nemoclaw-maintainer-cross-issue-sweep - SKILL.md Agent Skill

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

The pr-comparator (nemoclaw-maintainer-pr-comparator) calls this skill as a sub-step when comparing competing PRs. Adjacent-fix counts feed Tier 3 tiebreakers; contradicting hits factor into Tier 2 quality scoring.

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