from __future__ import annotations

import argparse
import json
import logging
import os
import subprocess
import sys
import tempfile
import time
from collections.abc import Mapping, Sequence
from dataclasses import asdict, dataclass, replace
from pathlib import Path
from time import perf_counter
from typing import TYPE_CHECKING, Any

from . import tool_hunter
from .benchmark import (
    create_benchmark_run,
    evaluate_benchmark,
    load_baseline_findings,
    load_benchmark_manifest,
    load_findings,
    resolve_benchmark_source,
    write_benchmark_artifacts,
)
from .calibration import (
    FalsePositiveLearning,
    calibrate_rankings,
    fp_reachability_overrides,
    learnings_from_verifications,
    load_false_positive_learnings,
    merge_false_positive_learnings,
    write_false_positive_learnings,
    write_ranking_calibrations,
)
from .complexity import map as complexity_map
from .complexity.ledger import persist_verdicts
from .config import ModelLayerConfig, ObligationsConfig, ResolvedConfig, RetiredConfigError, load_config, load_dotenv
from .findings import StaticFinding, quick_scan_findings, write_findings
from .fusion import FusionDecision, fuse_findings
from .gate import FALSE_POSITIVE_CEILING, RECALL_FLOOR
from .harness import HarnessRuntime, HarnessTraceWriter, write_harness_config
from .hunter import run_hunter_pool, write_hunter_trajectories
from .improve import RoundOutcome, run_improvement
from .index import build_code_chunks
from .learn.cli import add_commands as add_learn_commands
from .learn.cli import run as run_learn
from .mapping import analyze_entry_points, attach_reachability_hints, ingest_sarif, write_entry_points, write_static_hints
from .pair_gate import print_pair_metrics
from .pairs import (
    DEFAULT_CATALOG,
    SLICE_NAMES,
    evaluate_catalog,
    load_pair_catalog,
    result_payload,
    select_profile,
    select_slice,
    select_split,
    select_vendored,
)
from .policy import assert_rules_resolve, load_policy
from .preprocess import FileTarget, preprocess_repository, write_preprocess_artifact
from .profiling import start as start_profiling
from .provenance import fingerprint
from .rank import rank_obligations, rank_targets, write_rankings
from .regress import TRIGGERABLE, CandidateVerdict, run_regression, write_verdicts
from .reports import scan_exit_code, write_manifest, write_markdown_report, write_sarif_report
from .repos import (
    DEFAULT_REPO_DIR,
    RepoUnavailable,
    checkout_path,
    load_repo_recipes,
    recipe_payload,
)
from .repos import resolve as resolve_repo
from .repos import select as select_repo
from .repos import verify as verify_repo
from .ruleset import DEFAULT_RULESET_DIR, load_ruleset
from .run import ScanRun, create_scan_run
from .sandbox import resolve_sandbox_probe, resolve_sandbox_runner
from .scoring import build_score_artifact
from .semantic import (
    OverlayRecord,
    adjudicate,
    filter_promoted_hotspots,
    finding_from_coverage,
    load_facts,
    write_overlay,
)
from .semantic.facts import FactLoadError, SemanticFacts
from .stages import Stage, plan_for_mode, record_completed, record_skip, skip_as_degradation, stages_payload
from .verification import VerificationResult, verify_findings, write_verification_results

CALIBRATION_DIR = ".openultrasast/calibration"


@dataclass(frozen=True)
class ScanOutcome:
    scan_id: str
    run_dir: Path
    file_target_count: int
    ranked_target_count: int
    finding_count: int
    calibrations_applied: int
    exit_code: int


if TYPE_CHECKING:
    pass


def _configure_logging() -> None:
    """Send logs to stderr at the level the environment asks for; quiet by default."""
    level = os.environ.get("OUSAST_LOG_LEVEL", "").strip().upper()
    if level not in {"DEBUG", "INFO", "WARNING", "ERROR"}:
        return
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s %(name)s %(message)s", "%H:%M:%S"))
    root = logging.getLogger("openultrasast")
    root.handlers = [handler]
    root.setLevel(getattr(logging, level))
    root.propagate = False


def main(argv: list[str] | None = None) -> int:
    try:
        return _main(argv)
    except RetiredConfigError as exc:  # a retired key is an error that names its replacement, exit 2
        print(f"ousast: {exc}", file=sys.stderr)
        return 2


def _main(argv: list[str] | None) -> int:
    if (argv if argv is not None else sys.argv[1:])[:1] != ["pre-push"]:
        load_dotenv()
    parser = argparse.ArgumentParser(prog="ousast")
    subparsers = parser.add_subparsers(dest="command", required=True)

    replay_parser = subparsers.add_parser("pre-push", help="check Git push input or replay local revisions (experimental)")
    replay_parser.add_argument("path", type=Path)
    replay_parser.add_argument("--base")
    replay_parser.add_argument("--head")
    replay_parser.add_argument("--remote", nargs=2, metavar=("NAME", "URL"))
    replay_parser.add_argument("--comparison-base")
    replay_parser.add_argument("--model-config", type=Path, help="explicit optional witness selection endpoint configuration")
    replay_parser.add_argument("--prior-hook", type=Path, help="explicitly chain an existing hook with the same input and arguments")
    replay_parser.add_argument("--artifact", type=Path, required=True)
    replay_parser.add_argument(
        "--cache-dir", type=Path, help="reuse compatible local artifacts in a private directory outside the repository"
    )
    replay_parser.add_argument("--deadline", type=float, default=30.0)
    replay_parser.add_argument("--cancellation-allowance", type=float, default=2.0)
    replay_parser.add_argument("--max-regions", type=int, default=500)
    replay_parser.add_argument("--mode", choices=("advisory", "blocking"), default="advisory")
    replay_parser.add_argument("--incomplete-coverage", choices=("allow", "block"), default="allow")
    replay_parser.add_argument(
        "--experimental-declarations",
        type=Path,
        help="explicit --base/--head replay only: unreviewed capability declarations used to render the experimental "
        "evaluation explanation; never consulted by admission and cannot enable any capability",
    )
    replay_parser.add_argument(
        "--experimental-record-vetoes",
        action="store_true",
        help="explicit --base/--head replay only: record each comparison and admission veto per raw finding instead of "
        "applying it; output is labeled experimental, changes no result and enables no hook capability",
    )

    scan = subparsers.add_parser("scan", help="scan a local repository")
    scan.add_argument("path", type=Path)
    scan.add_argument("--mode", choices=("quick", "standard", "deep"), default="quick")
    scan.add_argument("--config", type=Path, default=Path("openultrasast.toml"))
    scan.add_argument("--fail-on", choices=("never", "findings", "verified", "worth-fixing"), default="never")

    index = subparsers.add_parser("index", help="chunk a local repository for embedding index construction")
    index.add_argument("path", type=Path)
    index.add_argument("--config", type=Path, default=Path("openultrasast.toml"))
    index.add_argument("--chunk-lines", type=int, default=80)

    benchmark = subparsers.add_parser("benchmark", help="run a benchmark manifest and write scoreboard artifacts")
    benchmark.add_argument("manifest", type=Path)
    benchmark.add_argument("--mode", choices=("quick", "standard", "deep"), default="quick")
    benchmark.add_argument("--config", type=Path, default=Path("openultrasast.toml"))

    improve = subparsers.add_parser(
        "improve",
        help="run the bounded self-improvement loop against a benchmark manifest and update the loop-owned ruleset ledger",
    )
    improve.add_argument("manifest", type=Path)
    improve.add_argument("--max-rounds", type=int, default=5)
    improve.add_argument("--recall-floor", type=float, default=RECALL_FLOOR)
    improve.add_argument("--fp-ceiling", type=float, default=FALSE_POSITIVE_CEILING)
    improve.add_argument(
        "--ledger", type=Path, default=None, help="ruleset ledger path (default: <target>/.openultrasast/calibration/rule_policy.json)"
    )
    improve.add_argument("--journal", type=Path, default=None, help="improvement journal path (default: alongside the ledger)")
    improve.add_argument(
        "--ruleset-dir", type=Path, default=DEFAULT_RULESET_DIR, help="ruleset directory to improve (default: the bundled ruleset)"
    )
    improve.add_argument("--dry-run", action="store_true", help="run rounds against a throwaway ledger; never touch the target's ledger")
    improve.add_argument(
        "--pair-catalog",
        type=Path,
        default=DEFAULT_CATALOG,
        help="pair catalog whose holdout split gates every round per provenance profile",
    )
    improve.add_argument(
        "--no-pair-gate", action="store_true", help="skip the per-profile holdout clause (faster; not for accepted ledgers)"
    )
    improve.add_argument(
        "--profile-tolerance", type=float, default=0.0, help="allowed per-profile drop in pair_correct/Youden before rejecting"
    )
    improve.add_argument("--min-holdout-pairs", type=int, default=5, help="profiles with fewer holdout pairs are reported, not gated")
    improve.add_argument(
        "--memory",
        nargs="?",
        const="",
        default=None,
        metavar="STORE",
        help="also propose rule-status edits from the plane memory store (a directory, file:// or minio:// URL; "
        "no value: OUSAST_MEMORY or the default store); off by default",
    )
    improve.add_argument(
        "--qualify-population",
        action="append",
        default=[],
        metavar="NAME",
        help="population a memory proposal will be qualified on; its rows never propose (repeatable)",
    )

