codeant-resolve-pr-comments

name	codeant-resolve-pr-comments
description	Find the pull request for the current branch, fetch all unresolved CodeAnt AI review comments, validate each code suggestion against surrounding context, and apply only safe, minimal fixes

Find the pull request for the current branch, fetch all unresolved CodeAnt AI review comments, validate each code suggestion against the surrounding code context, and apply only safe, minimal fixes that do not break existing logic.

Instructions

Step 1 — Find the Pull Request

The goal is to identify the correct PR. Use the following logic in order:

If the user provides a PR number (e.g., /codeant-resolve-pr-comments 42):

Use it directly. Skip to Step 2.

If no PR number is given, detect it from the current branch:

1. Get the current branch name:

git rev-parse --abbrev-ref HEAD

2. If the branch is main, master, or develop, stop and tell the user: "You are on the default branch. Please switch to a feature branch or provide a PR number."

3. List open PRs filtered by the current branch:

codeant pr list --source-branch "<current-branch>" --state open --limit 5

4. The output is a JSON array of PR objects, each with these fields:

   • number — PR number
   • title — PR title
   • state — open/closed
   • author — who created it
   • sourceBranch — the source branch name
   • targetBranch — the target branch name
   • url — link to the PR

5. Match the correct PR:

   • If exactly one PR is returned whose sourceBranch exactly matches the current branch name, use it.
   • If multiple PRs match, present them to the user and ask which one to use.
   • If zero PRs match, tell the user: "No open PR found for branch <branch>. Please provide a PR number." and stop.

Step 1b — Track Skill Invocation

Report that this skill was invoked:

codeant track --event "skill_invoked" --props '{"skill_name": "codeant-resolve-pr-comments", "source": "claude-code", "pr_number": <N>, "pr_url": "<PR_URL>"}'

Where <PR_URL> is the url field from the PR object found in Step 1.

Step 2 — Fetch CodeAnt Review Comments

Retrieve all review comments that CodeAnt AI posted on the PR:

codeant pr comments --pr-number <N> --codeant-generated true

The output is a JSON array of comment objects with these fields:

Field	Description
id	Unique comment identifier
type	"review" (inline on code) or "issue" (general PR comment)
author	Comment author (e.g., codeant-bot)
body	The full review comment text in markdown
path	File path where the comment was left (null for general comments)
line	Line number the comment refers to (null for general comments)
createdAt	When the comment was posted
isCodeantComment	Boolean — true if posted by CodeAnt
resolved	Boolean — true if the comment has been marked resolved

Filter comments: From the returned array:

1. Keep only comments where resolved is false.
2. Skip general PR comments (type is "issue" or path is null) — these are purely informational (status updates, sequence diagrams, quality gate results) and require no action. Do NOT include them in the summary or flag them for manual review.
3. If no actionable comments remain after filtering, tell the user "All CodeAnt comments on PR #N are already resolved or informational." and stop.

Step 3 — Categorize the Comments

Go through each remaining unresolved comment and categorize it:

Inline code comments (type is "review" and path is not null):

• These point to a specific file and line — they are actionable.
• The body field contains the reviewer's feedback. It may include:
  • A description of the issue
  • A code suggestion embedded in a markdown fenced code block
  • Sometimes a suggestion block (GitHub-style suggested change)

Step 4 — Analyze Each Comment and Assign a Verdict

For each inline comment (grouped by file to minimize re-reading), do the following:

4a. Read and Understand the Context

1. Read the file at the comment's line number, with 30 lines above and 30 lines below for full context.
2. Read the comment body carefully. Identify:
   • What is the problem? — What the reviewer says is wrong.
   • Is there a code suggestion? — Look for fenced code blocks (```suggestion, ```python, ```js, etc.) or inline code that represents a replacement.
   • What is the intent? — What behavior should the code have after the fix.

4b. Validate the Suggestion

For each comment, run through these checks:

1. Check that the code the comment references still exists. The file may have changed since the review. If the code at the referenced line no longer matches what the comment describes, mark as STALE.

2. Detect if this is an Architect / Logical Review comment. Architect reviews are identified by a title like "Architect Review", "Logical Review", or similar phrasing in the comment body, and typically include a **Prompt for AI Agent** section at the bottom. These are first-class, important reviews — not optional suggestions. Do NOT dismiss them as "big architectural changes" just because the title says architect. Many architect reviews require only a small, localized fix once you understand the intent. Treat them with the same seriousness as any other review.

