utm_source=openai))
In practice you’ll want:
- GitHub CLI authenticated for your org/user
- GitHub Copilot access (Pro/Business/Enterprise, depending on your environment)
- A local repo with a clean working directory (or at least clear diffs you intend to review)
Quick start
# Option A: run Copilot CLI via GitHub CLI
gh copilot -- --help
# Option B: run Copilot CLI directly (if installed)
copilot --help
The core idea: run multiple review passes at once
The Copilot CLI includes a /review command for analyzing code changes. ([docs.github.com](https://docs.github.com/copilot/how-tos/use-copilot-agents/use-copilot-cli?utm_source=openai)) That’s perfect for pre-commit or pre-push checks. To make it “parallel,” we run multiple review prompts concurrently, each with a specific purpose.
Think of it as a review checklist turned into separate workers:
- Correctness reviewer: logic errors, edge cases, broken flows
- Security reviewer: injection risks, authz/authn mistakes, secrets
- Performance reviewer: N+1 calls, slow loops, expensive queries
- Maintainability reviewer: naming, complexity, duplication, readability
- Test reviewer: missing tests, brittle tests, missing failure cases
A practical workflow for parallel review using your local diff
Start by generating a clean diff that the AI can reason about. A simple approach is to capture the diff into a file and feed it to multiple prompts.
# 1) Create a diff artifact (staged or unstaged, your choice)
git diff > /tmp/review.diff
# (Optional) For staged changes only
# git diff --cached > /tmp/review.diff
Now run several Copilot sessions in parallel. The exact CLI flags can evolve (the product is in preview), so keep the idea stable: multiple prompts, same diff, different lens. ([docs.github.com](https://docs.github.com/en/copilot/how-tos/use-copilot-agents/use-copilot-cli?utm_source=openai))
# 2) Parallel lenses (macOS/Linux). This pattern runs jobs concurrently.
# If your shell differs, adapt accordingly.
cat /tmp/review.diff | copilot -p "Review this diff for correctness bugs and edge cases. Output a numbered list with file/line hints." > /tmp/review.correctness.txt &
cat /tmp/review.diff | copilot -p "Review this diff for security issues (injection, auth, secrets, unsafe deserialization). Be concrete." > /tmp/review.security.txt &
cat /tmp/review.diff | copilot -p "Review this diff for performance concerns. Call out hotspots and suggest optimizations." > /tmp/review.performance.txt &
cat /tmp/review.diff | copilot -p "Review this diff for maintainability (complexity, naming, duplication). Suggest refactors." > /tmp/review.maintainability.txt &
cat /tmp/review.diff | copilot -p "Review this diff for test gaps. Propose specific test cases and where they should live." > /tmp/review.tests.txt &
wait
Finally, merge results into a single “AI review packet” your team can use.
# 3) Combine into a single report
{
echo "## Correctness"; cat /tmp/review.correctness.txt; echo
echo "## Security"; cat /tmp/review.security.txt; echo
echo "## Performance"; cat /tmp/review.performance.txt; echo
echo "## Maintainability"; cat /tmp/review.maintainability.txt; echo
echo "## Tests"; cat /tmp/review.tests.txt; echo
} > /tmp/review.packet.md
How to use the built-in review experience (interactive)
If you prefer a guided flow, use the interactive terminal session and the /review command to analyze changes before you commit. This is great when you want to iterate: apply a fix, re-run review, and keep going until the feedback quiets down. ([docs.github.com](https://docs.github.com/copilot/how-tos/use-copilot-agents/use-copilot-cli?utm_source=openai))
Team guardrails (so it stays useful, not noisy)
- Make prompts specific: “Find auth bypass risks in these handlers” beats “review my code.”
- Require evidence: ask for file/line references and concrete examples.
- Decide what humans own: architecture, product intent, and trade-offs stay human-led.
- Keep permissions tight: only allow what Copilot needs; avoid blanket approvals unless you truly trust the environment. ([docs.github.com](https://docs.github.com/copilot/concepts/agents/about-copilot-cli?utm_source=openai))
Where this fits in the broader GitHub review ecosystem
Parallel review with Copilot CLI is most valuable before you open a pull request or while you’re polishing it. It reduces churn and makes your PR description cleaner. Then your human reviewers can focus on the parts AI is weaker at: system behavior, business logic intent, and long-term maintainability decisions.
Next steps you can implement this week
- Create a small prompt library (correctness/security/perf/tests) in your repo.
- Add a simple script (e.g.,
./scripts/ai-review.sh) to generate the review packet. - Encourage developers to attach the packet summary to PR descriptions (not as a gate, as context).
- Track outcomes: fewer review rounds, fewer escaped defects, faster cycle time.
Done well, parallel code review with GitHub Copilot CLI doesn’t just speed things up—it helps your team build a habit of checking quality from multiple angles, early, and with less friction. ([docs.github.com](https://docs.github.com/copilot/how-tos/use-copilot-agents/use-copilot-cli?utm_source=openai))
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