    pairs = subparsers.add_parser(
        "pairs",
        help="scan isolated vuln-vs-fixed pairs (TP on vuln, silent on fix) and emit improve-loop signals",
    )
    pairs.add_argument("--catalog", type=Path, default=DEFAULT_CATALOG)
    pairs.add_argument("--slice", choices=SLICE_NAMES, default="all")
    pairs.add_argument(
        "--profile", choices=("all", "human", "agent", "mixed", "synthetic"), default="all", help="filter pairs by provenance profile"
    )
    pairs.add_argument("--split", choices=("all", "train", "holdout"), default="all", help="filter pairs by declared split")
    pairs.add_argument("--pointers", action="store_true", help="allow network for non-vendored pointer pairs this run (nightly; CI never)")
    pairs.add_argument("--json", action="store_true", help="print the pair scoreboard as JSON")

    repos = subparsers.add_parser(
        "repos",
        help="pinned known-vulnerable checkouts: list them, and verify a recipe against the code it names",
    )
    repos.add_argument("--dir", type=Path, default=DEFAULT_REPO_DIR)
    repos.add_argument("--repo", default="all", help="one recipe by name, or all")
    repos.add_argument("--fetch", action="store_true", help="allow network to materialise missing checkouts (CI never)")
    repos.add_argument("--json", action="store_true")

    subparsers.add_parser("mcp", help="run the narrow MCP server over stdio for OpenCode integration")
    model_cmd = subparsers.add_parser("model", help="maintainer: the model layer that arbitrates detector claims")
    model_sub = model_cmd.add_subparsers(dest="model_command", required=True)
    candidates = model_sub.add_parser("candidates", help="offline: what the candidate enumerator can reach, per family and per slice")
    candidates.add_argument("--out", type=Path, default=Path(".openultrasast/model"), help="where artifacts are written")
    candidates.add_argument("--catalog", type=Path, default=DEFAULT_CATALOG)
    candidates.add_argument("--slice", choices=SLICE_NAMES, default="all")
    candidates.add_argument("--generator", choices=("ir", "ruleset"), default="ir", help="which pass enumerates the candidate sites")
    candidates.add_argument("--json", action="store_true")

    learn = subparsers.add_parser("learn", help="maintainer: the learned decision engine's data (labels)")
    learn_sub = learn.add_subparsers(dest="learn_command", required=True)
    learn_labels = learn_sub.add_parser("labels", help="labels from the fail-closed sources in learn/sources.toml (host only)")
    learn_labels.add_argument("--sources", type=Path, help="the source list (default: the packaged learn/sources.toml)")
    learn_labels.add_argument("--root", type=Path, default=Path("."), help="the repository root the source paths are relative to")
    learn_labels.add_argument("--cache", type=Path, help="clones and checkouts (default ~/.cache/openultrasast)")
    learn_labels.add_argument("--out", type=Path, help="write the full rows to OUT/labels-<sha>.jsonl (local: they name functions)")
    learn_labels.add_argument("--snapshot", type=Path, help="write the counts-only snapshot (the committable record) here")
    learn_labels.add_argument("--include-title", action="store_true", help="the title-tier pairs as well (a sensitivity arm)")
    learn_labels.add_argument("--no-assumed-benign", action="store_true", help="skip mining the assumed-benign history source")
    add_learn_commands(learn_sub)
    plane = subparsers.add_parser("plane", help="maintainer: the ax-backed service plane on this host's kind cluster")
    plane_sub = plane.add_subparsers(dest="plane_command", required=True)
    plane_run = plane_sub.add_parser("run", help="execute a Run manifest on ax; a rerun skips done tasks")
    plane_run.add_argument("run_manifest", type=Path)
    plane_run.add_argument("--workers", type=int, default=1, help="tasks in flight at once (serialize labels never overlap)")
    plane_run.add_argument("--ax", default="ax", help="path of the ax CLI")
    plane_status = plane_sub.add_parser("status", help="per-task status and the token attribution table of a run")
    plane_status.add_argument("run")
    plane_status.add_argument("--units", action="store_true", help="per-unit rows from each task's units.jsonl")
    plane_sub.add_parser("doctor", help="check kind, Agent Substrate, the ax controller and the runner image")
    plane_remember = plane_sub.add_parser("remember", help="ingest a run's memory rows into the store (OUSAST_MEMORY)")
    plane_remember.add_argument("run")
    plane_remember.add_argument("--plane", type=Path, default=Path("plane"), help="where runs/<run>.yaml names population and split")
    plane_remember.add_argument("--population", help="the population the run measured (default: the Run's annotation)")
    plane_remember.add_argument("--split", help="the split of that population (default: the Run's annotation)")
    plane_engine = plane_sub.add_parser(
        "alerts-engine", help="a Run's `alerts` for PHP and languages quick mode does not cover, from the engine image on this host"
    )
    plane_engine.add_argument("run_manifest", type=Path)
    plane_engine.add_argument(
        "--source", type=Path, required=True, help="a frozen export: git archive HEAD src benchmarks/push/finding_dump.py"
    )
    plane_engine.add_argument("--cases", default="", help="comma-separated case ids (default: every alerts case of the Run)")
    plane_engine.add_argument("--deadline", type=float, default=1800.0, help="the engine's scan deadline per pin, seconds")
    plane_engine.add_argument("--image", default="openultrasast:dev")
    plane_engine.add_argument("--repos", type=Path, help="the case cache (default ~/.cache/openultrasast/independent)")
    plane_engine.add_argument("--work", type=Path, help="where pins are exported and results land (default: a temporary directory)")
    plane_harvest = plane_sub.add_parser(
        "harvest", help="the decision engine's harvest Runs (verify a/b + agree, model roles) over a label snapshot"
    )
    plane_harvest.add_argument("--labels", type=Path, required=True, help="the full label rows (`ousast learn labels --out`)")
    plane_harvest.add_argument(
        "--plane", type=Path, required=True, help="where to write runs/, tasks/, workspaces/, models/, units.json (scratch)"
    )
    plane_harvest.add_argument("--name", default="harvest", help="Run name prefix: <name>-verify and <name>-roles")
    plane_harvest.add_argument("--templates", type=Path, default=Path("plane"), help="the plane root holding tasks/ and models/ templates")
    plane_harvest.add_argument("--catalog", type=Path, default=Path("benchmarks/pairs/catalog.toml"))
    plane_harvest.add_argument(
        "--cache", type=Path, default=Path.home() / ".cache" / "openultrasast", help="case clones and recipe checkouts"
    )
    plane_harvest.add_argument("--ceiling", type=float, default=10.0, help="USD: each Run's task budgets sum to at most this")
    plane_harvest.add_argument("--runner-image", type=Path, help="re-pin the task templates to the image in this file first (ops/ax/up.sh)")
    plane_ws = plane_sub.add_parser("workspaces", help="one Workspace manifest per case pin of a population file")
    plane_ws.add_argument("population", type=Path)
    plane_ws.add_argument("--out", type=Path, default=Path("plane/workspaces"))
    plane_ws.add_argument("--validation-set", type=Path, help="JSON {case: [[path, function], ...]}: emit that set's increment instead")
    plane_ws.add_argument("--candidates", type=Path, help="with --validation-set: per-case candidate records (independent-v2-modelsinks)")
    plane_ws.add_argument("--triage", type=Path, help="with --validation-set: recorded batched-check scans (<case>--vulnerable_a.jsonl)")
    plane_ws.add_argument("--run", default="validation-46", help="with --validation-set: the Run name")
    plane_ws.add_argument("--plane", type=Path, default=Path("plane"), help="with --validation-set: the manifest root")
    plane_ws.add_argument(
        "--repos", type=Path, default=Path.home() / ".cache" / "openultrasast" / "independent",
        help="with --validation-set: one clone per case id, for the fix ranges agree scores by (evaluate.hunks)",
    )  # fmt: skip
    plane_ws.add_argument(
        "--runner-image", type=Path, help="with --validation-set: re-pin the task templates to the image in this file (ops/ax/up.sh)"
    )
    plane_ws.add_argument(
        "--loop", action="store_true",
        help="with --validation-set: append the improvement loop (alerts per case, then snapshot, measure, propose, improve)",
    )  # fmt: skip
    plane_ws.add_argument("--loop-commit", help="with --loop: the commit under test the gate runs at (default: git rev-parse HEAD)")
    plane_ws.add_argument("--loop-repo", help="with --loop: this repository's URL (default: git remote get-url origin)")
    plane_ws.add_argument("--loop-manifest", help="with --loop: the benchmark manifest the proposals are gated on")
    plane_ws.add_argument(
        "--qualify-population", action="append", default=[], metavar="NAME", help="with --loop: a population whose rows never propose"
    )

