classify-review-comment

Classify Review Comments

Classify GitHub pull request review comments into severity and topic categories. Works with a single comment (text), a GitHub comment URL, or an entire PR (classifies all comments).

This enables tracking review feedback patterns: what kinds of issues reviewers catch, how severe they are, and where AI-generated code needs the most improvement.

Labels

Read the labels file before classifying any comments:

config.json (in the same directory as this skill)

The labels file defines the exact set of valid values for severity and topic. You MUST select from these values — do not invent new labels. Each label includes a description and signal words or examples to guide your selection.

Classification rule: For each comment, find the single best-matching severity and single best-matching topic from the labels file. Match based on the label's description, signals/examples, and the comment content. Use unclassified only when no other label fits.

Input Modes

1. Single Comment (text)

Classify a comment provided directly as text.

Input: The raw comment body. Output: A single classification object.

2. Comment URL

Fetch a specific comment by its GitHub URL and classify it.

URL formats supported:

• https://github.com/{owner}/{repo}/pull/{number}#issuecomment-{id}
• https://github.com/{owner}/{repo}/pull/{number}#discussion_r{id}
• https://github.com/{owner}/{repo}/pull/{number}#pullrequestreview-{id}

Fetch with:

# Issue comment
gh api repos/{owner}/{repo}/issues/comments/{id} --jq '{author: .user.login, body: .body}'

# Review comment (discussion)
gh api repos/{owner}/{repo}/pulls/comments/{id} --jq '{author: .user.login, body: .body}'

# Review body comment
gh api repos/{owner}/{repo}/pulls/{number}/reviews/{id} --jq '{author: .user.login, body: .body}'

3. Full PR

Fetch all comments on a PR, filter out noise, and classify each one.

URL format: https://github.com/{owner}/{repo}/pull/{number}

Fetch with:

# Issue-level conversation comments
gh api repos/{owner}/{repo}/issues/{number}/comments --paginate --jq '.[] | {id: .id, author: .user.login, body: .body}'

# Inline review comments
gh api repos/{owner}/{repo}/pulls/{number}/comments --paginate --jq '.[] | {id: .id, author: .user.login, body: .body}'

# Review body comments (approvals, rejections, general review summaries)
gh api repos/{owner}/{repo}/pulls/{number}/reviews --paginate --jq '.[] | select(.body != null and .body != "") | {id: .id, author: .user.login, body: .body}'

Before classifying, filter out noise comments (these carry no review signal):

• Pure slash commands: body starts with / followed by a command word (e.g., /lgtm, /test e2e-aws, /approve, /retest, /cc)
• CI bot notifications: authors like openshift-ci-robot, openshift-ci[bot], cwbotbot, or any *[bot] author not listed in the allowed_bots section of config.json
• Comments matching any pattern in the noise_patterns section of config.json
• Auto-CC commands: /auto-cc

Do classify comments from:

• Human reviewers (all comments, including those directing bots)
• Any bot listed in allowed_bots in config.json (classify their substantive review comments — code issues, suggestions, questions)

Classification Approach

For each comment:

1. Read config.json to load the valid severity and topic values
2. Scan the comment body for signal words and patterns that match label descriptions
3. Select exactly one severity — match the comment's urgency/tone to the severity descriptions and signals
4. Select exactly one topic — match the comment's subject matter to the topic descriptions and examples
5. Score confidence using the rubric below
6. When ambiguous, prefer the more specific label over unclassified
7. When a comment spans multiple topics, pick the primary one — what is the reviewer's main concern?

Confidence Scoring Rubric

Each classification includes a confidence score (0.00–1.00) indicating how certain the classification is. Accumulate the score from these signals:

Signal	Weight	Criteria
Signal word match	+0.25	Comment contains signal words/phrases from config.json for the chosen severity or topic label
Unambiguous category	+0.25	Comment clearly fits one severity and one topic with no viable alternatives
Example pattern match	+0.25	Comment closely matches a real-world example in this skill or in config.json
Context reinforcement	+0.15	Multiple independent indicators point to the same classification (e.g., tone + keywords + structure all agree)
Single viable label	+0.10	No other severity or topic label is a reasonable alternative

Maximum score is 1.00. When multiple signals apply, sum them and cap at 1.00.

Interpretation:

• >= 0.95 — High confidence: classification can be auto-applied for low-risk labels
• 0.80–0.94 — Moderate confidence: human confirmation recommended
• < 0.80 — Low confidence: manual classification required

Additional classification rules:

• Slash commands mixed with text — if a comment has substantive text before a slash command (e.g., "good analysis\n/override ci/prow/e2e"), classify based on the substantive text, not the slash command
• Comments directed at bots/AI — classify by the underlying problem, not the fact that the recipient is a bot. "Fix the unit tests" is about test failures (test_gap), "rebase the PR" is a CI/process issue (ci), "push the changes" is a process failure (process). The topic should answer "what went wrong?" not "who is being told to fix it?"

