"""Evaluate stage — PR lifecycle orchestration.

Tier-based review routing. Model diversity: GPT-4o (domain) + Sonnet (Leo STANDARD)
+ Opus (Leo DEEP) = two model families, no correlated blind spots.

Flow per PR:
  1. Triage → Haiku (OpenRouter) → DEEP / STANDARD / LIGHT
  2. Tier overrides:
     a. Claim-shape detector: type: claim in YAML → STANDARD min (Theseus)
     b. Random pre-merge promotion: 15% of LIGHT → STANDARD (Rio)
  3. Domain review → GPT-4o (OpenRouter) — skipped for LIGHT when LIGHT_SKIP_LLM=True
  4. Leo review → Opus DEEP / Sonnet STANDARD (OpenRouter) — skipped for LIGHT
  5. Post reviews, submit formal Forgejo approvals, update SQLite
  6. If both approve → status = 'approved' (merge module picks it up)
  7. Retry budget: 3 attempts max, disposition on attempt 2+

Design reviewed by Ganymede, Rio, Theseus, Rhea, Leo.
LLM transport and prompts extracted to lib/llm.py (Phase 3c).
"""

import json
import logging
import random
from datetime import datetime, timezone

from . import config, db
from .domains import agent_for_domain, detect_domain_from_branch, detect_domain_from_diff
from .eval_parse import (
    deterministic_tier,
    diff_contains_claim_type,
    extract_changed_files,
    filter_diff,
    is_musings_only,
    parse_batch_response,
    parse_issues,
    parse_verdict,
    validate_batch_fanout,
)
from .forgejo import api as forgejo_api
from .forgejo import get_agent_token, get_pr_diff, repo_path
from .merge import PIPELINE_OWNED_PREFIXES
from .llm import run_batch_domain_review, run_domain_review, run_leo_review, triage_pr
from .eval_actions import dispose_rejected_pr, post_formal_approvals, terminate_pr
from .pr_state import approve_pr, close_pr, reopen_pr, start_review
from .validate import load_existing_claims

logger = logging.getLogger("pipeline.evaluate")


# ─── NOTE: Tier 0.5 mechanical pre-check moved to validate.py ────────────
# Tier 0.5 now runs as part of the validate stage (before eval), not inside
# evaluate_pr(). This prevents wasting eval_attempts on mechanically fixable
# PRs. Eval trusts that tier0_pass=1 means all mechanical checks passed.


# ─── Single PR evaluation ─────────────────────────────────────────────────


async def evaluate_pr(conn, pr_number: int, tier: str = None) -> dict:
    """Evaluate a single PR. Returns result dict."""
    from . import costs
    pr_cost = 0.0

    # Check eval attempt budget before claiming
    row = conn.execute("SELECT eval_attempts FROM prs WHERE number = ?", (pr_number,)).fetchone()
    eval_attempts = (row["eval_attempts"] or 0) if row else 0
    if eval_attempts >= config.MAX_EVAL_ATTEMPTS:
        # Terminal — hard cap reached. Close PR, tag source.
        logger.warning("PR #%d: eval_attempts=%d >= %d, terminal", pr_number, eval_attempts, config.MAX_EVAL_ATTEMPTS)
        await terminate_pr(conn, pr_number, "eval budget exhausted")
        return {"pr": pr_number, "terminal": True, "reason": "eval_budget_exhausted"}

    # Atomic claim — prevent concurrent workers from evaluating the same PR (Ganymede #11)
    if not start_review(conn, pr_number):
        logger.debug("PR #%d already claimed by another worker, skipping", pr_number)
        return {"pr": pr_number, "skipped": True, "reason": "already_claimed"}

    # Increment eval_attempts — but not if this is a merge-failure re-entry (Ganymede+Rhea)
    merge_cycled = conn.execute(
        "SELECT merge_cycled FROM prs WHERE number = ?", (pr_number,)
    ).fetchone()
    if merge_cycled and merge_cycled["merge_cycled"]:
        # Merge cycling — don't burn eval budget, clear flag
        conn.execute("UPDATE prs SET merge_cycled = 0 WHERE number = ?", (pr_number,))
        logger.info("PR #%d: merge-cycled re-eval, not incrementing eval_attempts", pr_number)
    else:
        conn.execute(
            "UPDATE prs SET eval_attempts = COALESCE(eval_attempts, 0) + 1 WHERE number = ?",
            (pr_number,),
        )
        eval_attempts += 1

    # Fetch diff
    diff = await get_pr_diff(pr_number)
    if not diff:
        # Close PRs with no diff — stale branch, nothing to evaluate
        await close_pr(conn, pr_number, last_error='closed: no diff against main (stale branch)')
        return {"pr": pr_number, "skipped": True, "reason": "no_diff_closed"}