    args = parser.parse_args(argv)
    # Diagnosis needs the stage costs, and nothing configures logging, so the default root level of
    # WARNING silently dropped every informational line. A run that reports nothing is
    # indistinguishable from a run with nothing to report, which is how four PHP investigations were
    # spent reading a later stage's timeout as the cause.
    _configure_logging()
    start_profiling()  # only when OUSAST_SAMPLE_PROFILE names a file
    if args.command == "pre-push":
        from .config import PushConfig
        from .push.runner import push, replay

        try:
            if bool(args.base) != bool(args.head) or bool(args.base) == bool(args.remote):
                parser.error("provide --base and --head together, or --remote NAME URL for Git stdin")
            settings = PushConfig(
                comparison_base=args.comparison_base,
                deadline_seconds=args.deadline,
                cancellation_allowance_seconds=args.cancellation_allowance,
                mode=args.mode,
                incomplete_coverage_policy=args.incomplete_coverage,
            )
            options: dict[str, Any] = dict(
                artifact=args.artifact,
                config=settings,
                max_regions=args.max_regions,
                cache_dir=args.cache_dir,
                model_config=args.model_config,
            )
            if args.base and args.prior_hook:
                parser.error("--prior-hook requires Git stdin mode")
            if args.experimental_record_vetoes and not args.base:
                parser.error("--experimental-record-vetoes requires explicit --base/--head replay; it is never a hook capability")
            if args.experimental_declarations and not args.experimental_record_vetoes:
                parser.error("--experimental-declarations requires --experimental-record-vetoes")
            prior_data = None
            if args.base:
                delivery = replay(
                    args.path,
                    base=args.base,
                    head=args.head,
                    record_vetoes=args.experimental_record_vetoes,
                    declarations=args.experimental_declarations,
                    **options,
                )
            else:
                # multiprocessing closes sys.stdin in its child; duplicate the Git pipe first.
                with os.fdopen(os.dup(0), "rb") as stream:
                    if args.prior_hook:
                        from .model.contracts import ExecutionBudget
                        from .push.runner import _prepare

                        shared = ExecutionBudget(time.monotonic() + settings.deadline_seconds, settings.cancellation_allowance_seconds)
                        try:
                            prior_data = _prepare(lambda: stream.read(1024 * 1024 + 1), shared)
                            if len(prior_data) > 1024 * 1024:
                                raise ValueError("input exceeds hook integration limit")
                        except (ValueError, RuntimeError, TimeoutError):
                            print("Hook input could not be retained for all consumers. Retry with complete input and sufficient time.")
                            return 1
                        options["execution_budget"] = shared

                    def updates() -> str:
                        return (prior_data if prior_data is not None else stream.read(1024 * 1024 + 1)).decode("utf-8")

                    delivery = push(args.path, updates=updates, remote_name=args.remote[0], remote_url=args.remote[1], **options)
        except ValueError as error:
            parser.error(str(error))
        print(delivery.text, end="", flush=True)
        if args.prior_hook and prior_data is not None:
            # This is the explicitly chained hook's own behavior, outside analysis.
            # Never replace its rejection with our advisory success or impose our timeout on it.
            try:
                prior = subprocess.run([str(args.prior_hook.resolve()), *args.remote], input=prior_data, cwd=args.path)
            except OSError:
                print("Existing hook could not be started. Restore its executable path before retrying.")
                return 1
            if prior.returncode:
                return prior.returncode if prior.returncode > 0 else 128 - prior.returncode
        return delivery.exit_code
    if args.command == "scan":
        return _scan(args.path, args.config, args.mode, args.fail_on)
    if args.command == "index":
        return _index(args.path, args.config, args.chunk_lines)
    if args.command == "benchmark":
        return _benchmark(args.manifest, args.config, args.mode)
    if args.command == "improve":
        return _improve(
            args.manifest,
            max_rounds=args.max_rounds,
            recall_floor=args.recall_floor,
            fp_ceiling=args.fp_ceiling,
            ledger=args.ledger,
            journal=args.journal,
            ruleset_dir=args.ruleset_dir,
            dry_run=args.dry_run,
            pair_catalog=None if args.no_pair_gate else args.pair_catalog,
            profile_tolerance=args.profile_tolerance,
            min_holdout_pairs=args.min_holdout_pairs,
            memory=args.memory,
            qualify_populations=tuple(args.qualify_population),
            guard_catalog=args.pair_catalog,
        )
    if args.command == "pairs":
        return _pairs(
            args.catalog,
            args.slice,
            json_out=args.json,
            profile=args.profile,
            split=args.split,
            pointers=args.pointers,
        )
    if args.command == "repos":
        return _repos(args.dir, args.repo, fetch=args.fetch, json_out=args.json)
    if args.command == "model":
        return _model_candidates(args)
    if args.command == "plane":
        return _plane(args)
    if args.command == "learn":
        return _learn_labels(args) if args.learn_command == "labels" else run_learn(args)
    if args.command == "mcp":
        from .mcp import serve  # lazy: keeps the import cycle (mcp -> cli) one-directional

        return serve()
    return 2


def _scan(path: Path, config_path: Path, mode: str, fail_on: str) -> int:
    outcome = _run_scan(path, config_path, mode, fail_on)
    _print_scan_outcome(outcome)
    return outcome.exit_code


def _run_scan(path: Path, config_path: Path, mode: str, fail_on: str) -> ScanOutcome:
    if not path.exists() or not path.is_dir():
        raise SystemExit(f"scan path is not a directory: {path}")