   When handling an architect/logical review:

   • Read the entire comment body, including the **Prompt for AI Agent** section. The prompt under that section is the authoritative instruction for what to change — follow it.
   • If the comment includes a concrete code suggestion, validate it normally (step 3 below).
   • If the comment has no explicit code suggestion, you MUST draft your own fix based on the **Prompt for AI Agent** section and the comment's intent. Do not punt to the user with "no suggestion provided, review manually" — implement the fix yourself, keeping it minimal and localized. Then validate your drafted fix with the same checks in step 3/4.
   • Only mark DO NOT ACCEPT if, after genuinely attempting to implement the fix, the change truly requires a broad restructuring that cannot be done safely in a minimal patch. A missing loading-state check, an added guard clause, a reordered await, or a small conditional wrapper is NOT a "big architectural change."

3. If a code suggestion is present in the body:

   • Extract the suggested code from the markdown.
   • Compare it against the current code at that location.
   • Verify the suggestion is syntactically valid in context:
     • Does it reference variables/functions that exist in scope?
     • Does it use imports that are already present (or need to be added)?
     • Does it match the language and style of the surrounding code?
   • Verify the suggestion does not break logic:
     • Does it change the return type or signature of a function?
     • Does it alter control flow in a way that affects callers?
     • Does it remove error handling or null checks?
     • Does it change the behavior for edge cases?

4. If no code suggestion is present:

   • Analyze the comment to understand the requested change. For architect/logical reviews, use the **Prompt for AI Agent** section as the primary instruction.
   • Draft a minimal fix — change only what is necessary to address the concern.
   • Do NOT refactor surrounding code, rename variables, or "improve" things beyond the scope of the comment.
   • Run the same validation checks as above on your drafted fix.

4c. Assign a Verdict to Each Comment

Based on the validation, assign one of these verdicts to every comment:

ACCEPT — Safe to apply, you should accept this. Assign this when ALL of these are true:

• The suggestion fixes a genuine bug, security issue, or correctness problem
• The suggested code is syntactically valid and all variables/imports are in scope
• The change does NOT alter the function's return type, signature, or public API
• The change does NOT remove or weaken existing error handling
• The change does NOT affect behavior for inputs that were previously handled correctly
• The fix is localized — it only touches the lines relevant to the issue

LIKELY ACCEPT — Looks correct, but verify the callers. Assign this when:

• The suggestion is logically sound and fixes a real issue
• BUT it changes behavior in a way that callers or tests might depend on (e.g., a function now returns an error where it previously returned nil, or a previously permissive validation now rejects some inputs)
• The fix itself is correct, but you cannot guarantee no downstream breakage without checking callers

DO NOT ACCEPT — This could break things. Assign this when ANY of these are true:

• The suggestion changes a function's return type or public interface
• The suggestion removes existing error handling or fallback logic
• The suggestion restructures control flow (reordering if/else, changing loop logic) beyond what the comment asks for
• The suggestion introduces a dependency or import that doesn't exist in the project
• The suggestion looks like a refactor disguised as a fix — it changes more than necessary
• You cannot understand what the suggestion does or why it's better

Important: Do NOT mark a comment DO NOT ACCEPT just because it is labeled "Architect Review" or "Logical Review" or because no code snippet is provided. Architect reviews are important and actionable — you are expected to implement the fix yourself based on the **Prompt for AI Agent** section. Only use DO NOT ACCEPT for architect reviews when the change genuinely requires a broad, multi-file restructuring that cannot be done as a minimal localized patch.

STALE — Code has changed since the review. Assign this when:

• The code at the referenced line no longer matches what the comment describes
• The file has been renamed or deleted

Step 5 — Present the Summary with Verdicts

Before making any changes, present a clear summary to the user:

• PR: #N — "title" (link to PR)
• Total unresolved CodeAnt comments: X

Then list every comment grouped by verdict:

ACCEPT — Safe to apply (N): For each, show:

• File path and line number
• One-line summary of the issue
• One-line explanation of why this is safe: what exactly the fix does and why it cannot break anything
• The actual code change (before → after) so the user can see it

LIKELY ACCEPT — Verify callers (N): For each, show:

• File path and line number
• One-line summary of the issue
• What the fix changes and why it's probably correct
• What could break: specifically which callers, tests, or behaviors to check
• The actual code change (before → after)

DO NOT ACCEPT — Could break logic (N): For each, show:

• File path and line number
• One-line summary of what the comment asks for
• Specific reason why the suggestion is risky — what exactly could break
• What the user should do instead (e.g., "review manually", "check with the team", "test this path first")

STALE — Code changed since review (N): For each, show:

• File path and line number
• What the comment expected to find vs. what's actually there now

Then ask the user: "I will apply the N ACCEPT fixes now. For the LIKELY ACCEPT fixes, I recommend you review the callers first — want me to apply those too, or skip them for now?"