    # Musings bypass
    if is_musings_only(diff):
        logger.info("PR #%d is musings-only — auto-approving", pr_number)
        await forgejo_api(
            "POST",
            repo_path(f"issues/{pr_number}/comments"),
            {"body": "Auto-approved: musings bypass eval per collective policy."},
        )
        approve_pr(conn, pr_number, domain='cross-domain', auto_merge=1,
                   leo_verdict='skipped', domain_verdict='skipped')
        return {"pr": pr_number, "auto_approved": True, "reason": "musings_only"}

    # Reweave bypass — reweave PRs only add frontmatter edges (supports/challenges/
    # related/depends_on/challenged_by). The eval LLM has no context for judging
    # edge correctness and consistently flags factual_discrepancy on valid edges.
    # Leo's manual PR review is the real quality gate for reweave.
    branch_row = conn.execute("SELECT branch FROM prs WHERE number = ?", (pr_number,)).fetchone()
    branch_name = branch_row["branch"] if branch_row else ""
    if branch_name.startswith("reweave/"):
        logger.info("PR #%d is reweave (branch=%s) — auto-approving, Leo reviews manually", pr_number, branch_name)
        await forgejo_api(
            "POST",
            repo_path(f"issues/{pr_number}/comments"),
            {"body": "Auto-approved: reweave structural update (frontmatter edges only). Leo reviews manually."},
        )
        approve_pr(conn, pr_number, domain='cross-domain', auto_merge=1,
                   leo_verdict='skipped', domain_verdict='skipped')
        db.audit(
            conn, "evaluate", "reweave_bypass",
            json.dumps({"pr": pr_number, "branch": branch_name}),
        )
        return {"pr": pr_number, "auto_approved": True, "reason": "reweave_bypass"}

    # NOTE: Tier 0.5 mechanical checks now run in validate stage (before eval).
    # tier0_pass=1 guarantees all mechanical checks passed. No Tier 0.5 here.

    # Filter diff
    review_diff, _entity_diff = filter_diff(diff)
    if not review_diff:
        review_diff = diff
    files = extract_changed_files(diff)

    # Detect domain — try diff paths first, then branch prefix, then 'general'
    domain = detect_domain_from_diff(diff)
    if domain is None:
        pr_row = conn.execute("SELECT branch FROM prs WHERE number = ?", (pr_number,)).fetchone()
        if pr_row and pr_row["branch"]:
            domain = detect_domain_from_branch(pr_row["branch"])
    if domain is None:
        domain = "general"
    agent = agent_for_domain(domain)

    # Update PR domain if not set
    conn.execute(
        "UPDATE prs SET domain = COALESCE(domain, ?), domain_agent = ? WHERE number = ?",
        (domain, agent, pr_number),
    )

    # Step 1: Triage (if not already triaged)
    # Try deterministic routing first ($0), fall back to Haiku triage ($0.001)
    if tier is None:
        tier = deterministic_tier(diff)
        if tier is not None:
            db.audit(
                conn, "evaluate", "deterministic_tier",
                json.dumps({"pr": pr_number, "tier": tier}),
            )
        else:
            tier, triage_usage, _triage_reason = await triage_pr(diff)
            pr_cost += costs.record_usage(
                conn, config.TRIAGE_MODEL, "eval_triage",
                input_tokens=triage_usage.get("prompt_tokens", 0),
                output_tokens=triage_usage.get("completion_tokens", 0),
                backend="openrouter",
            )

    # Tier overrides (claim-shape detector + random promotion)
    # Order matters: claim-shape catches obvious cases, random promotion catches the rest.

    # Claim-shape detector: type: claim in YAML → STANDARD minimum (Theseus)
    if tier == "LIGHT" and diff_contains_claim_type(diff):
        tier = "STANDARD"
        logger.info("PR #%d: claim-shape detector upgraded LIGHT → STANDARD (type: claim found)", pr_number)
        db.audit(
            conn, "evaluate", "claim_shape_upgrade", json.dumps({"pr": pr_number, "from": "LIGHT", "to": "STANDARD"})
        )

    # Random pre-merge promotion: 15% of LIGHT → STANDARD (Rio)
    if tier == "LIGHT" and random.random() < config.LIGHT_PROMOTION_RATE:
        tier = "STANDARD"
        logger.info(
            "PR #%d: random promotion LIGHT → STANDARD (%.0f%% rate)", pr_number, config.LIGHT_PROMOTION_RATE * 100
        )
        db.audit(conn, "evaluate", "random_promotion", json.dumps({"pr": pr_number, "from": "LIGHT", "to": "STANDARD"}))

    conn.execute("UPDATE prs SET tier = ? WHERE number = ?", (tier, pr_number))

    # Update last_attempt timestamp (status already set to 'reviewing' by atomic claim above)
    conn.execute(
        "UPDATE prs SET last_attempt = datetime('now') WHERE number = ?",
        (pr_number,),
    )