    plan = plan_for_mode(mode)
    config = load_config(config_path if config_path.exists() else None)
    hunter_model = config.models.hunter
    run = create_scan_run(path, config)
    runtime = HarnessRuntime(
        scan_id=run.scan_id,
        config=config,
        trace_writer=HarnessTraceWriter(run.root / "trace" / "events.jsonl", redact=config.hardening.redact_secrets),
    )
    write_harness_config(config=config, processors=[], contract_mode="strict", path=run.root / "harness.json")
    runtime.start(mode=mode, target=run.target)
    policy = runtime.run_stage("policy_load", load_policy)
    ledger_path = run.target / CALIBRATION_DIR / "false_positive_learnings.json"
    rule_ledger_path = run.target / CALIBRATION_DIR / "rule_policy.json"
    ruleset = runtime.run_stage("ruleset_load", lambda: load_ruleset(DEFAULT_RULESET_DIR, rule_ledger_path))
    runtime.run_stage("policy_check", lambda: assert_rules_resolve(ruleset, policy))
    static_hints = runtime.run_stage("static_mapping", lambda: _load_static_hints(config.static_analysis.sarif_paths))
    write_static_hints(static_hints, run.root / "mapping" / "static_hints.json")
    snapshot, targets = runtime.run_stage(
        "preprocess",
        lambda: preprocess_repository(run.target, run.root / "preprocess" / "file_targets.json", static_hints),
    )
    entry_points = runtime.run_stage("entry_point_mapping", lambda: analyze_entry_points(run.target, targets))
    write_entry_points(entry_points, run.root / "mapping" / "entry_points.json")
    targets = attach_reachability_hints(targets, entry_points)
    write_preprocess_artifact(snapshot, targets, run.root / "preprocess" / "file_targets.json")
    provenance = runtime.run_stage("provenance", lambda: fingerprint(run.target))
    rankings = runtime.run_stage("rank", lambda: rank_targets(targets))
    prior_learnings = load_false_positive_learnings(ledger_path)
    rankings, calibrations = runtime.run_stage("calibrate", lambda: calibrate_rankings(rankings, prior_learnings))
    applied_calibrations = [calibration for calibration in calibrations if calibration.applied_learning_ids]
    write_rankings(rankings, run.root / "rank" / "ranking.json")
    write_ranking_calibrations(calibrations, run.root / "calibration" / "applied_calibrations.json")
    if mode == "standard":
        hunter_result = runtime.run_stage(
            "hunter_pool",
            lambda: run_hunter_pool(run.target, targets, rankings, scan_id=run.scan_id, ruleset=ruleset, policy=policy),
        )
        findings = hunter_result.findings
        trajectories = hunter_result.trajectories
    else:
        findings = runtime.run_stage(
            "quick_findings",
            lambda: quick_scan_findings(
                run.target,
                targets,
                rankings,
                ruleset,
                policy,
                min_emit_priority=config.ruleset.min_emit_priority,
                min_emit_precision=config.ruleset.min_emit_precision,
            ),
        )
        trajectories = []
    # Shadow-status rules fire but are excluded from the report and the score;
    # their outcomes are preserved separately for precision tracking.
    shadow_findings = [finding for finding in findings if finding.status == "shadow"]
    if shadow_findings:
        findings = [finding for finding in findings if finding.status != "shadow"]
        write_findings(shadow_findings, run.root / "shadow_findings.json")
    # Bounded-CI budget: cap the reported finding count (0 = unlimited). Findings are
    # severity/priority-sorted, so truncation keeps the most severe and is disclosed.
    max_findings = config.hardening.max_findings
    if max_findings and len(findings) > max_findings:
        runtime.state["degradations"].append(
            {"stage": "budget", "reason": "max_findings_exceeded", "requested": max_findings, "actual": len(findings)}
        )
        findings = findings[:max_findings]
    findings_path = run.root / "findings.json"
    verification_path = run.root / "verification.json"
    markdown_path = run.root / "report.md"
    sarif_path = run.root / "report.sarif"
    manifest_path = run.root / "manifest.json"
    trajectories_path = run.root / "traces" / "hunter_trajectories.jsonl"
    write_findings(findings, findings_path)
    if trajectories:
        write_hunter_trajectories(trajectories, trajectories_path)
    verifications = runtime.run_stage("verify", lambda: verify_findings(findings))
    write_verification_results(verifications, verification_path)
    runtime.run_stage(
        "record_calibration",
        lambda: _persist_calibration_feedback(run, ledger_path, prior_learnings, findings, verifications),
    )
    # Fusion: deterministic two-panel adjudication for triggered findings (standard mode).
    fusion_decisions: list[FusionDecision] = []
    fusion_path = run.root / "fusion.json"
    if mode == "standard" and config.fusion.enabled:
        fusion_decisions = runtime.run_stage(
            "fusion",
            lambda: fuse_findings(findings, verifications, high_assurance=config.fusion.high_assurance),
        )
        if fusion_decisions:
            fusion_path.write_text(json.dumps([decision.to_dict() for decision in fusion_decisions], indent=2, sort_keys=True) + "\n")
    cwe_by_rule = {rule.rule_id: rule.cwe for rule in ruleset}
    rule_cwe_by_id = {finding.finding_id: cwe_by_rule.get(finding.finding_id.split(":", 1)[0], "") for finding in findings}
    # A confirmed false positive (prior-scan learning) lowers a finding's effective
    # reachability multiplier in the score instead of deleting the rule.
    reachability_override = fp_reachability_overrides(findings, prior_learnings)
    score_artifact = runtime.run_stage(
        "score",
        lambda: build_score_artifact(
            findings,
            rule_cwe_by_id,
            policy,
            k=config.score.k,
            min_score=config.score.min_score,
            block_severity_reachable=config.score.block_severity_reachable,
            blocking=config.score.blocking,
            reachability_override=reachability_override,
        ),
    )
    score_path = run.root / "score.json"
    score_path.write_text(json.dumps(score_artifact.to_dict(), indent=2, sort_keys=True) + "\n")
    plan = record_completed(plan, Stage.STATIC)
    complexity_payload: dict[str, object] | None = None
    complexity_map_path = run.root / "complexity_map.json"
    overlay_path = run.root / "overlay.json"
    built_map = None
    overlay_records: list[OverlayRecord] = []
    wrote_overlay = False
    obligations_payload: dict[str, object] | None = None
    model_payload: dict[str, object] | None = None
    obligations_cited: dict[str, dict[str, object]] = {}
    if Stage.MAP in plan.requested:
        built_map = runtime.run_stage(
            "map",
            lambda: complexity_map.build_complexity_map(
                targets,
                findings,
                complexity_map_path,
                repo_files=[target.path for target in targets],
                ledger_path=run.target / CALIBRATION_DIR / "complexity_ledger.json",
            ),
        )
        facts_or_error: SemanticFacts | FactLoadError
        try:
            facts_or_error = load_facts()
        except FactLoadError as exc:
            facts_or_error = exc
            runtime.state["degradations"].append({"stage": "overlay", "reason": "facts_unavailable", "detail": str(exc)})
        overlay_records = list(
            runtime.run_stage(
                "overlay",
                lambda: adjudicate(root=run.target, targets=targets, findings=findings, facts=facts_or_error),
            )
        )
        write_overlay(overlay_records, overlay_path)
        wrote_overlay = True
        coverage_findings = [finding_from_coverage(record) for record in overlay_records if record.disposition == "coverage"]
        if coverage_findings:
            seen = {finding.finding_id for finding in findings}
            extra = [item for item in coverage_findings if item.finding_id not in seen]
            if extra:
                findings = findings + extra
                write_findings(findings, findings_path)
        if config.obligations.enabled:
            obligation_findings, obligations_payload, obligations_cited = _check_obligations(
                root=run.target,
                targets=targets,
                entries=entry_points,
                facts=facts_or_error,
                settings=config.obligations,
                runtime=runtime,
                hunter_model=hunter_model,
            )
            if obligation_findings:
                findings = rank_obligations(findings + obligation_findings)
                write_findings(findings, findings_path)
        # Fifth MAP proposer: the model layer (contributor-scan Req 1). Additive -- it adds findings beside
        # the pattern, overlay and obligation ones and removes none of them.
        model_findings, model_payload = _run_model_layer(
            root=run.target,
            targets=targets,
            entries=entry_points,
            settings=config.model,
            runtime=runtime,
            config=config,
        )
        if model_findings:
            findings = findings + model_findings
            write_findings(findings, findings_path)
        plan = record_completed(plan, Stage.MAP)
        if not hunter_model:
            runtime.state["degradations"].append(skip_as_degradation(Stage.MAP, "hunter_model_unavailable"))
        else:
            built_map, findings, verifications = _attach_tool_hunter(
                root=run.target,
                built_map=built_map,
                findings=findings,
                verifications=verifications,
                hunter_model=hunter_model,
                max_hotspots=config.complexity.max_hunter_hotspots,
                max_findings=max_findings,
                findings_path=findings_path,
                verification_path=verification_path,
                complexity_map_path=complexity_map_path,
                runtime=runtime,
            )
        complexity_payload = {
            "hotspot_count": len(built_map.hotspots),
            "heuristic_only": built_map.heuristic_only,
        }
    verdicts_path = run.root / "verdicts.json"
    wrote_verdicts = False
    verdict_records: tuple[CandidateVerdict, ...] = ()
    if Stage.REGRESS in plan.requested:
        probe = resolve_sandbox_probe()
        if not probe.available():
            plan = record_skip(plan, Stage.REGRESS, "sandbox_unavailable")
            runtime.state["degradations"].append(skip_as_degradation(Stage.REGRESS, "sandbox_unavailable"))
        else:
            sandbox = resolve_sandbox_runner()
            hotspots = built_map.hotspots if built_map is not None else ()
            hotspots = filter_promoted_hotspots(hotspots, overlay_records, findings)
            promoted = [
                finding
                for finding in findings
                if finding.finding_id in {record.proposal_id for record in overlay_records if record.disposition == "promote"}
            ]
            cwe_by_rule_id = {rule.rule_id: rule.cwe for rule in ruleset}
            rule_cwe_by_finding = {finding.finding_id: cwe_by_rule_id.get(finding.finding_id.split(":", 1)[0], "") for finding in promoted}
            languages_by_path = {target.path: target.language for target in targets}
            records = runtime.run_stage(
                "regress",
                lambda: run_regression(
                    hotspots,
                    promoted,
                    max_candidates=config.regress.max_candidates,
                    policy=policy,
                    rule_cwe=rule_cwe_by_finding,
                    languages_by_path=languages_by_path,
                    repo_root=run.target,
                    sandbox=sandbox,
                    sandbox_limits=config.sandbox,
                    images=dict(config.regress.images),
                ),
            )
            write_verdicts(records, verdicts_path)
            persist_verdicts(run.target / CALIBRATION_DIR / "complexity_ledger.json", records)
            # Req 6.4: proof rungs live on verdicts, not on StaticFinding, so obligations re-rank once the sandbox has spoken.
            proven_ids = {fid for record in records if record.verdict == TRIGGERABLE for fid in record.inventory_finding_ids}
            if proven_ids and any(finding.finding_id.startswith("obligation:") for finding in findings):
                findings = rank_obligations(findings, proven_ids=proven_ids)
                write_findings(findings, findings_path)
            verdict_records = records
            wrote_verdicts = True
            plan = record_completed(plan, Stage.REGRESS)
    worth_fixing_payload = _worth_fixing_payload(verdict_records) if wrote_verdicts else None
    # Req 5.6: what each family could see, for the report. Absent when the model layer did not run --
    # and a scan with no model layer states no limits because it made no claims.
    coverage_rows = model_payload.get("coverage") if isinstance(model_payload, dict) else None
    coverage_for_report = coverage_rows if isinstance(coverage_rows, list) and coverage_rows else None
    runtime.run_stage(
        "report",
        lambda: write_markdown_report(
            findings,
            markdown_path,
            verifications,
            redact=config.hardening.redact_secrets,
            complexity_map=built_map,
            verdicts=verdict_records if wrote_verdicts else None,
            overlay=overlay_records if wrote_overlay else None,
            obligations=obligations_cited or None,
            obligations_summary=obligations_payload or None,
            coverage=coverage_for_report,
            # Degradations are coverage facts: an empty graph, a graph built in parts, a query that never
            # ran. Kept out of the report they leave the reader unable to tell "nothing was found" from
            # "nothing was looked at".
            degradations=list(runtime.state.get("degradations") or ()),
        ),
    )
    runtime.run_stage(
        "sarif",
        lambda: write_sarif_report(
            findings,
            verifications,
            sarif_path,
            overlay=overlay_records if wrote_overlay else None,
            obligations=obligations_cited or None,
        ),
    )
    runtime.run_stage(
        "manifest",
        lambda: write_manifest(
            run=run,
            findings=findings,
            verifications=verifications,
            artifact_paths=_artifact_paths(
                findings=findings_path,
                verification=verification_path,
                markdown=markdown_path,
                sarif=sarif_path,
                score=score_path,
                trajectories=trajectories_path if trajectories else None,
                fusion=fusion_path if fusion_decisions else None,
                complexity_map=complexity_map_path if complexity_payload is not None else None,
                overlay=overlay_path if wrote_overlay else None,
                verdicts=verdicts_path if wrote_verdicts else None,
            ),
            path=manifest_path,
            score=score_artifact.to_dict(),
            degradations=runtime.state["degradations"] or None,
            fusion=[_fusion_summary(decision) for decision in fusion_decisions] or None,
            stages=stages_payload(plan),
            complexity=complexity_payload,
            worth_fixing=worth_fixing_payload,
            provenance=provenance.to_dict(),
            obligations=obligations_payload,
            model=model_payload,
        ),
    )
    runtime.finish(status="succeeded")
    return ScanOutcome(
        scan_id=run.scan_id,
        run_dir=run.root,
        file_target_count=len(targets),
        ranked_target_count=len(rankings),
        finding_count=len(findings),
        calibrations_applied=len(applied_calibrations),
        exit_code=scan_exit_code(findings, verifications, fail_on, worth_fixing_verdicts=verdict_records),
    )


