review-issues-severity

# Review Issues by Severity Parse `$ARGUMENTS` for optional filters: - Label filters (e.g., `question`, `bug`) - Focus areas (e.g., "merge disposition", "Arrow", "ClickHouse") - If absent, perform a broad review across all open issues. ## Steps ### 1. Gather candidate issues Run the following GitHub API queries **in parallel** to surface issues matching different severity signals: #### a) Long discussions (high comment count) ```bash gh api 'repos/dlt-hub/dlt/issues?state=open&per_page=100&sort=comments&direction=desc&page=1' \ --jq '.[] | "\(.number)\t\(.comments)\t\(.updated_at | split("T")[0])\t\(.created_at | split("T")[0])\t\([.labels[].name] | join(","))\t\(.title)"' ``` #### b) Issues with specific labels (if filters provided) ```bash gh api 'repos/dlt-hub/dlt/issues?state=open&labels=LABEL&per_page=100&sort=comments&direction=desc' \ --jq '.[] | "\(.number)\t\(.comments)\t\(.updated_at | split("T")[0])\t\(.created_at | split("T")[0])\t\([.labels[].name] | join(","))\t\(.title)"' ``` #### c) Recently stale issues (updated 2+ months ago, with maintainer comments) Look for issues where `updated_at` is old relative to the current date but had prior maintainer engagement. ### 2. Deep-dive top candidates For the top 10-15 most promising candidates, fetch full details using a subagent or parallel `gh issue view` calls: ```bash gh issue view -R dlt-hub/dlt NUMBER --json title,body,comments,labels,createdAt,updatedAt,author,assignees ``` For each issue, extract: - **Participants:** who commented, their association (MEMBER = maintainer, NONE/CONTRIBUTOR = community) - **Timeline:** when maintainers last engaged, how long since last response - **Problem description:** what breaks and under what conditions - **Reproduction quality:** is there a clean repro script? - **Current status:** fixed? workaround? stalled? abandoned? - **Production severity:** does this cause data loss, crashes, or silent corruption? ### 3. Classify each issue Apply these criteria to each issue: | Signal | What to look for | |--------|-----------------| | **Serious problem** | Data corruption, silent data loss, hard crashes, cascading failures, security issues | | **Long discussion** | 5+ comments, especially with multiple community reporters hitting the same issue | | **Maintainer abandoned** | Maintainer commented but last maintainer response is 2+ months old, no linked PR, reporter follow-ups unanswered | | **Question label** | Issue has `question` label — these often represent real bugs initially miscategorized | | **Community effort** | Reporter provided detailed repro scripts, root-cause analysis, or proposed fixes that went unacknowledged | ### 4. Prioritize Assign priority tiers: - **P0 — Critical:** Silent data corruption, cascading failures, confirmed root cause with no fix. Production systems at risk. - **P1 — High:** Hard crashes in common deployment patterns, blocking features for significant user segments, confirmed bugs with stalled fixes. - **P2 — Medium:** Broken features in specific environments, intermittent failures, missing warnings that lead users astray. - **P3 — Low:** Documentation gaps, environment-specific issues with known workarounds, feature requests with community PRs pending review. Within each tier, rank by: 1. Seriousness of the problem in production (data loss > crash > degraded performance > inconvenience) 2. Negative community impact if not solved (effort wasted by reporters, users hitting the issue independently) 3. Amount of effort already invested by reporters (repro scripts, root-cause analysis, proposed PRs) ### 5. Output format Present results as: #### Per-issue analysis (grouped by priority tier) For each issue: - **Issue link, title, comment count, last activity date** - **Problem:** 2-3 sentence description of what breaks - **Why this priority:** key severity signal - **Abandonment signal:** when maintainer last engaged, what was left unresolved - **Community effort:** what the reporter invested (repro scripts, analysis, PRs) #### Summary matrix A table with columns: Priority | Issue # | Type | Production Impact | Community Effort | Maintainer Status #### Cross-cutting patterns Note any clusters of related issues (e.g., multiple issues sharing a root cause) or systemic patterns in maintainer response.