    # Check if domain review already completed (resuming after Leo rate limit)
    existing = conn.execute("SELECT domain_verdict, leo_verdict FROM prs WHERE number = ?", (pr_number,)).fetchone()
    existing_domain_verdict = existing["domain_verdict"] if existing else "pending"
    _existing_leo_verdict = existing["leo_verdict"] if existing else "pending"

    # Step 2: Domain review (GPT-4o via OpenRouter)
    # LIGHT tier: skip entirely when LIGHT_SKIP_LLM enabled (Rhea: config flag rollback)
    # Skip if already completed from a previous attempt
    domain_review = None  # Initialize — used later for feedback extraction (Ganymede #12)
    domain_usage = {"prompt_tokens": 0, "completion_tokens": 0}
    leo_usage = {"prompt_tokens": 0, "completion_tokens": 0}
    if tier == "LIGHT" and config.LIGHT_SKIP_LLM:
        domain_verdict = "skipped"
        logger.info("PR #%d: LIGHT tier — skipping domain review (LIGHT_SKIP_LLM=True)", pr_number)
        conn.execute(
            "UPDATE prs SET domain_verdict = 'skipped', domain_model = 'none' WHERE number = ?",
            (pr_number,),
        )
    elif existing_domain_verdict not in ("pending", None):
        domain_verdict = existing_domain_verdict
        logger.info("PR #%d: domain review already done (%s), skipping to Leo", pr_number, domain_verdict)
    else:
        logger.info("PR #%d: domain review (%s/%s, tier=%s)", pr_number, agent, domain, tier)
        domain_review, domain_usage = await run_domain_review(review_diff, files, domain or "general", agent)

        if domain_review is None:
            # OpenRouter failure (timeout, error) — revert to open for retry.
            # NOT a rate limit — don't trigger 15-min backoff, just skip this PR.
            reopen_pr(conn, pr_number)
            if pr_cost > 0:
                conn.execute("UPDATE prs SET cost_usd = cost_usd + ? WHERE number = ?", (pr_cost, pr_number))
            return {"pr": pr_number, "skipped": True, "reason": "openrouter_failed"}

        domain_verdict = parse_verdict(domain_review, agent)
        conn.execute(
            "UPDATE prs SET domain_verdict = ?, domain_model = ? WHERE number = ?",
            (domain_verdict, config.EVAL_DOMAIN_MODEL, pr_number),
        )

        # Post domain review as comment (from agent's Forgejo account)
        agent_tok = get_agent_token(agent)
        await forgejo_api(
            "POST",
            repo_path(f"issues/{pr_number}/comments"),
            {"body": domain_review},
            token=agent_tok,
        )

    # If domain review rejects, skip Leo review (save Opus)
    if domain_verdict == "request_changes":
        logger.info("PR #%d: domain rejected, skipping Leo review", pr_number)
        domain_issues = parse_issues(domain_review) if domain_review else []
        reopen_pr(conn, pr_number, leo_verdict='skipped',
                  last_error='domain review requested changes',
                  eval_issues=json.dumps(domain_issues))
        db.audit(
            conn, "evaluate", "domain_rejected", json.dumps({"pr": pr_number, "agent": agent, "issues": domain_issues})
        )
        db.record_review(
            conn, pr_number, "rejected",
            domain=domain, agent=agent, reviewer=agent, reviewer_model="gpt-4o",
            notes=(domain_review or "")[:4000],
        )

        # Disposition: check if this PR should be terminated or kept open
        await dispose_rejected_pr(conn, pr_number, eval_attempts, domain_issues)

        if domain_verdict != "skipped":
            pr_cost += costs.record_usage(
                conn, config.EVAL_DOMAIN_MODEL, "eval_domain",
                input_tokens=domain_usage.get("prompt_tokens", 0),
                output_tokens=domain_usage.get("completion_tokens", 0),
                backend="openrouter",
            )
        if pr_cost > 0:
            conn.execute("UPDATE prs SET cost_usd = cost_usd + ? WHERE number = ?", (pr_cost, pr_number))
        return {
            "pr": pr_number,
            "domain_verdict": domain_verdict,
            "leo_verdict": "skipped",
            "eval_attempts": eval_attempts,
        }

