clay-borg/tools/cb-cost.py

#!/usr/bin/env python3
"""M-D2-CST: USD cost of agentic work, attributed to workplan tasks.

Normative spec: specs/CostAccounting.md. Decision: decisions/ADR-0003.

Reads Claude Code session transcripts, deduplicates by requestId, prices
each response at its own model's rate and its own cache TTL, and attributes
it to the task named by the next commit at or after it within the same
session.

Positive control (InnerLoop v1.0 §Step 5, CostAccounting CA-02/CA-14):
this tool asserts it did the work it reports. `--self-test` exercises four
assertions against fixtures with known answers; every reporting run checks
the dedup invariant and reconciles attributed + unattributed + open +
unpriced against the raw total, aborting rather than printing a number that
does not add up.

Usage:
  python3 tools/cb-cost.py                      # whole repo, no pin
  python3 tools/cb-cost.py --pin fc76445        # pinned at a commit
  python3 tools/cb-cost.py --by-task            # per-task attribution
  python3 tools/cb-cost.py --composition        # cost split by component
  python3 tools/cb-cost.py --self-test          # positive control
  python3 tools/cb-cost.py --json
"""

import argparse
import collections
import datetime
import glob
import json
import os
import re
import subprocess
import sys

try:
    import tomllib
except ModuleNotFoundError:  # pragma: no cover - py<3.11
    print("ERROR: needs Python 3.11+ for tomllib", file=sys.stderr)
    sys.exit(1)

from repo import ROOT as REPO  # noqa: E402  (single source of fact, T01)

PRICES = os.path.join(REPO, "benchmarks", "baselines", "model-prices.toml")
COMPONENTS = ("input", "output", "cache_read", "write_5m", "write_1h")
# CA-08 attribution key. Qualified first: a bare `T\d\d` collides across
# workplans — CB-WP-0002 T01, CB-WP-0003 T01 and CB-WP-0004 T01 all matched
# the same bucket, so a per-task figure summed three unrelated tasks. Found
# by T02's positive control, which reported $12.10 for "T01".
TASK_QUALIFIED_RE = re.compile(r"\b(CB-WP-\d+)[ -](T\d\d)\b")
TASK_RE = re.compile(r"\bT\d\d\b")


def task_label(subject):
    """Attribution label for a commit subject, or None.

    Qualified ids (`CB-WP-0004 T01`) become `CB-WP-0004-T01`. Bare ids
    (`T01: ...`) stay bare — commits predating this convention did not name
    their workplan and must not be retroactively assigned to one.
    """
    m = TASK_QUALIFIED_RE.search(subject)
    if m:
        return f"{m.group(1)}-{m.group(2)}"
    m = TASK_RE.search(subject)
    return m.group(0) if m else None


UNATTRIBUTED = "UNATTRIBUTED"
OPEN_REMAINDER = "OPEN (uncommitted)"


class Abort(Exception):
    """A positive-control failure. Never degrades to a printed number."""


# ---------------------------------------------------------------- pricing


def load_prices(path=PRICES):
    with open(path, "rb") as fh:
        return tomllib.load(fh)


def components(usage):
    """Token counts per billable component (CA-04: writes split by TTL)."""
    cc = usage.get("cache_creation") or {}
    return {
        "input": usage.get("input_tokens", 0),
        "output": usage.get("output_tokens", 0),
        "cache_read": usage.get("cache_read_input_tokens", 0),
        "write_5m": cc.get("ephemeral_5m_input_tokens", 0),
        "write_1h": cc.get("ephemeral_1h_input_tokens", 0),
    }


def rates_at(prices, model, when=None):
    """Input/output rate in force for `model` at ISO instant `when` (CA-16).

    Promotional rates are data, not comments. A response is priced at the
    promo rate when its timestamp falls on or before `promo_until`.
    """
    pr = prices.get(model)
    if not pr:
        return None
    if "promo_until" in pr and when:
        until = pr["promo_until"]
        # tomllib returns a datetime.date for a bare TOML date.
        if str(when)[:10] <= str(until)[:10]:
            return pr["promo_input"], pr["promo_output"]
    return pr["input"], pr["output"]


def check_price_sheet_age(prices, today=None):
    """CA-17: a stale sheet invalidates verdicts, so it fails a command."""
    import datetime as _dt

    recorded = prices.get("recorded")
    if recorded is None:
        return "price sheet has no `recorded` date"
    max_age = prices.get("max_age_days", 90)
    today = today or _dt.date.today()
    if isinstance(recorded, _dt.datetime):
        recorded = recorded.date()
    age = (today - recorded).days
    if age > max_age:
        return (f"price sheet is {age} days old (max {max_age}); refresh "
                f"benchmarks/baselines/model-prices.toml or new M-D2-CST "
                f"`better` verdicts are invalid")
    return None


def price_of(prices, model, toks, when=None):
    """USD for one response. Returns None when the model is unpriced (CA-05)."""
    pr = prices.get(model)
    if not pr:
        return None
    rin, rout = rates_at(prices, model, when)
    cache = prices["cache"]
    unit = rin / 1e6
    return (
        toks["input"] * unit
        + toks["output"] * rout / 1e6
        + toks["cache_read"] * unit * cache["read"]
        + toks["write_5m"] * unit * cache["write_5m"]
        + toks["write_1h"] * unit * cache["write_1h"]
    )