def _attach_tool_hunter(
    *,
    root: Path,
    built_map: complexity_map.ComplexityMap,
    findings: list[StaticFinding],
    verifications: list[VerificationResult],
    hunter_model: str,
    max_hotspots: int,
    max_findings: int,
    findings_path: Path,
    verification_path: Path,
    complexity_map_path: Path,
    runtime: HarnessRuntime,
) -> tuple[complexity_map.ComplexityMap, list[StaticFinding], list[VerificationResult]]:
    client = tool_hunter.resolve_hunter_client()
    if client is None:
        return built_map, findings, verifications
    hunter_hotspots = list(built_map.hotspots[: max(max_hotspots, 0)])
    hunter_findings = runtime.run_stage(
        "tool_hunter",
        lambda: tool_hunter.run_tool_hunter(
            root,
            hunter_hotspots,
            client=client,
            model=hunter_model,
            max_steps=tool_hunter.DEFAULT_MAX_STEPS,
        ),
    )
    seen = {finding.finding_id for finding in findings}
    extra = [finding for finding in hunter_findings if finding.finding_id not in seen]
    if extra:
        findings = findings + extra
        if max_findings and len(findings) > max_findings:
            if not any(entry.get("reason") == "max_findings_exceeded" for entry in runtime.state["degradations"]):
                runtime.state["degradations"].append(
                    {"stage": "budget", "reason": "max_findings_exceeded", "requested": max_findings, "actual": len(findings)}
                )
            findings = findings[:max_findings]
            kept = {finding.finding_id for finding in findings}
            extra = [finding for finding in extra if finding.finding_id in kept]
        if extra:
            verifications = verifications + verify_findings(extra)
            write_verification_results(verifications, verification_path)
    write_findings(findings, findings_path)
    built_map = replace(built_map, heuristic_only=False)
    complexity_map.write_complexity_map(built_map, complexity_map_path)
    return built_map, findings, verifications


def _persist_calibration_feedback(
    run: ScanRun,
    ledger_path: Path,
    prior_learnings: list[FalsePositiveLearning],
    findings: list[StaticFinding],
    verifications: list[VerificationResult],
) -> list[FalsePositiveLearning]:
    # Store rejected or held findings as scoped learnings for the next scan.
    new_learnings = learnings_from_verifications(findings, verifications)
    merged = merge_false_positive_learnings(prior_learnings, new_learnings)
    write_false_positive_learnings(merged, ledger_path)
    write_false_positive_learnings(new_learnings, run.root / "calibration" / "false_positive_learnings.json")
    return merged


def _print_scan_outcome(outcome: ScanOutcome) -> None:
    print(f"scan_id={outcome.scan_id}")
    print(f"run_dir={outcome.run_dir}")
    print(f"file_targets={outcome.file_target_count}")
    print(f"ranked_targets={outcome.ranked_target_count}")
    print(f"findings={outcome.finding_count}")
    print(f"calibrations_applied={outcome.calibrations_applied}")


def _load_static_hints(sarif_paths: tuple[str, ...]) -> list[object]:
    hints: list[object] = []
    for sarif_path in sarif_paths:
        path = Path(sarif_path)
        if path.exists():
            hints.extend(ingest_sarif(path))
    return hints


def _worth_fixing_payload(verdicts: tuple[CandidateVerdict, ...]) -> dict[str, object]:
    worth = [record for record in verdicts if record.worth_fixing]
    return {
        "count": len(worth),
        "verdicts": [
            {
                "path": record.path,
                "function_name": record.function_name,
                "verdict": record.verdict,
                "reason": record.reason,
                "worth_fixing": True,
            }
            for record in worth
        ],
    }


def _artifact_paths(**paths: Path | None) -> dict[str, Path]:
    return {name: path for name, path in paths.items() if path is not None}


def _fusion_summary(decision: FusionDecision) -> dict[str, object]:
    return {
        "finding_id": decision.finding_id,
        "disposition": str(decision.disposition),
        "decision_source": decision.decision_source,
        "triggers": decision.triggers,
    }


def _benchmark(manifest_path: Path, config_path: Path, mode: str) -> int:
    if not manifest_path.exists() or not manifest_path.is_file():
        raise SystemExit(f"benchmark manifest is not a file: {manifest_path}")
    manifest = load_benchmark_manifest(manifest_path)
    target = resolve_benchmark_source(manifest_path, manifest)
    run = create_benchmark_run(target, manifest)
    started_at = perf_counter()
    outcome = _run_scan(target, config_path, mode, "never")
    runtime_seconds = perf_counter() - started_at
    findings = _load_scan_findings(outcome.run_dir)
    baseline_findings = load_baseline_findings(manifest_path, manifest)
    result = evaluate_benchmark(
        run=run,
        mode=mode,
        findings=findings,
        scan_id=outcome.scan_id,
        scan_run_dir=outcome.run_dir,
        runtime_seconds=runtime_seconds,
        baseline_findings=baseline_findings,
    )
    write_benchmark_artifacts(run, manifest_path, result)
    _print_scan_outcome(outcome)
    print(f"benchmark_run_id={run.benchmark_run_id}")
    print(f"benchmark_run_dir={run.root}")
    print(f"benchmark_expected={result.metrics.expected_findings_total}")
    print(f"benchmark_matched={result.metrics.matched_findings_total}")
    print(f"benchmark_missed={result.metrics.missed_findings_total}")
    return 0


def _improve(
    manifest_path: Path,
    *,
    max_rounds: int,
    recall_floor: float,
    fp_ceiling: float,
    ledger: Path | None,
    journal: Path | None,
    ruleset_dir: Path,
    dry_run: bool,
    pair_catalog: Path | None = None,
    profile_tolerance: float = 0.0,
    min_holdout_pairs: int = 5,
    memory: str | None = None,
    qualify_populations: tuple[str, ...] = (),
    guard_catalog: Path | None = None,
) -> int:
    if not manifest_path.exists() or not manifest_path.is_file():
        raise SystemExit(f"benchmark manifest is not a file: {manifest_path}")
    pair_cases: tuple[object, ...] = ()
    if pair_catalog is not None:
        if not pair_catalog.is_file():
            raise SystemExit(f"pair catalog is not a file: {pair_catalog} (pass --no-pair-gate to skip the per-profile holdout clause)")
        pair_cases = select_split(load_pair_catalog(pair_catalog), "holdout")
    manifest = load_benchmark_manifest(manifest_path)
    target = resolve_benchmark_source(manifest_path, manifest)
    policy = load_policy()
    session = _memory_session(memory, manifest, target, ruleset_dir, qualify_populations, guard_catalog) if memory is not None else None