    # Step 3: Leo review (Opus — only if domain passes, skipped for LIGHT)
    leo_verdict = "skipped"
    leo_review = None  # Initialize — used later for issue extraction
    if tier != "LIGHT":
        logger.info("PR #%d: Leo review (tier=%s)", pr_number, tier)
        leo_review, leo_usage = await run_leo_review(review_diff, files, tier)

        if leo_review is None:
            # DEEP: Opus rate limited (queue for later). STANDARD: OpenRouter failed (skip, retry next cycle).
            reopen_pr(conn, pr_number)
            if domain_verdict != "skipped":
                pr_cost += costs.record_usage(
                    conn, config.EVAL_DOMAIN_MODEL, "eval_domain",
                    input_tokens=domain_usage.get("prompt_tokens", 0),
                    output_tokens=domain_usage.get("completion_tokens", 0),
                    backend="openrouter",
                )
            if pr_cost > 0:
                conn.execute("UPDATE prs SET cost_usd = cost_usd + ? WHERE number = ?", (pr_cost, pr_number))
            reason = "opus_rate_limited" if tier == "DEEP" else "openrouter_failed"
            return {"pr": pr_number, "skipped": True, "reason": reason}

        leo_verdict = parse_verdict(leo_review, "LEO")
        conn.execute("UPDATE prs SET leo_verdict = ? WHERE number = ?", (leo_verdict, pr_number))

        # Post Leo review as comment (from Leo's Forgejo account)
        leo_tok = get_agent_token("Leo")
        await forgejo_api(
            "POST",
            repo_path(f"issues/{pr_number}/comments"),
            {"body": leo_review},
            token=leo_tok,
        )
    else:
        # LIGHT tier: Leo is auto-skipped, domain verdict is the only gate
        conn.execute("UPDATE prs SET leo_verdict = 'skipped' WHERE number = ?", (pr_number,))

    # Step 4: Determine final verdict
    # "skipped" counts as approve (LIGHT skips both reviews deliberately)
    both_approve = leo_verdict in ("approve", "skipped") and domain_verdict in ("approve", "skipped")

    if both_approve:
        # Get PR author for formal approvals
        pr_info = await forgejo_api(
            "GET",
            repo_path(f"pulls/{pr_number}"),
        )
        pr_author = pr_info.get("user", {}).get("login", "") if pr_info else ""

        # Submit formal Forgejo reviews (required for merge)
        await post_formal_approvals(pr_number, pr_author)

        # Auto-merge agent PRs: if branch is NOT pipeline-owned, set auto_merge=1
        # so the merge cycle picks it up without manual intervention.
        branch_row = conn.execute("SELECT branch FROM prs WHERE number = ?", (pr_number,)).fetchone()
        branch_name = branch_row["branch"] if branch_row else ""
        is_agent_pr = not branch_name.startswith(PIPELINE_OWNED_PREFIXES)

        approve_pr(conn, pr_number, domain=domain, auto_merge=1 if is_agent_pr else 0)
        db.audit(
            conn,
            "evaluate",
            "approved",
            json.dumps({"pr": pr_number, "tier": tier, "domain": domain, "leo": leo_verdict, "domain_agent": agent,
                         "auto_merge": is_agent_pr}),
        )
        db.record_review(
            conn, pr_number, "approved",
            domain=domain, agent=agent, reviewer="leo", reviewer_model="sonnet" if tier == "STANDARD" else "opus",
            notes=(leo_review or "")[:4000] if leo_review else None,
        )
        if is_agent_pr:
            logger.info("PR #%d: APPROVED + auto_merge (agent branch %s)", pr_number, branch_name)
        else:
            logger.info("PR #%d: APPROVED (tier=%s, leo=%s, domain=%s)", pr_number, tier, leo_verdict, domain_verdict)
    else:
        # Collect all issue tags from both reviews
        all_issues = []
        if domain_verdict == "request_changes" and domain_review is not None:
            all_issues.extend(parse_issues(domain_review))
        if leo_verdict == "request_changes" and leo_review is not None:
            all_issues.extend(parse_issues(leo_review))

        reopen_pr(conn, pr_number, eval_issues=json.dumps(all_issues))
        # Store feedback for re-extraction path
        feedback = {"leo": leo_verdict, "domain": domain_verdict, "tier": tier, "issues": all_issues}
        conn.execute(
            "UPDATE sources SET feedback = ? WHERE path = (SELECT source_path FROM prs WHERE number = ?)",
            (json.dumps(feedback), pr_number),
        )
        db.audit(
            conn,
            "evaluate",
            "changes_requested",
            json.dumps(
                {"pr": pr_number, "tier": tier, "leo": leo_verdict, "domain": domain_verdict, "issues": all_issues}
            ),
        )
        db.record_review(
            conn, pr_number, "approved-with-changes",
            domain=domain, agent=agent, reviewer="leo",
            reviewer_model="sonnet" if tier == "STANDARD" else "opus",
            notes=(leo_review or domain_review or "")[:4000],
        )
        logger.info(
            "PR #%d: CHANGES REQUESTED (leo=%s, domain=%s, issues=%s)",
            pr_number,
            leo_verdict,
            domain_verdict,
            all_issues,
        )

        # Disposition: check if this PR should be terminated or kept open
        await dispose_rejected_pr(conn, pr_number, eval_attempts, all_issues)

