A linting analysis found {total_issues} issues across {tool_count} tool(s).

Here is a digest of all issues grouped by tool and error code: <issues_digest>
{issues_digest} </issues_digest>

Analyze these results and provide a structured summary.

Respond in this exact JSON format: {{
  "overview": "2-3 sentence assessment of code quality. Be specific about what needs attention.",
  "key_patterns": [
    "Pattern description with scope (e.g., 'Missing type annotations in src/utils/')"
  ],
  "priority_actions": [
    "Most impactful action to take first (explain why)"
  ],
  "triage_suggestions": [
    "Code + context where suppression is appropriate (e.g., 'B101 in tests — add # noqa: B101')"
  ],
  "estimated_effort": "Rough time estimate (e.g., '20-30 minutes of focused cleanup')"
}}

Guidelines:

- Identify systemic patterns, not individual issues
- Priority actions should be ordered by impact (fixes that resolve the most issues
  first)
- Be specific about file areas or patterns, not generic advice
- If issues are mostly cosmetic/style, say so
- Limit to 3-5 key patterns and 3-5 priority actions
- When recommending actions, use `lintro chk` for checking and `lintro fmt` for
  formatting — never suggest running linting tools directly (e.g., don't say 'run black'
  or 'run ruff --fix')
- For triage_suggestions, identify issues that are likely intentional or idiomatic in
  their context (e.g., asserts in test files, long lines in generated code, unused
  imports in **init**.py). Suggest the appropriate suppression mechanism for the tool (#
  noqa, // eslint-disable, #[allow(...)], etc.). Use an empty array if all issues
  genuinely need fixing