    # The loop writes its accepted ledger where `scan`/`benchmark` read it, so a
    # subsequent scan of this target automatically picks up the improved ruleset.
    default_ledger = target / CALIBRATION_DIR / "rule_policy.json"
    with tempfile.TemporaryDirectory(prefix="ousast-improve-") as scratch:
        if dry_run:
            ledger_path = Path(scratch) / "rule_policy.json"
            journal_path = Path(scratch) / "improve_journal.json"
        else:
            ledger_path = ledger or default_ledger
            journal_path = journal or ledger_path.with_name("improve_journal.json")

        outcomes = run_improvement(
            target,
            manifest,
            ledger_path=ledger_path,
            journal_path=journal_path,
            ruleset_dir=ruleset_dir,
            policy=policy,
            max_rounds=max_rounds,
            recall_floor=recall_floor,
            fp_ceiling=fp_ceiling,
            pair_cases=pair_cases,
            profile_tolerance=profile_tolerance,
            min_holdout_pairs=min_holdout_pairs,
            proposals=session.proposals(ledger_path, journal_path) if session else (),
        )
        _print_improve_outcomes(outcomes, manifest_path, target, ledger_path, dry_run=dry_run)
        if session is not None:
            session.finish(outcomes, journal_path, dry_run=dry_run)
    return 0


def _memory_session(
    memory: str, manifest: object, target: Path, ruleset_dir: Path, populations: tuple[str, ...], catalog: Path | None
) -> Any:
    from .improve.memory import MemorySession  # lazy: the default `improve` path never touches the store

    return MemorySession.open(memory, manifest, target, ruleset_dir, populations, catalog)


def _pairs(
    catalog: Path,
    slice_name: str,
    *,
    json_out: bool,
    profile: str = "all",
    split: str = "all",
    pointers: bool = False,
) -> int:
    if not catalog.exists() or not catalog.is_file():
        raise SystemExit(f"pair catalog is not a file: {catalog}")
    cases = select_split(select_profile(select_slice(load_pair_catalog(catalog), slice_name), profile), split)
    try:
        result = evaluate_catalog(cases, pointers=True if pointers else None)
    except ValueError as exc:
        raise SystemExit(f"--k-runs: {exc}") from exc
    if json_out:
        print(json.dumps(result_payload(result), indent=2, sort_keys=True))
        return 0
    print_pair_metrics("pair eval", result)
    print(f"signals={len(result.signals)}")
    return 0


def _repos(directory: Path, name: str, *, fetch: bool, json_out: bool) -> int:
    """List the pinned checkouts and check each recipe against the code it claims to describe.

    Offline by default: without ``--fetch`` a missing checkout is reported as missing, not fetched. Exit is
    non-zero only when a materialised checkout CONTRADICTS its recipe -- a recipe that merely has not been
    fetched is not a failure, or CI could never run this.
    """
    recipes = select_repo(load_repo_recipes(directory), name)
    if not recipes:
        raise SystemExit(f"no repository recipe matches {name!r} in {directory}")

    rows: list[dict[str, object]] = []
    contradicted = 0
    for recipe in recipes:
        problems: tuple[str, ...] = ()
        try:
            root = resolve_repo(recipe, fetch=True if fetch else None)
        except RepoUnavailable:
            state, path = "absent", checkout_path(recipe)
        else:
            problems = verify_repo(recipe, root)
            state, path = ("contradicted" if problems else "verified"), root
            contradicted += 1 if problems else 0
        rows.append({**recipe_payload(recipe), "state": state, "path": str(path), "problems": list(problems)})

        if not json_out:
            in_scope = len(recipe.in_scope)
            measures = ",".join(recipe.measures)
            print(f"{recipe.name:10} {state:13} {recipe.language:8} measures={measures} known={len(recipe.known)} in_scope={in_scope}")
            for problem in problems:
                print(f"  ! {problem}")
            if state == "absent":
                print(f"  fetch with: ousast repos --repo {recipe.name} --fetch")

    if json_out:
        print(json.dumps(rows, indent=2, sort_keys=True))
    return 1 if contradicted else 0


def _model_skipped(reason: str) -> dict[str, object]:
    """A skipped stage reports the same SHAPE as a run one, plus why it skipped.

    A consumer reading this should not have to branch on whether the stage ran; a payload that is sometimes
    a full record and sometimes a single key is how a report ends up silently omitting a section.
    """
    return {**_model_payload(None), "skipped": reason}


def _model_payload(
    result: object, *, unjudged_paths: tuple[str, ...] = (), coverage: Sequence[Mapping[str, object]] = ()
) -> dict[str, object]:
    """What the scan records about the model layer.

    `unjudged_sample` names the regions the budget did not reach, lowest-ranked first. Counting them is the
    honesty requirement (Req 3.3); NAMING them is what makes an exclusion list for a large repository
    something a maintainer can write from evidence rather than guesswork.
    """
    scope = getattr(result, "scope", None)
    outcomes = getattr(result, "question_outcomes", ())
    if scope is not None:
        unjudged_paths = tuple(
            dict.fromkeys(
                [q.identity.path for q in scope.deferred if q.reason != "tier_zero"]
                + [q.identity.path for q in outcomes if q.status != "completed"]
            )
        )[:10]
    return {
        "scope": scope.to_payload() if scope is not None else None,
        "question_outcomes": [q.to_payload() for q in outcomes],
        "family_coverage": [c.to_payload() for c in getattr(result, "family_coverage", ())],
        "partitions": [asdict(p) for p in getattr(result, "partitions", ())],
        "by_rung": dict(getattr(result, "by_rung", {}) or {}),
        "regions_scanned": getattr(result, "regions_scanned", 0),
        "regions_unjudged": getattr(result, "regions_unjudged", 0),
        "regions_unasked": getattr(result, "regions_unasked", 0),
        "unjudged_sample": list(unjudged_paths),
        "model_calls": getattr(result, "model_calls", 0),
        "cost_usd": getattr(result, "cost_usd", 0.0),
        "seconds": getattr(result, "seconds", 0.0),
        "build_seconds": getattr(result, "build_seconds", 0.0),
        "query_seconds": getattr(result, "query_seconds", 0.0),
        "query_seconds_by_kind": dict(getattr(result, "query_seconds_by_kind", {}) or {}),
        "arbitrate_seconds": getattr(result, "arbitrate_seconds", 0.0),
        "coverage": list(coverage),
    }


def _run_model_layer(
    *,
    root: Path,
    targets: list[FileTarget],
    entries: Sequence[object],
    settings: ModelLayerConfig,
    runtime: HarnessRuntime,
    config: ResolvedConfig,
) -> tuple[list[StaticFinding], dict[str, object]]:
    """Fifth MAP proposer (contributor-scan Req 1): the model layer, arbitrating regions of the repository.

    Additive by construction. Without a CPG engine this records a reason and returns nothing, and the scan is
    byte-for-byte what it is today -- the model layer must never make the tool worse for someone who does not
    have Joern.
    """
    if not settings.enabled:
        return [], _model_skipped("disabled")

    from .cpg.backend import resolve_cpg_backend
    from .model.endpoint import resolve_chat_endpoint, resolve_models
    from .model.regions import regions_for
    from .model.scan import ScanBudget, scan_repository
    from .model.shipped import declared_sources

    backend = resolve_cpg_backend()
    if not backend.available():
        runtime.state["degradations"].append({"stage": "model", "reason": "cpg_unavailable"})
        return [], _model_skipped("cpg_unavailable")

    # The project's own build declaration, when it makes one, decides what the budget is spent on first.
    regions = regions_for(entries, targets, shipped=declared_sources(root))
    if not regions:
        return [], _model_skipped("no_regions")

    # Req 5.6: what each family could see, recorded whether or not it found anything. A family that ran
    # and reported nothing is the case a reader is most likely to misread.
    from .model.report import coverage_rows

    coverage = coverage_rows(regions)

    resolved = resolve_chat_endpoint(config)
    client = resolved[0] if resolved else None
    if client is None:
        # Not a failure: entailment still works and only the suspicion band goes unasked (Req 11.3).
        runtime.state["degradations"].append({"stage": "model", "reason": "learning_endpoint_unavailable"})

    result = runtime.run_stage(
        "model",
        lambda: scan_repository(
            root,
            regions,
            backend=backend,
            client=client,
            model=resolve_models(config)[0],
            budget=ScanBudget(max_model_calls=settings.max_model_calls, max_regions=settings.max_regions),
        ),
    )
    for degradation in result.degradations:
        runtime.state["degradations"].append(dict(degradation))

    findings = [_finding_from_model(item) for item in result.findings]
    return findings, _model_payload(result, coverage=coverage)


def _model_rationale(item: object, witness: str, family: str) -> str:
    """The witness, plus how many entry points reach this site when more than one does.