    # Record cost (only for reviews that actually ran)
    if domain_verdict != "skipped":
        pr_cost += costs.record_usage(
            conn, config.EVAL_DOMAIN_MODEL, "eval_domain",
            input_tokens=domain_usage.get("prompt_tokens", 0),
            output_tokens=domain_usage.get("completion_tokens", 0),
            backend="openrouter",
        )
    if leo_verdict not in ("skipped",):
        if tier == "DEEP":
            pr_cost += costs.record_usage(
                conn, config.EVAL_LEO_MODEL, "eval_leo",
                input_tokens=leo_usage.get("prompt_tokens", 0),
                output_tokens=leo_usage.get("completion_tokens", 0),
                backend="max",
            )
        else:
            pr_cost += costs.record_usage(
                conn, config.EVAL_LEO_STANDARD_MODEL, "eval_leo",
                input_tokens=leo_usage.get("prompt_tokens", 0),
                output_tokens=leo_usage.get("completion_tokens", 0),
                backend="openrouter",
            )

    if pr_cost > 0:
        conn.execute("UPDATE prs SET cost_usd = cost_usd + ? WHERE number = ?", (pr_cost, pr_number))

    return {
        "pr": pr_number,
        "tier": tier,
        "domain": domain,
        "leo_verdict": leo_verdict,
        "domain_verdict": domain_verdict,
        "approved": both_approve,
    }


# ─── Rate limit backoff ───────────────────────────────────────────────────

# When rate limited, don't retry for 15 minutes. Prevents ~2700 wasted
# CLI calls overnight when Opus is exhausted.
_rate_limit_backoff_until: datetime | None = None
_RATE_LIMIT_BACKOFF_MINUTES = 15


# ─── Batch domain review ─────────────────────────────────────────────────


async def _run_batch_domain_eval(
    conn, batch_prs: list[dict], domain: str, agent: str,
) -> tuple[int, int]:
    """Execute batch domain review for a group of same-domain STANDARD PRs.

    1. Claim all PRs atomically
    2. Run single batch domain review
    3. Parse + validate fan-out
    4. Post per-PR comments
    5. Continue to individual Leo review for each
    6. Fall back to individual review for any validation failures

    Returns (succeeded, failed).
    """
    from .forgejo import get_pr_diff as _get_pr_diff

    succeeded = 0
    failed = 0

    # Step 1: Fetch diffs and build batch
    pr_diffs = []
    claimed_prs = []
    for pr_row in batch_prs:
        pr_num = pr_row["number"]

        # Atomic claim
        if not start_review(conn, pr_num):
            continue

        # Increment eval_attempts — skip if merge-cycled (Ganymede+Rhea)
        mc_row = conn.execute("SELECT merge_cycled FROM prs WHERE number = ?", (pr_num,)).fetchone()
        if mc_row and mc_row["merge_cycled"]:
            conn.execute(
                "UPDATE prs SET merge_cycled = 0, last_attempt = datetime('now') WHERE number = ?",
                (pr_num,),
            )
            logger.info("PR #%d: merge-cycled re-eval, not incrementing eval_attempts", pr_num)
        else:
            conn.execute(
                "UPDATE prs SET eval_attempts = COALESCE(eval_attempts, 0) + 1, "
                "last_attempt = datetime('now') WHERE number = ?",
                (pr_num,),
            )

        diff = await _get_pr_diff(pr_num)
        if not diff:
            reopen_pr(conn, pr_num)
            continue

        # Musings bypass
        if is_musings_only(diff):
            await forgejo_api(
                "POST",
                repo_path(f"issues/{pr_num}/comments"),
                {"body": "Auto-approved: musings bypass eval per collective policy."},
            )
            approve_pr(conn, pr_num, domain='cross-domain', auto_merge=1,
                       leo_verdict='skipped', domain_verdict='skipped')
            succeeded += 1
            continue

        review_diff, _ = filter_diff(diff)
        if not review_diff:
            review_diff = diff
        files = extract_changed_files(diff)

        # Build label from branch name or first claim filename
        branch = pr_row.get("branch", "")
        label = branch.split("/")[-1][:60] if branch else f"pr-{pr_num}"

        pr_diffs.append({
            "number": pr_num,
            "label": label,
            "diff": review_diff,
            "files": files,
            "full_diff": diff,  # kept for Leo review
            "file_count": len([l for l in files.split("\n") if l.strip()]),
        })
        claimed_prs.append(pr_num)

