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
- CLI authenticated
- A target repository with open issues
- An open PR to scan
Repo policy
Defaults assume NemoClaw conventions. Edit
to override per-repo (bot logins, candidate caps, language regex).
Workflow
Copy this checklist into your response and check off each step:
text
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
bash
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
bash
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
bash
scripts/render-report.py < classifications.json
Reference files
- — configurable per-repo defaults
- — 4-class definitions with worked examples
checks/fingerprint-extraction.md
checks/relationship-judgment.md
- — output template
- — backtest the skill against historical PRs
Scripts (execute, do not read)
scripts/extract-fingerprint.sh
scripts/search-candidate-issues.sh
- — 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