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Discovery & Batch Selection

agents/projects/code-health/accessibility-cleanup/references/discovery_and_batch_selection.md

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Discovery & Batch Selection

Follow this workflow to identify candidates to process.

Steps

  1. Discovery: Delegate to the generalist sub-agent with this exact prompt:

    "Run the discovery script find_candidates.py located in the skill's scripts/ directory:

    bash
    python3 agents/projects/code-health/accessibility-cleanup/scripts/find_candidates.py

    For the candidates returned by the script, triage them one-by-one (by reading the file contents) to determine if they represent a genuine accessibility issue as described in references/patterns.md, or compare against general known common accessibility issues/mistakes. Skip any candidates that are false positives (e.g., correct API contracts or already using proper delegates). Exclude any previously rejected files (if provided by the user).

    If a candidate represents a genuine anti-pattern, return its details.

    If all candidates from the script are false positives, or if the script returns 'No candidates found', perform an open-ended codebase search using code search tools to find other accessibility anti-patterns (e.g., searching for suspect uses of announceForAccessibility, setAccessibilityLiveRegion with assertive, custom AccessibilityActionCompat label overrides, or proactive requestFocus focus jumps).

    Return details for the first valid candidate found (File Path, matching lines, and a short explanation of why it is an issue)."

  2. Present Candidate & Triaging Loop: Output the candidate details to the user. Announce the candidate with exactly this message format (replace the bracketed details with the findings): "I've identified a candidate for accessibility cleanup:

    • File: [File Path]
    • Issue Lines: [Issue Lines]
    • Reason: [Brief Explanation]"

    Ask the user for confirmation: "Shall I proceed with the cleanup of this file?"

    • If the user agrees: Proceed to Refactoring & Implementation.
    • If the user rejects the candidate: Loop back to Step 1 (AI-Led Discovery & Analysis), instructing the sub-agent to find the next candidate while explicitly excluding the rejected file path. Repeat this loop until a candidate is confirmed by the user.