    if not pr_diffs:
        return 0, 0

    # Enforce BATCH_EVAL_MAX_DIFF_BYTES — split if total diff is too large.
    # We only know diff sizes after fetching, so enforce here not in _build_domain_batches.
    total_bytes = sum(len(p["diff"].encode()) for p in pr_diffs)
    if total_bytes > config.BATCH_EVAL_MAX_DIFF_BYTES and len(pr_diffs) > 1:
        # Keep PRs up to the byte cap, revert the rest to open for next cycle
        kept = []
        running_bytes = 0
        for p in pr_diffs:
            p_bytes = len(p["diff"].encode())
            if running_bytes + p_bytes > config.BATCH_EVAL_MAX_DIFF_BYTES and kept:
                break
            kept.append(p)
            running_bytes += p_bytes
        overflow = [p for p in pr_diffs if p not in kept]
        for p in overflow:
            reopen_pr(conn, p["number"], dec_eval_attempts=True)
            claimed_prs.remove(p["number"])
            logger.info(
                "PR #%d: diff too large for batch (%d bytes total), deferring to next cycle",
                p["number"], total_bytes,
            )
        pr_diffs = kept

    if not pr_diffs:
        return 0, 0

    # Detect domain for all PRs (should be same domain)
    conn.execute(
        "UPDATE prs SET domain = COALESCE(domain, ?), domain_agent = ? WHERE number IN ({})".format(
            ",".join("?" * len(claimed_prs))
        ),
        [domain, agent] + claimed_prs,
    )

    # Step 2: Run batch domain review
    logger.info(
        "Batch domain review: %d PRs in %s domain (PRs: %s)",
        len(pr_diffs),
        domain,
        ", ".join(f"#{p['number']}" for p in pr_diffs),
    )
    batch_response, batch_domain_usage = await run_batch_domain_review(pr_diffs, domain, agent)

    if batch_response is None:
        # Complete failure — revert all to open
        logger.warning("Batch domain review failed — reverting all PRs to open")
        for pr_num in claimed_prs:
            reopen_pr(conn, pr_num)
        return 0, len(claimed_prs)

    # Step 3: Parse + validate fan-out
    parsed = parse_batch_response(batch_response, claimed_prs, agent)
    valid_reviews, fallback_prs = validate_batch_fanout(parsed, pr_diffs, agent)

    db.audit(
        conn, "evaluate", "batch_domain_review",
        json.dumps({
            "domain": domain,
            "batch_size": len(pr_diffs),
            "valid": len(valid_reviews),
            "fallback": fallback_prs,
        }),
    )

    # Record batch domain review cost ONCE for the whole batch (not per-PR)
    from . import costs
    costs.record_usage(
        conn, config.EVAL_DOMAIN_MODEL, "eval_domain",
        input_tokens=batch_domain_usage.get("prompt_tokens", 0),
        output_tokens=batch_domain_usage.get("completion_tokens", 0),
        backend="openrouter",
    )

    # Step 4: Process valid reviews — post comments + continue to Leo
    for pr_data in pr_diffs:
        pr_num = pr_data["number"]

        if pr_num in fallback_prs:
            # Revert — will be picked up by individual eval next cycle
            reopen_pr(conn, pr_num, dec_eval_attempts=True)
            logger.info("PR #%d: batch fallback — will retry individually", pr_num)
            continue

        if pr_num not in valid_reviews:
            # Should not happen, but safety
            reopen_pr(conn, pr_num)
            continue

        review_text = valid_reviews[pr_num]
        domain_verdict = parse_verdict(review_text, agent)

        # Post domain review comment
        agent_tok = get_agent_token(agent)
        await forgejo_api(
            "POST",
            repo_path(f"issues/{pr_num}/comments"),
            {"body": review_text},
            token=agent_tok,
        )

        conn.execute(
            "UPDATE prs SET domain_verdict = ?, domain_model = ? WHERE number = ?",
            (domain_verdict, config.EVAL_DOMAIN_MODEL, pr_num),
        )

        # If domain rejects, handle disposition (same as individual path)
        if domain_verdict == "request_changes":
            domain_issues = parse_issues(review_text)
            eval_attempts = (conn.execute(
                "SELECT eval_attempts FROM prs WHERE number = ?", (pr_num,)
            ).fetchone()["eval_attempts"] or 0)

            reopen_pr(conn, pr_num, leo_verdict='skipped',
                      last_error='domain review requested changes',
                      eval_issues=json.dumps(domain_issues))
            db.audit(
                conn, "evaluate", "domain_rejected",
                json.dumps({"pr": pr_num, "agent": agent, "issues": domain_issues, "batch": True}),
            )
            await dispose_rejected_pr(conn, pr_num, eval_attempts, domain_issues)
            succeeded += 1
            continue

        # Domain approved — continue to individual Leo review
        logger.info("PR #%d: batch domain approved, proceeding to individual Leo review", pr_num)

        review_diff = pr_data["diff"]
        files = pr_data["files"]

        leo_review, leo_usage = await run_leo_review(review_diff, files, "STANDARD")

        if leo_review is None:
            reopen_pr(conn, pr_num)
            logger.debug("PR #%d: Leo review failed, will retry next cycle", pr_num)
            continue

        if leo_review == "RATE_LIMITED":
            reopen_pr(conn, pr_num)
            logger.info("PR #%d: Leo rate limited, will retry next cycle", pr_num)
            continue

        leo_verdict = parse_verdict(leo_review, "LEO")
        conn.execute("UPDATE prs SET leo_verdict = ? WHERE number = ?", (leo_verdict, pr_num))

