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hassio-addons/.github/prompts/issue-execute-plan.md
alexbelgium ddf06a7647 AI triage v2: plan-then-approve, manual precedence, ready PRs 
Redesign the AI issue-triage pipeline so it can fix confidently on its own,
ask for approval only when unsure, and always yield to manual actions — while
staying cheap and fast.

- Tier 2 (daily_ai_fix) becomes graded: high-confidence small fixes open a
  READY-for-review PR; anything medium/large gets a full Opus-written plan
  comment (ai:plan-pending) instead of a PR.
- New Tier 3 (on_issue_approved): maintainer adds ai:approved and the posted
  plan is executed on Opus into a ready PR — immediate, zero cost until asked.
- New @claude interactive workflow (on_claude_mention): maintainer-only,
  Sonnet-low, full precedence over the automated tiers.
- New CodeRabbit follow-up (on_pr_coderabbit): one-shot Sonnet pass that fixes
  or replies to CodeRabbit's review of an ai-fix/* PR.
- Tier 1 self-healing: a reporter's reply to a needs-info request re-runs
  classification exactly once (ai:needs-info); a daily catch-up re-dispatches
  any issue that never got triaged.
- Extras: global kill switch (repo var AI_DISABLED), stale-bot exemption for
  AI labels, shared ai_guard_paths.sh, track_progress on the Opus tiers,
  CLAUDE.md documentation.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-24 07:08:01 +02:00

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Execute an approved plan — tier 3

@alexbelgium reviewed an AI-written plan and approved it. Your job is to carry that plan out and open a pull request. The plan was already accepted, so do not re-litigate it — execute it. The only judgement left to you is whether the plan still applies to the current source.

Read:

  • /tmp/ai-exec/plan.md — the approved plan (root cause, the exact files and diff, verification, risk). This is your spec.
  • /tmp/ai-exec/issue.json — the issue it fixes (for Closes #<n> and context).

Hard limits (identical to the fix sweep — a workflow step enforces them)

  1. Never modify .github/ or .templates/. Repo-wide infrastructure.
  2. Never touch the version or upstream fields in config.yaml.
  3. One add-on, one branch: ai-fix/<addon>-<issue-number>.
  4. Never merge, never close the issue, never enable auto-merge. Open the pull request ready for review — CI (onpr_check-pr.yaml) validates it, a human ships it.

Do this in order

  1. Apply the plan. Make exactly the edits it describes. Match surrounding style (bash / Dockerfile; conventions vary per add-on). Run shellcheck on any shell you change. Add a CHANGELOG.md entry in the add-on's format.

  2. If the plan is stale — the source moved since it was written and the diff no longer applies cleanly:

    • Small drift (a line shifted, a nearby rename): adapt minimally to achieve the plan's stated intent, and note the deviation in the PR body.
    • Large drift (the root cause or the target code is gone or now different): stop. Do not guess a new fix. Comment on the issue explaining why the plan no longer applies, relabel ai:needs-human (see step 5), open no PR.

  3. Open the pull request, ready for review. Body: the root cause with file and line, what the change does, how you verified it (or an explicit statement that you could not), any deviation from the plan, and Closes #<n>. Note that it was executed from an approved plan.

  4. Comment on the issue with the root cause in plain language (the reader is a Home Assistant user) and the pull request link. Close with a note that this is automated analysis pending Alex's review.

  5. Relabel, as your last action:

    gh issue edit <n> --remove-label ai:approved --remove-label ai:plan-pending --add-label <result>

    <result> is ai:fixed if you opened a PR, or ai:needs-human if the plan was too stale to apply (step 2). A workflow step also strips the approval labels afterwards and flags ai:needs-human if no PR resulted — treat that as a bug in your run, not a safety net.

Keep the change within the spirit of the approved plan. If carrying it out honestly requires substantially more than the plan described, that is a sign the plan was wrong — stop and relabel ai:needs-human rather than expanding scope.