Issue

/afc:issue — GitHub Issue Analysis

Analyzes a single GitHub issue (title, body, labels, comments, attached images) and produces a structured analysis document. Searches the codebase for related files and suggests the appropriate next afc skill (debug, spec, or auto).

Pre-fetch

!gh --version >/dev/null 2>&1 && gh auth status >/dev/null 2>&1 && echo "GH_OK" || echo "GH_UNAVAILABLE"

Arguments

• $ARGUMENTS — (required) One of:
  • Issue number: 123 or #123
  • GitHub URL: https://github.com/owner/repo/issues/123
  • Cross-repo: owner/repo#123

Execution Steps

1. Prerequisites Check

If the pre-fetch returned GH_UNAVAILABLE:

• Output: [afc:issue] Error: GitHub CLI (gh) is not installed or not authenticated. Install from https://cli.github.com/ and run 'gh auth login'.
• Abort immediately.

2. Parse Input

Determine the input format and extract owner, repo, and issue number:

1. GitHub URL (https://github.com/{owner}/{repo}/issues/{number}): extract from path, set GH_REPO_FLAG="--repo {owner}/{repo}"
2. Cross-repo ({owner}/{repo}#{number}): split on #, set GH_REPO_FLAG="--repo {owner}/{repo}"
3. Local number (123 or #123): strip leading #, set GH_REPO_FLAG="" (use current repo from git remote)

3. Collect Issue Data

gh issue view {number} {GH_REPO_FLAG} --json number,title,body,labels,author,comments,createdAt,state,url

If the command fails → output error and abort.

Parse and extract: TITLE, BODY, LABELS, AUTHOR, COMMENTS, CREATED_AT, STATE, URL.

4. Analyze Attached Images

Extract image URLs from the issue body (skip images inside code block fences):

• Markdown: ![alt]({url})
• HTML: <img src="{url}">

For each URL: fetch with WebFetch, analyze content (error messages, UI screenshots, console output, stack traces), tag results with [Image Analysis]. Record failures as [Image unavailable: {url}].

If no images: note No attached media found.

5. Analyze Comments

If more than 20 comments: analyze only the most recent 20 and note "Analyzed 20 of {N} comments (most recent)".

Extract: reproduction steps, error logs, workarounds, maintainer responses, related issue/PR references.

6. Search Codebase

Extract keywords from title, body, and comments:

• Error messages (quoted strings, stack trace patterns)
• Function/class/module names, file paths, project-specific technical terms

Search with Grep and Glob. Record matching files with line numbers and relevance reason. If no matches: No related code found in current codebase.

7. Classify Issue

Type — choose the strongest matching signal:

Signal	Type
Error messages, stack traces, "broken", "doesn't work", reproduction steps	Bug Report
"Add", "support", "new feature", "would be nice", "enhancement"	Feature Request
"How to", "is it possible", "what is", "documentation"	Question
"Improve", "refactor", "better", "optimize", existing feature modification	Enhancement

Severity — based on production impact, not code location:

Level	Criteria
Critical	Data loss, security vulnerability, application crash, or complete feature unavailability with no workaround
High	Core functionality broken; workaround exists but is painful or undocumented
Medium	Non-critical functionality impacted; reasonable workaround available, or significant UX degradation
Low	Cosmetic issue, minor UX friction, or improvement with no functional impact

Estimated Scope:

Level	Criteria
Small	1–2 files — typo, config change, isolated bug fix
Medium	3–5 files — feature addition, moderate refactor
Large	6+ files — cross-cutting concern, architectural change

8. Determine Next Step

Type	Suggested Next Step
Bug Report	/afc:debug "{issue title summary}"
Feature Request	/afc:spec "{feature description}" or /afc:auto "{feature description}"
Question	Reply to issue — provide answer or point to documentation
Enhancement	/afc:spec "{enhancement description}"
Insufficient info	Reply to issue — request: {specific missing information}

9. Save Analysis Document

mkdir -p .claude/afc/issues

If .claude/afc/issues/{number}-*.md exists → ask user: "Overwrite existing analysis for issue #{number}?"

Generate slug from title: lowercase, replace non-alphanumeric with -, truncate to 40 chars.

Write to .claude/afc/issues/{number}-{slug}.md:

# Issue #{number}: {title}

> Analyzed: {YYYY-MM-DD}
> Source: {url}
> Labels: {labels, comma-separated}
> Author: {author}
> State: {Open/Closed}

## Summary

{2-4 sentence summary including context from body, comments, and images — not just a restatement of the title.}

## Attached Media Analysis

{If images exist:}
> [Image Analysis] Below is AI interpretation of attached media.

- **Image 1** ({filename or "screenshot"}): {description}

{If no images:}
No attached media found.

## Codebase Impact

- `{path}:{line}` — {why this file is related}

{If no related files:}
No related code found in current codebase.

## Classification

- **Type**: {Bug Report | Feature Request | Question | Enhancement}
- **Severity**: {Critical | High | Medium | Low}
- **Estimated Scope**: {Small (1-2 files) | Medium (3-5) | Large (6+)}

## Suggested Next Step

- [ ] {Primary suggestion with full command} — {reason}
- [ ] {Secondary suggestion if applicable} — {reason}

10. Output Summary

Issue analyzed
├─ Issue: #{number} — {title}
├─ Type: {classification}
├─ Severity: {severity}
├─ Related files: {count}
├─ Document: .claude/afc/issues/{number}-{slug}.md
└─ Next step: {primary suggestion}

Notes

• Read-only: Does not modify any code. Only creates an analysis document.
• Image analysis is best-effort: AI interpretation of screenshots may be imprecise — [Image Analysis] tag makes this explicit.
• Not part of auto pipeline: Standalone skill, invoked manually.
• Relationship to triage: afc:triage handles bulk analysis; afc:issue handles deep individual analysis.
• Comment limit: Max 20 comments analyzed (most recent prioritized).