    A site reached from twenty-six entry points is one defect with a wide blast radius, which is worth
    saying once. It used to be said twenty-six times.
    """
    base = witness or f"the model layer reported a {family} candidate"
    reached = int(getattr(item, "reached_from", 1) or 1)
    return base if reached <= 1 else f"{base} (reached from {reached} regions)"


def _finding_from_model(item: object) -> StaticFinding:
    """A model finding as a StaticFinding, carrying its rung and witness (Req 5.1)."""
    site = str(getattr(item, "site", ""))
    path, _, rest = site.partition(":")
    line, _, function = rest.partition(":")
    rung = getattr(item, "rung", None)
    witness = str(getattr(item, "witness", "") or getattr(item, "contradiction", ""))
    family = str(getattr(item, "family", ""))
    return StaticFinding(
        finding_id=f"model:{family}:{site}",
        path=path or site,
        title=f"{family} ({getattr(rung, 'value', rung)})",
        severity="medium",
        confidence="medium",
        evidence_level="suspicion",
        rationale=_model_rationale(item, witness, family),
        line=int(line) if line.isdigit() else None,
        function_name=function or None,
        reachability_status="unknown",
        reachability_evidence=[],
        reachability_conditions=[],
        tags=[f"family:{family}", f"rung:{getattr(rung, 'value', rung)}"],
        ranking_priority=0.0,
        rung=str(getattr(rung, "value", rung) or "suspicion"),
    )


def _check_obligations(
    *,
    root: Path,
    targets: list[FileTarget],
    entries: Sequence[object],
    facts: SemanticFacts | FactLoadError,
    settings: ObligationsConfig,
    runtime: HarnessRuntime,
    hunter_model: str | None = None,
) -> tuple[list[StaticFinding], dict[str, object], dict[str, dict[str, object]]]:
    """Fourth MAP proposer (authorization-obligations Req 3-6): obligations without a dominating discharger, at suspicion.

    Function-local until path records exist; the declared policy is read from the target; obligation findings never
    enter the sandbox candidate set.
    """
    from .semantic.ir import parse_file
    from .semantic.obligations import load_obligation_facts
    from .semantic.obligations.check import check_obligations, findings_to_static
    from .semantic.obligations.dominance import OrderDominance
    from .semantic.obligations.policy import PolicyError, load_declared_policy

    payload: dict[str, object] = {"sibling_sets": 0, "under_populated": 0, "operations": 0, "findings_by_label": {}, "policy_version": None}
    if isinstance(facts, FactLoadError):
        runtime.state["degradations"].append({"stage": "obligations", "reason": "facts_unavailable"})
        return [], payload, {}
    irs: dict[str, tuple[Any, str]] = {}
    texts: dict[str, str] = {}
    for target in targets:
        try:
            text = (root / target.path).read_text(errors="ignore")
        except OSError:
            continue
        texts[target.path] = text
        irs[target.path] = (parse_file(target.path, text, target.language), text)
    policy = None
    policy_path = root / settings.policy_path
    try:
        policy = load_declared_policy(policy_path)
    except PolicyError as exc:
        runtime.state["degradations"].append({"stage": "obligations", "reason": "policy_invalid", "detail": str(exc)[:200]})
    result = runtime.run_stage(
        "obligations",
        lambda: check_obligations(
            irs=irs,
            entries=entries,
            facts=load_obligation_facts(),
            flow_facts=facts,
            policy=policy,
            paths=(),
            dominance=OrderDominance(texts=texts),
            store_shapes=(),  # known-fix hints came from the deleted mechanism store
            min_siblings=settings.min_siblings,
        ),
    )
    from .semantic.obligations.intent import adjudicate_intent

    result = adjudicate_intent(result, client=tool_hunter.resolve_hunter_client(), model=hunter_model or "", texts=texts)
    for degradation in result.degradations:
        runtime.state["degradations"].append(degradation)
    by_label: dict[str, int] = {}
    for finding in result.findings:
        by_label[finding.label] = by_label.get(finding.label, 0) + 1
    payload = {
        "sibling_sets": len(result.sibling_sets),
        "under_populated": len(result.under_populated),
        "operations": result.operations,
        "findings_by_label": by_label,
        "policy_version": result.policy_version,
        "degradations": [dict(item) for item in result.degradations],
        "sets": [{"module": group.key[0], "resource": group.key[1], "handlers": len(group.handlers)} for group in result.sibling_sets],
    }
    statics = findings_to_static(result)
    cited: dict[str, dict[str, object]] = {}
    for finding, static in zip(result.findings, statics, strict=True):
        cited[static.finding_id] = {
            "obligation": finding.operation.kind,
            "resource": finding.operation.resource,
            "missing": finding.missing,
            "provenance": finding.provenance,
            "label": finding.label,
            "evidence": list(finding.evidence),
            "known_fix": finding.known_fix,
            "intent": finding.intent,
            "intent_rationale": finding.intent_rationale,
        }
    return statics, payload, cited


def _first_source(root: Path) -> str:
    from .preprocess import preprocess_repository

    _, targets = preprocess_repository(root)
    return targets[0].path if targets else "."


def _print_improve_outcomes(outcomes: list[RoundOutcome], manifest_path: Path, target: Path, ledger_path: Path, *, dry_run: bool) -> None:
    print(f"manifest={manifest_path}")
    print(f"target={target}")
    accepted = [o for o in outcomes if o.accepted]
    for outcome in outcomes:
        edits = ", ".join(f"{e.rule_id}:{e.from_status}->{e.to_status}" for e in outcome.edits) or "-"
        print(
            f"round {outcome.round}: {outcome.reason} | "
            f"recall {outcome.recall_before:.2%}->{outcome.recall_after:.2%} "
            f"fp {outcome.fp_before:.2%}->{outcome.fp_after:.2%} "
            f"score {outcome.score_before}->{outcome.score_after} | edits: {edits}"
        )
        if outcome.profile_regressions or outcome.profiles_under_minimum:
            print(f"  profiles: regressed={outcome.profile_regressions or '-'} under_minimum={outcome.profiles_under_minimum or '-'}")
    print(f"rounds={len(outcomes)} accepted={len(accepted)}")
    if dry_run:
        print("dry_run=true (no ledger or mechanism store written)")
    elif accepted:
        print(f"ledger={ledger_path}")
    else:
        print("ledger=unchanged (no round accepted)")


def _load_scan_findings(run_dir: Path) -> list[StaticFinding]:
    return load_findings(run_dir / "findings.json")


def _learn_labels(args: argparse.Namespace) -> int:
    """``ousast learn labels``: exit 2 on a source outside the list, an excluded file or an unreadable clone."""
    from .learn.labels import DEFAULT_CACHE, DEFAULT_SOURCES, LabelSourceError, build_labels, write_labels

    try:
        build, record = build_labels(
            args.root, sources_path=args.sources or DEFAULT_SOURCES, cache=args.cache or DEFAULT_CACHE, include_title=args.include_title,
            assumed_benign=not args.no_assumed_benign,
        )  # fmt: skip
    except LabelSourceError as exc:
        print(f"learn labels: {exc}", file=sys.stderr)
        return 2
    if args.out is not None:
        digest = write_labels(build, args.out / "labels.jsonl")
        (args.out / "labels.jsonl").rename(args.out / f"labels-{digest[:16]}.jsonl")
        record["labels_sha256"] = digest
    text = json.dumps(record, indent=2, sort_keys=True) + "\n"
    if args.snapshot is not None:
        args.snapshot.parent.mkdir(parents=True, exist_ok=True)
        args.snapshot.write_text(text, encoding="utf-8")
    pins = record["verified_pin_labels"]["by_family"]
    print(f"labels: {record['rows']} rows in {record['groups']} repository groups; per family (positive/negative groups, model):")
    for family, entry in pins.items():
        print(f"  {family}: {entry['positive_groups']}/{entry['negative_groups']} {entry['model']}")
    return 0


def _plane(args: argparse.Namespace) -> int:
    from .plane import reconciler  # lazy: the plane is a maintainer surface, not the scan path