Output Format

Single comment

{
  "severity": "<value from config.json severity list>",
  "topic": "<value from config.json topic list>",
  "confidence": 0.95,
  "rationale": "Brief one-line explanation of why this classification was chosen"
}

Full PR

{
  "pr": "https://github.com/openshift/hypershift/pull/7620",
  "total_comments": 15,
  "classified": 5,
  "filtered_noise": 10,
  "comments": [
    {
      "id": 2871360513,
      "author": "jparrill",
      "body_preview": "small nit: I would move the vars...",
      "severity": "<value from config.json>",
      "topic": "<value from config.json>",
      "confidence": 0.95,
      "rationale": "Reviewer suggests moving variable declarations for consistency"
    }
  ],
  "summary": {
    "by_severity": {"nitpick": 1, "suggestion": 1, "required_change": 2, "question": 1},
    "by_topic": {"style": 1, "api_design": 1, "logic_bug": 2, "test_gap": 1},
    "by_confidence": {"high": 3, "moderate": 1, "low": 1}
  }
}

Real-World Examples

These are from actual PRs in openshift/hypershift:

Comment: "small nit: I would move the vars key, log and cloudName to var ( section just to be consistent."

{"severity": "nitpick", "topic": "style", "confidence": 1.00, "rationale": "Reviewer suggests grouping variables for consistency — cosmetic, not functional"}

Comment: "Why not use NewARMClientOptions here for the clientOptions?"

{"severity": "question", "topic": "api_design", "confidence": 0.90, "rationale": "Reviewer asks about API choice for client options construction"}

Comment: "This will panic if items is nil — needs a nil check before the loop"

{"severity": "required_change", "topic": "logic_bug", "confidence": 1.00, "rationale": "Nil pointer dereference would cause runtime panic"}

Comment: "failing during hypershift install\n\nClusterRoleBinding is invalid: roleRef.kind: Unsupported value\n"

{"severity": "required_change", "topic": "logic_bug", "confidence": 0.90, "rationale": "Installation fails due to missing required roleRef fields"}

Comment: "hypershift-jira-solve-ci - the unit test job is failing and needs fixed"

{"severity": "required_change", "topic": "test_gap", "confidence": 0.90, "rationale": "Unit tests are failing — the bot made code changes without ensuring tests pass"}

Comment: "hypershift-jira-solve-ci - rebase the PR to fix the konflux issues"

{"severity": "suggestion", "topic": "ci", "confidence": 0.85, "rationale": "PR needs rebasing to resolve CI pipeline issues — classify by the problem (CI), not the recipient (bot)"}

Comment: "hypershift-jira-solve-ci - this still needs fixed since the code did not get pushed"

{"severity": "required_change", "topic": "process", "confidence": 0.85, "rationale": "Code changes were not committed/pushed — a process failure, not a code issue"}

Comment: "e2e-aws-4-21 failed on Teardown but due to uncleaned cloud resources, not VPC endpoint blocking the finalizer\n/override ci/prow/e2e-aws-4-21"

{"severity": "suggestion", "topic": "ci", "confidence": 0.75, "rationale": "Reviewer explains CI failure root cause and overrides — substantive analysis before the slash command"}

Comment: "Oh no that's ok. I missed that part. No changes requested."

{"severity": "unclassified", "topic": "approval", "confidence": 0.70, "rationale": "Reviewer withdrawing their earlier question — acknowledgment"}

Comment: "dup of https://github.com/openshift/hypershift/pull/7727"

{"severity": "unclassified", "topic": "process", "confidence": 0.90, "rationale": "Marking PR as duplicate of another — process meta-comment"}

Comment: "The root cause of the CI failure in this PR has been identified. The fix in rejectVpcEndpointConnections doesn't work because of a case mismatch between AWS API responses and SDK v2 enum constants."

{"severity": "required_change", "topic": "logic_bug", "confidence": 1.00, "rationale": "Detailed root cause analysis identifying a case mismatch bug"}

Comment: "This controller is doing too much — the reconciler should delegate VPC cleanup to a separate controller instead of inlining it"

{"severity": "suggestion", "topic": "architecture_design", "confidence": 0.90, "rationale": "Reviewer identifies a separation-of-concerns issue at the controller level"}

Comment: "The service account token is being logged in plain text here — this needs to be redacted"

{"severity": "required_change", "topic": "security", "confidence": 1.00, "rationale": "Sensitive credentials exposed in log output — security vulnerability"}

CodeRabbit issue flagged (starting with _Potential issue_ | _Critical_):

{"severity": "required_change", "topic": "logic_bug", "confidence": 0.85, "rationale": "CodeRabbit identified a critical code issue requiring attention"}