MergeIQ: Automatically Score & Prioritise PR Complexity for GitLab and GitHub

Score the complexity of any GitLab MR or GitHub PR using a 4-dimension framework: Size (20%), Cognitive Load (30%), Review Effort (30%), and Risk/Impact (20%). Works with GitLab or GitHub. Zero external dependencies. Use when asked to review, triage, score, or prioritise pull requests and merge requests by complexity.

MR / PR Complexity Scorer

A provider-agnostic complexity scoring engine for Merge Requests (GitLab) and Pull Requests (GitHub). Built on a 4-dimension framework that captures what "complex" actually means in code review — not just lines changed.

Complexity Dimensions

Dimension	Weight	What it measures
Size	20%	Volume of code changed (logarithmic — big PRs saturate fast)
Cognitive Load	30%	Directory breadth, cross-module changes, file diversity
Review Effort	30%	Discussion depth, reviewer count, approval iterations
Risk / Impact	20%	Breaking changes, migrations, security labels, dependencies

Output tiers: trivial / simple / moderate / complex / highly_complex

When to Use

• Triaging a backlog of open PRs by complexity before a review session
• Flagging high-complexity MRs for mandatory second review
• Generating weekly complexity trend reports for a team
• Understanding why a PR is taking a long time (dimension breakdown)
• Building engineering director dashboards (see score_mr.py)

Quick Start

bash

# Score a GitHub PR (basic — just the PR object)
curl -s "https://api.github.com/repos/OWNER/REPO/pulls/NUMBER" \
     -H "Authorization: Bearer $GITHUB_TOKEN" \
     | python score_mr.py --provider github

# Score a GitLab MR (with diff stats)
curl -s "https://gitlab.com/api/v4/projects/PROJECT_ID/merge_requests/IID?include_diff_stats=true" \
     -H "PRIVATE-TOKEN: $GITLAB_TOKEN" \
     | python score_mr.py --provider gitlab

# Richer scoring — fetch files + reviews too
curl -s ".../pulls/NUMBER" > pr.json
curl -s ".../pulls/NUMBER/files" > files.json
curl -s ".../pulls/NUMBER/reviews" > reviews.json
python score_mr.py --provider github --pr pr.json --files files.json --reviews reviews.json

Example Output

json

{
  "provider": "github",
  "id": 412,
  "title": "Migrate auth service to OAuth2",
  "score": {
    "total": 74.2,
    "tier": "complex",
    "size": 68.0,
    "cognitive": 81.5,
    "review_effort": 72.0,
    "risk_impact": 60.0
  },
  "summary": "High mental load: 14 files across 6 directories, 3 reviewers involved",
  "tier_insight": "Needs careful review — high cognitive load and cross-module impact.",
  "stats": {
    "additions": 412,
    "deletions": 87,
    "files_changed": 14,
    "reviewers": 3,
    "discussions": 9,
    "net_lines": 325
  }
}

Files

text

mr-complexity-scorer/
  SKILL.md                      # This file
  mr_complexity_service.py      # Core 4-dimension scoring engine (pure Python)
  score_mr.py                   # CLI: pipe in API JSON, get complexity JSON out
  requirements.txt              # No external deps — stdlib only, Python 3.9+
  adapters/
    gitlab_adapter.py           # GitLab MR API dict → MRData
    github_adapter.py           # GitHub PR API dict → MRData

Using in Your Code

python

from mr_complexity_service import MRComplexityCalculator, MRData
from adapters.github_adapter import github_pr_to_mrdata

# Build MRData from a GitHub PR dict (from API or webhook payload)
mr_data = github_pr_to_mrdata(
    pr=pr_dict,
    files=files_list,       # optional: /pulls/:number/files
    commits=commits_list,   # optional: /pulls/:number/commits
    reviews=reviews_list,   # optional: /pulls/:number/reviews
)

calculator = MRComplexityCalculator()
result = calculator.calculate(mr_data)

print(result.complexity_tier)   # "complex"
print(result.total_score)       # 74.2
print(result.human_summary)     # "High mental load: ..."

Enrichment — What's Worth Fetching

Extra API call	Unlocks	Worth it?
/pulls/:n/files	File path cognitive analysis	Yes, always
/pulls/:n/reviews	Accurate reviewer count + iters	Yes for review dim
/pulls/:n/commits	Breaking-change detection	Nice to have
/pulls/:n/comments	Inline discussion count	Optional

Without enrichment, the scorer still works — it uses changed_files, review_comments, and requested_reviewers from the base PR object. Enriched data improves accuracy.

Extending to Other Providers

Implement a thin adapter that maps your provider's MR/PR dict to MRData:

python

from mr_complexity_service import MRData

def linear_issue_to_mrdata(issue: dict) -> MRData:
    return MRData(
        iid=issue["number"],
        title=issue["title"],
        # ... map your fields
    )

Works with: GitLab, GitHub, Gitea, Bitbucket, Azure DevOps — anything with MR/PR metadata.

Adjusting Weights

python

from mr_complexity_service import MRComplexityCalculator, ComplexityConfig

config = ComplexityConfig(
    weight_size=0.15,
    weight_cognitive=0.35,
    weight_review=0.30,
    weight_risk=0.20,
)
calculator = MRComplexityCalculator(config=config)