autofix-repo-resolve

name	autofix-repo-resolve
description	Disambiguate which repository URL is the actual target when a Jira ticket mentions multiple repos. Reads ticket context and candidate URLs, classifies each URL, and writes a verdict with the target and confidence level.
allowed-tools	Read Write Bash
metadata	{"x-artifacts":"agent-output.txt .autofix-context/repo-resolve-verdict.json"}

Skill: Repo URL Disambiguation

You are resolving which repository is the actual target of a Jira bug ticket that mentions multiple repository URLs. Your goal is to determine which single repo the bug should be fixed in.

Step 1: Read inputs

1. Read .autofix-context/ticket.json for the ticket description, comments, and Jira component name.
2. Read .autofix-context/repo-candidates.json for the list of candidate repository URLs detected in the ticket.

Step 2: Analyze the ticket context

For each candidate URL, classify it by reading the surrounding context in the ticket description and comments:

• target: The repo the bug is actually about. Look for:
  • Explicit statements: "the impacted repository is...", "target repo:", "affected repo:", "this is a bug in..."
  • The repo discussed alongside the actual symptoms, error messages, or stack traces
  • The repo that matches the Jira component name
• contextual: Mentioned for background but not the target. Look for:
  • Cross-references: "see also", "related to", "similar issue in..."
  • Reproduction environments, test fixtures, or demo repos
  • Repos mentioned in steps-to-reproduce as tools or dependencies, not as the thing that's broken
• negative: Explicitly stated as NOT the problem. Look for:
  • "this is NOT a X issue", "not related to Y", "X is functioning correctly"
  • "removed X component", "not a code issue in Y"
• unclear: Cannot determine the relationship from context.

Step 3: Determine target and confidence

• high: One URL has an explicit target statement, or only one URL remains after filtering out negative and contextual mentions.
• medium: One URL is more likely the target based on proximity to bug symptoms, but no explicit statement. Or, the Jira component name aligns with one candidate.
• low: Multiple URLs remain plausible targets after analysis. No clear signal.

If confidence is low, set target_url to the most likely candidate but note the ambiguity in reasoning.

Step 4: Write verdict

Write the verdict to .autofix-context/repo-resolve-verdict.json:

{
  "target_url": "https://github.com/org/repo",
  "confidence": "high",
  "reasoning": "The description explicitly states 'The impacted repository is: odh-model-controller' under Other Information.",
  "candidates": [
    {"url": "https://github.com/opendatahub-io/odh-model-controller", "classification": "target"},
    {"url": "https://github.com/opendatahub-io/odh-dashboard", "classification": "negative"},
    {"url": "https://github.com/rh-aiservices-bu/fraud-detection", "classification": "contextual"}
  ]
}

Validate the verdict:

uv run --script ${CLAUDE_SKILL_DIR}/scripts/write_json.py \
  ${CLAUDE_SKILL_DIR}/schemas/repo-resolve-verdict.json \
  .autofix-context/repo-resolve-verdict.json \
  --input .autofix-context/repo-resolve-verdict.json

uv run --script ${CLAUDE_SKILL_DIR}/scripts/validate_verdict.py \
  .autofix-context/repo-resolve-verdict.json

If validation fails, fix the JSON and re-validate.

Gotchas

• Always classify ALL candidate URLs, not just the target.
• The ticket text may contain URLs in Jira markup format (e.g., [text|url] or [url]). Parse both plain URLs and markup links.
• Do not access any repository or clone any code. This is a text-analysis-only skill.
• When the Jira component name clearly maps to one of the candidate repos (e.g., component "AI Core Dashboard" and candidate odh-dashboard), use this as a signal but not as the sole basis for high confidence -- the ticket text may indicate a different repo is the actual target.