Step 6 — Apply the Fixes

After the user confirms:

• Apply all ACCEPT fixes.
• Apply LIKELY ACCEPT fixes only if the user said yes.
• Do NOT apply DO NOT ACCEPT or STALE fixes.
• Make the smallest possible change that addresses each comment.
• If the fix requires adding an import, add it.
• If multiple comments refer to the same file, apply all fixes to that file before moving to the next file, being careful that fixes don't conflict with each other.

Step 6b — Track Results

After applying fixes, report the outcome:

codeant track --event "suggestions_applied" --props '{"skill_name": "codeant-resolve-pr-comments", "source": "claude-code", "pr_number": <N>, "pr_url": "<PR_URL>", "accept_count": <N>, "likely_accept_count": <N>, "do_not_accept_count": <N>, "stale_count": <N>, "total_comments": <N>}'

Use the actual counts from the verdicts assigned in Step 4. For likely_accept_count, only count ones the user chose to apply.

Step 7 — Report Results

Present a final report:

Applied (N comments):

• For each: file, line, one-line summary of what was changed, and the verdict (ACCEPT or LIKELY ACCEPT).

Not applied — DO NOT ACCEPT (N comments):

• For each: file, line, specific reason the suggestion is risky.

Not applied — STALE (N comments):

• For each: file, line, what changed since the review.

Step 8 — Resolve Applied Conversations

After applying fixes, resolve the corresponding review conversations on the PR so they no longer show as unresolved. For each comment that was successfully applied (ACCEPT and user-approved LIKELY ACCEPT), run:

codeant pr resolve --pr-number <N> --comment-id <COMMENT_ID>

The --comment-id flag takes the comment's id field from Step 2. The CLI will auto-detect the remote and repo, but you can also pass --remote and --name explicitly.

Platform-specific notes:

• GitHub: Uses --comment-id (the numeric comment ID). The CLI resolves the review thread containing that comment via the GraphQL API. If you already have the GraphQL thread node ID, you can pass --thread-id instead.
• GitLab: Uses --discussion-id (the discussion ID from the comment's discussionId field).
• Bitbucket: Uses --comment-id (the numeric comment ID).
• Azure DevOps: Uses --thread-id (the numeric thread ID from the comment's threadId field).

Run these in sequence (one per applied comment). If a resolve call fails (e.g., insufficient permissions), log a warning but do not stop — continue resolving the remaining comments and report any failures in the final summary.

Do NOT resolve:

• Comments marked DO NOT ACCEPT or STALE
• Comments the user chose to skip
• General PR comments (type is "issue")

Step 9 — Offer to Commit and Push

After presenting the final report, check which files were modified:

git status --short

List the changed files to the user and ask:

"These are the files that were changed:

• <file1>
• <file2>
• ...

Would you like me to commit and push these changes to the current branch? You can also tell me to commit only specific files."

• If the user says yes (or specifies which files to include), stage the selected files, create a commit with a clear message summarizing the fixes applied (e.g., "Apply CodeAnt review fixes for PR #N"), and push to the current branch.
• If the user says no or wants to review first, do nothing — leave the changes uncommitted.
• If the user specifies a subset of files, only stage and commit those files.

Step 0 — Ensure codeant-cli is up to date

Before doing anything else, check that the codeant CLI is on the latest version:

npm view codeant-cli version

Compare this with the installed version:

codeant --version

If the installed version is older than the latest published version, update it:

npm install -g codeant-cli@latest

If the update fails (e.g., permission error), warn the user and continue — a slightly outdated CLI is better than blocking the entire workflow.

Important Rules

• Do NOT modify files that are not referenced in the comments.
• Do NOT apply a suggestion if you cannot verify it is safe. It is always better to skip and explain than to break the code.
• Do NOT batch-apply suggestions blindly. Validate each one individually.
• If two comments conflict (e.g., one says add a check, another says remove it), flag both and ask the user.
• Keep fixes minimal. A fix for a missing null check should add the null check — not restructure the function.