nemoclaw-maintainer-cross-issue-sweep

name	nemoclaw-maintainer-cross-issue-sweep
description	Scans other open issues to find ones a given PR may also fix or accidentally break. Outputs adjacent-fix opportunities and contradiction risks with file:line evidence. Use when reviewing a PR to discover bundling opportunities or downstream impact across the issue queue.
user_invocable	true

Cross-Issue Regression Sweep

Surfaces the issues a single PR may also fix or accidentally break beyond the one it claims to address. Two outputs:

• Adjacent fixes — "PR may also close #X" → bundling intel (ship one PR, close multiple issues)
• Contradicting risks — "PR may break what #Y wants" → coordination needed before merge

Prerequisites

• gh CLI authenticated
• A target repository with open issues
• An open PR to scan

Repo policy

Defaults assume NemoClaw conventions. Edit repo-policy.md to override per-repo (bot logins, candidate caps, language regex).

Workflow

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

Step 1: Extract fingerprint

scripts/extract-fingerprint.sh <pr-number>

Pulls four dimensions: touched files, touched symbols (per-language regex), error-string tokens, and the PR's primary linked issue (for exclusion). See checks/fingerprint-extraction.md.

Step 2: Search candidate issues

scripts/search-candidate-issues.sh <fingerprint-json>

Three search dimensions, capped at 30 total candidates:

• Per symbol: top 10 by recency
• Per file path: top 5 by recency
• Per error string: top 5 by recency

Dedupes; excludes the PR's primary linked issue.

Step 3: Classify each candidate

For each candidate, the LLM classifies as one of four classes per checks/relationship-judgment.md:

• ADJACENT_FIX — PR's changes likely also resolve this issue
• CONTRADICTING — PR's approach blocks what this issue wants
• SAME_ISSUE_DIFF — same root bug as PR's primary issue (dedup filter)
• UNRELATED — no meaningful relationship

Required for ADJACENT_FIX or CONTRADICTING:

• Cite specific PR diff line
• Cite specific issue symptom
• Confidence: high / medium / low

If no specific evidence can be cited, the LLM must answer UNRELATED. This floors hallucination.

Step 4: Reverse-link boost

If the candidate issue's body or comments already mention this PR's number, the relationship is already in someone's mental model. Boost confidence by one tier (low → medium, medium → high).

Step 5: Filter

• Suppress UNRELATED + SAME_ISSUE_DIFF
• Drop low-confidence judgments
• Keep ADJACENT_FIX and CONTRADICTING with high or medium confidence

Step 6: Render report

scripts/render-report.py < classifications.json

See templates/report.md for the format.

Reference files

• repo-policy.md — configurable per-repo defaults
• relationship-rules.md — 4-class definitions with worked examples
• checks/fingerprint-extraction.md — what to pull from the diff, per language
• checks/relationship-judgment.md — LLM judgment criteria + evidence requirement
• templates/report.md — output template
• validation/backtest.md — backtest the skill against historical PRs

Scripts (execute, do not read)

• scripts/extract-fingerprint.sh — symbols + paths + error strings, deterministic
• scripts/search-candidate-issues.sh — GitHub Search wrapper, dedupe, cap
• scripts/render-report.py — report renderer

Composition with other skills

This skill is a separate, optional follow-up to nemoclaw-maintainer-pr-comparator. The comparator does not call it or include its findings in the deterministic score. Run the sweep explicitly when a maintainer wants adjacent-fix or contradiction evidence alongside the comparator verdict, and report that evidence separately.

What this skill does NOT do (deferred)

These would raise the ceiling but require infrastructure beyond GitHub API + LLM:

• Run PR code against adversarial inputs (sandboxed)
• Static-analyzer dataflow tracing (CodeQL, Semgrep)
• ML-based symbol disambiguation across codebases