Scan open issues to find issues that a PR can also fix or conflict with. Report each relationship with file and line evidence. Use this skill during PR review to find related fixes and risks.
npx skills add https://github.com/NVIDIA/NemoClaw --skill nemoclaw-maintainer-cross-issue-sweep
<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->
<!-- SPDX-License-Identifier: Apache-2.0 -->
Find open issues that a PR can affect in addition to its linked issue. Report two relationship types:
gh CLI authenticatedThe defaults use NemoClaw conventions. Edit repo-policy.md for another repository.
Copy this checklist into your response and check off each step:
Cross-issue sweep progress:
- [ ] Step 1: Extract fingerprint (files, symbols, error strings, primary issue)
- [ ] Step 2: Search candidate issues (capped at 30, primary excluded)
- [ ] Step 3: Classify each candidate (4-class with evidence)
- [ ] Step 4: Apply reverse-link boost
- [ ] Step 5: Filter (drop UNRELATED, SAME_ISSUE_DIFF, low-confidence)
- [ ] Step 6: Render report using templates/report.md
scripts/extract-fingerprint.sh <pr-number>
The script collects changed files, changed symbols, error strings, and the linked issue.
See checks/fingerprint-extraction.md.
scripts/search-candidate-issues.sh <fingerprint-json>
Search these three inputs. Keep no more than 30 candidates:
Remove duplicates and the linked issue.
Classify each candidate with the rules in checks/relationship-judgment.md:
For ADJACENT_FIX or CONTRADICTING, cite:
Classify the issue as UNRELATED if this evidence is not available.
Increase confidence by one level if the issue body or comments mention the PR number.
scripts/render-report.py < classifications.json
See templates/report.md for the format.
scripts/extract-fingerprint.sh — symbols, paths, and error stringsscripts/search-candidate-issues.sh — GitHub Search wrapper, dedupe, capscripts/render-report.py — report rendererThis skill is an optional follow-up to nemoclaw-maintainer-pr-comparator.
The comparator does not run this skill or use its findings in the score.
Run this skill when a maintainer asks for related-issue evidence. Report the evidence separately.
The skill does not:
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Take nvidia/nemoclaw-maintainer-cross-issue-sweep from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.