        # Post Leo review
        leo_tok = get_agent_token("Leo")
        await forgejo_api(
            "POST",
            repo_path(f"issues/{pr_num}/comments"),
            {"body": leo_review},
            token=leo_tok,
        )

        costs.record_usage(
            conn, config.EVAL_LEO_STANDARD_MODEL, "eval_leo",
            input_tokens=leo_usage.get("prompt_tokens", 0),
            output_tokens=leo_usage.get("completion_tokens", 0),
            backend="openrouter",
        )

        # Final verdict
        both_approve = leo_verdict in ("approve", "skipped") and domain_verdict in ("approve", "skipped")

        if both_approve:
            pr_info = await forgejo_api("GET", repo_path(f"pulls/{pr_num}"))
            pr_author = pr_info.get("user", {}).get("login", "") if pr_info else ""
            await post_formal_approvals(pr_num, pr_author)
            approve_pr(conn, pr_num, domain=domain or 'cross-domain', auto_merge=1)
            db.audit(
                conn, "evaluate", "approved",
                json.dumps({"pr": pr_num, "tier": "STANDARD", "domain": domain,
                            "leo": leo_verdict, "domain_agent": agent, "batch": True}),
            )
            logger.info("PR #%d: APPROVED (batch domain + individual Leo)", pr_num)
        else:
            all_issues = []
            if leo_verdict == "request_changes":
                all_issues.extend(parse_issues(leo_review))
            reopen_pr(conn, pr_num, eval_issues=json.dumps(all_issues))
            feedback = {"leo": leo_verdict, "domain": domain_verdict,
                        "tier": "STANDARD", "issues": all_issues}
            conn.execute(
                "UPDATE sources SET feedback = ? WHERE path = (SELECT source_path FROM prs WHERE number = ?)",
                (json.dumps(feedback), pr_num),
            )
            db.audit(
                conn, "evaluate", "changes_requested",
                json.dumps({"pr": pr_num, "tier": "STANDARD", "leo": leo_verdict,
                            "domain": domain_verdict, "issues": all_issues, "batch": True}),
            )
            eval_attempts = (conn.execute(
                "SELECT eval_attempts FROM prs WHERE number = ?", (pr_num,)
            ).fetchone()["eval_attempts"] or 0)
            await dispose_rejected_pr(conn, pr_num, eval_attempts, all_issues)

        succeeded += 1

    return succeeded, failed


def _build_domain_batches(
    rows: list, conn,
) -> tuple[dict[str, list[dict]], list[dict]]:
    """Group STANDARD PRs by domain for batch eval. DEEP and LIGHT stay individual.

    Returns (batches_by_domain, individual_prs).
    Respects BATCH_EVAL_MAX_PRS and BATCH_EVAL_MAX_DIFF_BYTES.
    """
    domain_candidates: dict[str, list[dict]] = {}
    individual: list[dict] = []

    for row in rows:
        pr_num = row["number"]
        tier = row["tier"]

        # Only batch STANDARD PRs with pending domain review
        if tier != "STANDARD":
            individual.append(row)
            continue

        # Check if domain review already done (resuming after Leo rate limit)
        existing = conn.execute(
            "SELECT domain_verdict, domain FROM prs WHERE number = ?", (pr_num,)
        ).fetchone()
        if existing and existing["domain_verdict"] not in ("pending", None):
            individual.append(row)
            continue

        domain = existing["domain"] if existing and existing["domain"] and existing["domain"] != "general" else "general"
        domain_candidates.setdefault(domain, []).append(row)

    # Build sized batches per domain
    batches: dict[str, list[dict]] = {}
    for domain, prs in domain_candidates.items():
        if len(prs) == 1:
            # Single PR — no batching benefit, process individually
            individual.extend(prs)
            continue
        # Cap at BATCH_EVAL_MAX_PRS
        batch = prs[: config.BATCH_EVAL_MAX_PRS]
        batches[domain] = batch
        # Overflow goes individual
        individual.extend(prs[config.BATCH_EVAL_MAX_PRS :])

    return batches, individual


# ─── Main entry point ──────────────────────────────────────────────────────


async def evaluate_cycle(conn, max_workers=None) -> tuple[int, int]:
    """Run one evaluation cycle.

