I Put a Free LLM in Front of Every Pull Request. Here's the Exact Config

First-pass code review is the most automatable part of engineering work, yet most teams burn human attention on formatting nits instead of architectural risk. This article documents a concrete workflow that puts a free model in front of every pull request, including the exact CI configuration and prompt template. The setup runs on MonkeyCode's free model access and free server option, which keeps the entire experiment at zero cost.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Why the First Pass Exists

A human reviewer should spend their limited attention on logic errors, race conditions, and design tradeoffs. Instead, they burn it on missing semicolons, inconsistent naming, and forgotten error handling. The first-pass review exists to catch those mechanical issues before a human ever opens the diff.

The problem is that first-pass reviews are boring, which means they get skipped, rushed, or delegated to the most junior person on the team. A free model does not get bored, does not rush, and does not complain about the assignment. It just reads the diff and flags the same categories of issues every single time.

The Workflow

The workflow has three stages: a GitHub Action that triggers on pull request events, a prompt template that defines the review categories, and a comment bot that posts findings back to the PR. Each stage is small enough to reason about independently, which makes the whole system debuggable when it misbehaves.

Stage 1: The Trigger

name: free-model-review
on:
  pull_request:
    types: [opened, synchronize]
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run free-model review
        env:
          API_KEY: ${{ secrets.MONKEYCODE_API_KEY }}
        run: |
          pip install requests
          python review.py --diff "$(git diff origin/main...HEAD)"

The trigger fires on opened and synchronize, which covers both new PRs and subsequent commits. The git diff command captures the full change set, and the script passes it to the model as a single structured payload.

Stage 2: The Prompt Template

The prompt template is the heart of the system, and it matters more than the model choice. A vague prompt produces vague comments, while a structured prompt produces actionable findings.

You are a first-pass code reviewer. Classify each issue into one of:
- BUG: logic error, race condition, null dereference, off-by-one
- STYLE: naming, formatting, dead code, inconsistent patterns
- SECURITY: injection, hardcoded secrets, unsafe deserialization
- MISSING: error handling, tests, edge cases

For each issue, include:
- File and line number
- Category
- One-sentence explanation
- Suggested fix (one line of code or a short description)

Ignore: whitespace, comment wording, refactoring preferences.
Output format: Markdown table. If no issues, output "No issues found."

The category list is deliberately small, because a model that tries to review everything ends up reviewing nothing well. The output format is constrained to a Markdown table, which makes the bot's comment readable and the parsing trivial.

Stage 3: The Comment Bot

import os
import sys
import requests

def post_comment(repo, pr_number, body):
    url = f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments"
    headers = {
        "Authorization": f"token {os.environ['GITHUB_TOKEN']}",
        "Accept": "application/vnd.github+json",
    }
    requests.post(url, json={"body": body}, headers=headers)

if __name__ == "__main__":
    diff = sys.argv[1]
    response = requests.post(
        "https://api.monkeycode.ai/v1/chat/completions",
        headers={"Authorization": f"Bearer {os.environ['API_KEY']}"},
        json={
            "model": "default",
            "messages": [
                {"role": "system", "content": "You are a code reviewer."},
                {"role": "user", "content": f"Review this diff:\n\n{diff}"},
            ],
        },
    )
    findings = response.json()["choices"][0]["message"]["content"]
    post_comment(os.environ["GITHUB_REPOSITORY"], os.environ["PR_NUMBER"], findings)

The bot posts the model's findings as a PR comment, which keeps the review in the same place where developers already look. The script is intentionally minimal, because a longer script would introduce its own bugs and obscure the model's output.

What the Setup Caught

The workflow ran for two weeks on a small TypeScript codebase, and the results were surprisingly specific. The model caught a null dereference in a refactored utility function, flagged a hardcoded database password that had been committed in a test fixture, and consistently identified missing error handling around fetch calls.

The most valuable catch was the hardcoded password, because it was a security issue that a human reviewer would have noticed eventually, but the model caught it within seconds of the PR opening. The least valuable output was a recurring suggestion to extract a helper function that did not need extracting, which the team learned to ignore.

What the Setup Missed

The model missed a race condition in an async cache update, which is exactly the kind of issue that requires understanding the runtime behavior rather than reading the diff. It also missed a breaking change in an internal API contract, because the breaking change was in a different file than the one being modified.

These misses are not failures; they are the boundary of the tool. A free model is a first-pass reviewer, not a senior engineer, and the workflow is designed around that distinction. A human reviewer still reads every PR, but they read it with the mechanical issues already resolved.

The Cost Calculation

The entire experiment ran on MonkeyCode's free tier, which includes 10 million tokens and a free server option. A typical PR review consumed roughly 2,000 tokens, which means the free allowance covered about 5,000 PR reviews before hitting any limit. The free server option handled the script execution, so the only infrastructure cost was the GitHub Actions minutes, which are free for public repositories.

Teams that want to replicate this setup should verify the current token allowance and server terms against the project's README, because free tiers change without warning. The workflow itself is provider-agnostic, so swapping in a different API endpoint requires changing one URL and one header.

Limitations and Who Should Not Use This

Teams with strict data-residency requirements should not send proprietary source code to a third-party API, even a free one. Teams that need guaranteed uptime or contractual SLAs should not depend on a community tier for a critical workflow step. And teams that cannot tolerate false-positive comments should not enable the bot without a human approval step.

The setup is best for small teams and open-source projects that want a consistent first-pass review without paying for a commercial review tool. It is not a replacement for human review, and it should not be marketed as one.

The Takeaway

The first-pass review is a solved problem, and the solution is a free model with a structured prompt and a CI integration. The workflow in this article takes about an hour to set up, costs nothing to run, and catches the mechanical issues that waste human attention. Start with a small repository, review the model's output for a week, and then decide whether the bot earns a permanent place in the pipeline.