    if args.plane_command == "run":
        _plane_memory("seed", args.run_manifest)
        result = reconciler.run(args.run_manifest, workers=args.workers, ax=args.ax)
        print(f"run finished: {result}")
        _plane_memory("ingest", args.run_manifest)
        return 0 if result == "done" else 1
    if args.plane_command == "remember":
        manifest = args.plane / "runs" / f"{args.run}.yaml"
        return _plane_memory("ingest", manifest if manifest.is_file() else None, run=args.run, population=args.population, split=args.split)
    if args.plane_command == "status":
        print(reconciler.status(args.run, units=args.units))
        return 0
    if args.plane_command == "alerts-engine":
        return _plane_alerts_engine(args)
    if args.plane_command == "harvest":
        return _plane_harvest(args)
    if args.plane_command == "doctor":
        checks = reconciler.doctor()
        for name, ok, text in checks:
            print(f"[{'ok' if ok else 'FAIL'}] {name}: {text}")
        return 0 if all(ok for _, ok, _ in checks) else 1
    if args.validation_set is not None:
        return _plane_increment(args)
    from .plane.workspaces import workspaces

    written = workspaces(args.population, args.out)
    print(f"{len(written)} Workspace manifests written to {args.out}")
    return 0


def _plane_harvest(args: argparse.Namespace) -> int:
    from .plane.generate import repin_templates
    from .plane.harvest import write_harvest

    if args.runner_image is not None:
        repin_templates(args.templates, args.runner_image)
    command = f"ousast plane harvest --labels {args.labels.name} --name {args.name} --ceiling {args.ceiling:g}"
    files = write_harvest(
        args.labels, args.plane, name=args.name, command=command, catalog=args.catalog, cache=args.cache, templates=args.templates,
        ceiling=args.ceiling,
    )  # fmt: skip
    for relative in sorted(k for k in files if k.startswith("runs/")):
        print(f"{args.plane / relative}: {files[relative].splitlines()[1].lstrip('# ')}")
    return 0


def _plane_alerts_engine(args: argparse.Namespace) -> int:
    from .plane import engine_alerts

    only = [c.strip() for c in args.cases.split(",") if c.strip()]
    try:
        results = engine_alerts.alerts_engine(
            args.run_manifest, source=args.source, only=only, work=args.work, cache=args.repos or engine_alerts.CACHE,
            deadline=args.deadline, image=args.image,
        )  # fmt: skip
    except (engine_alerts.EngineAlertsError, OSError, ValueError, subprocess.CalledProcessError) as exc:
        print(f"alerts-engine failed: {exc}", file=sys.stderr)
        return 2
    keys = ("case", "status", "reason", "alerts", "alerts_engine", "in_fix_range", "engine", "uncovered", "seconds")
    for result in results:
        print(json.dumps({k: result[k] for k in keys if k in result}, sort_keys=True))
    return 0


def _plane_memory(
    step: str, run_manifest: Path | None, *, run: str | None = None, population: str | None = None, split: str | None = None
) -> int:
    """``seed`` before a Run, ``ingest`` after it (and ``ousast plane remember``). A store failure is reported and
    never changes the run's result: ingest can be repeated by hand."""
    from .plane import memory, reconciler
    from .plane.tasks.remember import UNKNOWN, remember_run

    try:
        store = memory.open_store()
        if step == "seed":
            assert run_manifest is not None
            seeded = memory.seed(run_manifest, store)
            if seeded:
                print(f"memory {store.describe()}: seeded {len(seeded)} tasks (reused facts, loop snapshot): {', '.join(seeded)}")
            return 0
        annotations: dict[str, str] = {}
        if run_manifest is not None:
            spec, _ = reconciler.load_run(run_manifest)
            run, annotations = run or spec.metadata.name, dict(spec.metadata.annotations)
        assert run is not None
        results = remember_run(
            reconciler.run_dir(run), store, population=population or annotations.get(memory.POPULATION_ANNOTATION) or UNKNOWN,
            split=split or annotations.get(memory.SPLIT_ANNOTATION) or UNKNOWN,
        )  # fmt: skip
    except (memory.MemoryStoreError, OSError, ValueError) as exc:
        print(f"memory {step} failed: {exc}", file=sys.stderr)
        return 1
    fresh = [r for r in results if not r.skipped]
    kinds = " ".join(f"{k}={n}" for k, n in memory.counts(fresh).items()) or "none"
    print(f"memory {store.describe()}: {run}: {len(fresh)} tasks ingested, {len(results) - len(fresh)} already in the index; rows {kinds}")
    return 0


def _plane_increment(args: argparse.Namespace) -> int:
    from .plane.generate import LOOP_MANIFEST, Loop, increment

    if args.candidates is None or args.triage is None:
        print("--validation-set needs --candidates and --triage", file=sys.stderr)
        return 2
    home = str(Path.home())
    paths = (args.population, args.validation_set, args.candidates, args.triage, args.repos)
    shown = [str(p).replace(home, "~", 1) for p in paths]
    command = (
        f"ousast plane workspaces {shown[0]} --validation-set {shown[1]} --candidates {shown[2]} --triage {shown[3]}"
        f" --repos {shown[4]} --run {args.run}"
    )
    if args.runner_image is not None:
        command += f" --runner-image {str(args.runner_image).replace(home, '~', 1)}"
    loop = None
    if args.loop:
        git = lambda *a: subprocess.run(["git", *a], capture_output=True, text=True, check=True).stdout.strip()  # noqa: E731
        loop = Loop(
            repo=args.loop_repo or git("remote", "get-url", "origin"), commit=args.loop_commit or git("rev-parse", "HEAD"),
            manifest=args.loop_manifest or LOOP_MANIFEST, populations=tuple(args.qualify_population),
        )  # fmt: skip
        command += f" --loop --loop-commit {loop.commit} --loop-repo {loop.repo} --loop-manifest {loop.manifest}"
        command += "".join(f" --qualify-population {p}" for p in loop.populations)
    written = increment(
        args.population, args.validation_set, args.candidates, args.triage, plane=args.plane, run_name=args.run, command=command,
        repos=args.repos, runner_image=args.runner_image, loop=loop,
    )  # fmt: skip
    print(f"{len(written)} manifests written under {args.plane} (Run {args.run})")
    return 0


def _model_candidates(args: argparse.Namespace) -> int:
    """What the candidate enumerator can reach, per family and per slice. No model is called anywhere here.

    The ceiling is a property of the corpus and the enumerator, so it can end a design before a cent is spent
    on it — which is what it did for the pattern-ruleset generator (12.1% against the IR's 96.6%).
    """
    from .model.candidates import ceiling
    from .model.taxonomy import load_families

    cases = select_vendored(select_slice(load_pair_catalog(args.catalog), args.slice))
    coverage = ceiling(cases, taxonomy=load_families(), generator=args.generator)
    out: Path = args.out
    out.mkdir(parents=True, exist_ok=True)
    payload = coverage.to_dict()
    (out / "candidate-ceiling.json").write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
    if args.json:
        print(json.dumps(payload, indent=2, sort_keys=True))
    else:
        for family, block in sorted(coverage.per_family.items()):
            ceiling_value = float(block["ceiling"]) if isinstance(block["ceiling"], int | float) else 0.0
            print(
                f"model candidates {family}: {block['with_candidate']}/{block['pairs']} "
                f"= {ceiling_value:.1%} ceiling, sites {block['sites']}"
            )
        for slice_name, block in sorted(coverage.per_slice.items()):
            ceiling_value = float(block["ceiling"]) if isinstance(block["ceiling"], int | float) else 0.0
            print(f"  slice {slice_name}: {ceiling_value:.1%} over {block['pairs']} pairs")
    return 0


def _index(path: Path, config_path: Path, chunk_lines: int) -> int:
    if not path.exists() or not path.is_dir():
        raise SystemExit(f"index path is not a directory: {path}")
    config = load_config(config_path if config_path.exists() else None)
    run = create_scan_run(path, config)
    snapshot, targets = preprocess_repository(run.target, run.root / "preprocess" / "file_targets.json")
    chunks = build_code_chunks(run.target, targets, max_lines=chunk_lines)
    payload = {
        "store": config.embeddings.store or "json-local",
        "embedding_model": config.embeddings.model,
        "repo_root": snapshot.root,
        "repo_commit": snapshot.commit,
        "chunk_count": len(chunks),
        "chunks": [chunk.__dict__ for chunk in chunks],
    }
    path_out = run.root / "index" / "chunks.json"
    path_out.parent.mkdir(parents=True, exist_ok=True)
    path_out.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
    print(f"scan_id={run.scan_id}")
    print(f"run_dir={run.root}")
    print(f"chunks={len(chunks)}")
    print(f"index_artifact={path_out}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())