    Groups eligible STANDARD PRs by domain for batch domain review.
    DEEP PRs get individual eval. LIGHT PRs get auto-approved.
    Leo review always individual (safety net for batch cross-contamination).
    """
    global _rate_limit_backoff_until

    # Check if we're in Opus rate-limit backoff
    opus_backoff = False
    if _rate_limit_backoff_until is not None:
        now = datetime.now(timezone.utc)
        if now < _rate_limit_backoff_until:
            remaining = int((_rate_limit_backoff_until - now).total_seconds())
            logger.debug("Opus rate limit backoff: %d seconds remaining — triage + domain review continue", remaining)
            opus_backoff = True
        else:
            logger.info("Rate limit backoff expired, resuming full eval cycles")
            _rate_limit_backoff_until = None

    # Find PRs ready for evaluation
    if opus_backoff:
        verdict_filter = "AND (p.domain_verdict = 'pending' OR (p.leo_verdict = 'pending' AND p.tier != 'DEEP'))"
    else:
        verdict_filter = "AND (p.leo_verdict = 'pending' OR p.domain_verdict = 'pending')"

    # Stagger removed — migration protection no longer needed. Merge is domain-serialized
    # and entity conflicts auto-resolve. Safe to let all eligible PRs enter eval. (Cory, Mar 14)

    rows = conn.execute(
        f"""SELECT p.number, p.tier, p.branch, p.domain FROM prs p
           LEFT JOIN sources s ON p.source_path = s.path
           WHERE p.status = 'open'
           AND p.tier0_pass = 1
           AND COALESCE(p.eval_attempts, 0) < {config.MAX_EVAL_ATTEMPTS}
           {verdict_filter}
           AND (p.last_attempt IS NULL
                OR p.last_attempt < datetime('now', '-10 minutes'))
           ORDER BY
               CASE WHEN COALESCE(p.eval_attempts, 0) = 0 THEN 0 ELSE 1 END,
               CASE COALESCE(p.priority, s.priority, 'medium')
                   WHEN 'critical' THEN 0
                   WHEN 'high' THEN 1
                   WHEN 'medium' THEN 2
                   WHEN 'low' THEN 3
                   ELSE 4
               END,
               p.created_at ASC
           LIMIT ?""",
        (max_workers or config.MAX_EVAL_WORKERS,),
    ).fetchall()

    if not rows:
        return 0, 0

    succeeded = 0
    failed = 0

    # Group STANDARD PRs by domain for batch eval
    domain_batches, individual_prs = _build_domain_batches(rows, conn)

    # Process batch domain reviews first
    for domain, batch_prs in domain_batches.items():
        try:
            agent = agent_for_domain(domain)
            b_succeeded, b_failed = await _run_batch_domain_eval(
                conn, batch_prs, domain, agent,
            )
            succeeded += b_succeeded
            failed += b_failed
        except Exception:
            logger.exception("Batch eval failed for domain %s", domain)
            # Revert all to open
            for pr_row in batch_prs:
                reopen_pr(conn, pr_row["number"])
            failed += len(batch_prs)

    # Process individual PRs (DEEP, LIGHT, single-domain, fallback)
    for row in individual_prs:
        try:
            if opus_backoff and row["tier"] == "DEEP":
                existing = conn.execute(
                    "SELECT domain_verdict FROM prs WHERE number = ?",
                    (row["number"],),
                ).fetchone()
                if existing and existing["domain_verdict"] not in ("pending", None):
                    logger.debug(
                        "PR #%d: skipping DEEP during Opus backoff (domain already %s)",
                        row["number"],
                        existing["domain_verdict"],
                    )
                    continue

            result = await evaluate_pr(conn, row["number"], tier=row["tier"])
            if result.get("skipped"):
                reason = result.get("reason", "")
                logger.debug("PR #%d skipped: %s", row["number"], reason)
                if "rate_limited" in reason:
                    from datetime import timedelta

                    if reason == "opus_rate_limited":
                        _rate_limit_backoff_until = datetime.now(timezone.utc) + timedelta(
                            minutes=_RATE_LIMIT_BACKOFF_MINUTES
                        )
                        opus_backoff = True
                        logger.info(
                            "Opus rate limited — backing off Opus for %d min, continuing triage+domain",
                            _RATE_LIMIT_BACKOFF_MINUTES,
                        )
                        continue
                    else:
                        _rate_limit_backoff_until = datetime.now(timezone.utc) + timedelta(
                            minutes=_RATE_LIMIT_BACKOFF_MINUTES
                        )
                        logger.info(
                            "Rate limited (%s) — backing off for %d minutes", reason, _RATE_LIMIT_BACKOFF_MINUTES
                        )
                        break
            else:
                succeeded += 1
        except Exception:
            logger.exception("Failed to evaluate PR #%d", row["number"])
            failed += 1
            reopen_pr(conn, row["number"])

    if succeeded or failed:
        logger.info("Evaluate cycle: %d evaluated, %d errors", succeeded, failed)

    return succeeded, failed