Issue Hunter — Issue Analysis & Selection

Overview

Systematically analyze open issues in target repositories, score them by fixability, and select the best candidates to work on. Produces a structured analysis document with root cause hypotheses and fix approaches.

Use cases: Open-source contribution targeting, sprint planning, backlog grooming, bug triage.

Prerequisites

GitHub CLI must be authenticated — issue fetching relies on gh API calls:

gh auth status   # Must show "Logged in"

If not configured, ask the user to provide:

1. GitHub username
2. GitHub token — run gh auth login or set export GH_TOKEN=

Without auth, API rate limits will block bulk issue fetching.

Workflow

Step 1: Fetch Issues

For each target repository, fetch open issues:

# Fetch bug-labeled issues
gh issue list --repo {owner}/{repo} --label bug --state open --limit 30 \
    --json number,title,body,labels,comments,createdAt,updatedAt

# Also check these labels
gh issue list --repo {owner}/{repo} --label "good first issue" --state open --limit 10 \
    --json number,title,body,labels,comments,createdAt

gh issue list --repo {owner}/{repo} --label "help wanted" --state open --limit 10 \
    --json number,title,body,labels,comments,createdAt

If no label filtering works, fetch recent open issues:

gh issue list --repo {owner}/{repo} --state open --limit 50 \
    --json number,title,body,labels,comments,createdAt

Step 2: Score Each Issue

Rate each issue on these factors:

Total score = sum of all factors (max 18).

Step 3: Select Candidates

Selection criteria (in priority order):

1. Clearly a bug (not a feature request in disguise)
2. Self-contained (fixable without deep domain knowledge)
3. Testable (can write automated tests to verify)
4. No existing PR addressing it
5. Score ≥ 12/18

Red flags — skip issues if:

• Already has an open PR (check with gh pr list --repo owner/repo --search "fixes #{number}")
• Requires access to proprietary services/APIs
• Involves native/platform-specific code you can't test
• Maintainer has said "won't fix" or "by design"

Step 4: Deep-Dive Selected Issues

For each selected issue:

1. Read the full thread — comments often contain root cause hints or partial fixes
2. Identify relevant source files — search the codebase for keywords from the issue
3. Draft a root cause hypothesis — "I believe X happens because Y"
4. Draft a fix approach — "I'll modify Z to handle the W case"
5. Estimate effort — Low (< 1 hour), Medium (1-4 hours), High (4+ hours)

Step 5: Produce Analysis Document

Write {workspace}/issue-analysis.md:

# Issue Analysis — {Repository/Campaign Name}

> Generated: {date}

## Summary

| # | Repository | Issue | Title | Score | Effort | Status |
|---|-----------|-------|-------|-------|--------|--------|
| 1 | owner/repo | #123 | Bug title | 16/18 | Low | Selected |
| 2 | owner/repo | #456 | Bug title | 12/18 | Medium | Selected |
| 3 | owner/repo | #789 | Bug title | 8/18 | High | Skipped |

---

## Detailed Analysis

### owner/repo — Selected: #123 — Bug title

- **Score**: 16/18 (Repro: 3, Scope: 3, Complexity: 3, Signal: 2, Fresh: 3, Testable: 2)
- **Root cause**: {hypothesis}
- **Fix approach**: {description}
- **Files to modify**: `path/to/file.py`, `tests/test_file.py`
- **Effort estimate**: Low (< 1 hour)
- **Risk**: Low — straightforward null check
- **Dependencies**: None

### owner/repo — Skipped: #789 — Bug title
- **Reason**: Requires architecture redesign, score 8/18

Output

• {workspace}/issue-analysis.md — Per-repo issue analysis with selections

Tips

• Pairs naturally with repo-scout (upstream) and repo-setup / dev-test (downstream).
• For sprint planning: use the scoring system on your own project's issues to prioritize.
• For backlog grooming: run periodically to re-score issues as new information appears.
• When analyzing a single repo, skip the summary table and go straight to detailed analysis.