# ------------------------------------------------------------- transcripts


def transcript_paths(slug):
    """Every transcript for the repo, including the subagent tree (CA-06)."""
    base = os.path.expanduser(f"~/.claude/projects/{slug}")
    return sorted(glob.glob(f"{base}/*.jsonl")) + sorted(
        glob.glob(f"{base}/*/subagents/agent-*.jsonl")
    )


def read_responses(path, pin=None):
    """Deduplicate one transcript by requestId, asserting CA-02."""
    groups = collections.defaultdict(list)
    for line in open(path):
        try:
            d = json.loads(line)
        except json.JSONDecodeError:
            continue
        if d.get("type") != "assistant":
            continue
        usage = (d.get("message") or {}).get("usage")
        if usage is None:
            continue
        ts = d.get("timestamp") or ""
        if pin and ts > pin:
            continue
        groups[d.get("requestId")].append(d)

    out = []
    for rid, rows in groups.items():
        # CA-02 positive control. Input-side counters are charged once per
        # response and MUST be identical across the group; a divergence means
        # the format changed underneath us and dedup would mis-bill.
        #
        # output_tokens is different: in streamed transcripts (observed in the
        # subagents/ tree) early lines carry a PARTIAL count and only the last
        # line carries the final total — 5, 5, 195 for one response. Taking
        # the first row silently under-reports output, which is why this is a
        # max() and not a first-wins.
        seen_model = {r["message"].get("model") for r in rows}
        if len(seen_model) != 1:
            raise Abort(
                f"{os.path.basename(path)}: requestId {rid} spans {len(rows)} lines "
                f"with {len(seen_model)} distinct models — CA-02 violated"
            )
        per_row = [components(r["message"]["usage"]) for r in rows]
        toks = dict(per_row[0])
        for field in ("input", "cache_read", "write_5m", "write_1h"):
            distinct = {t[field] for t in per_row}
            if len(distinct) != 1:
                raise Abort(
                    f"{os.path.basename(path)}: requestId {rid} has {len(distinct)} "
                    f"distinct values for {field} across {len(rows)} lines "
                    f"({sorted(distinct)}) — CA-02 violated; input-side counters "
                    f"are charged once per response and must not vary"
                )
        toks["output"] = max(t["output"] for t in per_row)
        head = rows[0]
        # Tool calls are spread across the group's lines, so they are counted
        # over the whole group — one response may carry several (SS-05).
        blocks = [c for r in rows for c in (r["message"].get("content") or [])
                  if c.get("type") == "tool_use"]
        tool_calls = len(blocks)
        cats = sorted({
            c for c in (classify_tool(b.get("name"), b.get("input") or {})
                        for b in blocks) if c
        })
        out.append(
            {
                "request_id": rid,
                "model": head["message"].get("model"),
                "timestamp": head.get("timestamp") or "",
                "session": head.get("sessionId") or os.path.basename(path),
                "toks": toks,
                "tool_calls": tool_calls,
                "categories": cats,
                "subagent": "/subagents/" in path,
            }
        )
    return out


# CB-RES-0003: which turns are mechanical (a deterministic tool could do
# them) versus judgment (only an agent can). The baseline for measuring
# whether automation actually removes turns rather than relocating them.
def classify_tool(name, inp):
    if name == "mcp__dev-hub__update_task_status":
        return "hub task status"
    if name.startswith("mcp__dev-hub__"):
        return "hub other"
    if name != "Bash":
        return None
    cmd = (inp.get("command") or "").strip()
    if not cmd:
        return None
    if "python3 - <<" in cmd:
        if "status: todo" in cmd or "status: done" in cmd:
            return "workplan status edit"
        if "jsonl" in cmd or "requestId" in cmd or "usage" in cmd:
            return "ad-hoc transcript analysis"
        return "ad-hoc text patching"
    if cmd.startswith(("cd ", "export ")):
        return "environment setup"
    if cmd.split()[0] in ("grep", "ls", "wc", "sed", "head", "tail", "cat", "find"):
        return "orientation / inspect"
    if cmd.startswith("git "):
        return "git"
    if "make " in cmd:
        return "make (gates)"
    return None


# Categories a deterministic tool could plausibly own. Judgment-bearing
# categories (git commit messages, make gates) are excluded deliberately.
MECHANICAL = frozenset({
    "environment setup", "ad-hoc text patching", "orientation / inspect",
    "ad-hoc transcript analysis", "hub task status", "workplan status edit",
})


def session_shape(responses):
    """SH-1..SH-3 from specs/SessionShape.md."""
    import statistics

    ctx = sorted(r["toks"]["cache_read"] for r in responses)
    with_tools = [r for r in responses if r["tool_calls"] > 0]
    batched = [r for r in with_tools if r["tool_calls"] > 1]
    calls = sum(r["tool_calls"] for r in with_tools)
    pct = statistics.quantiles(ctx, n=100) if len(ctx) > 1 else [ctx[0]] * 99
    return {
        "SH-1_mean_context": statistics.mean(ctx) if ctx else 0,
        "SH-2_p90_context": pct[89],
        "SH-3_batching_rate": (len(batched) / len(with_tools)) if with_tools else 0.0,
        "p50_context": pct[49],
        "responses_with_tools": len(with_tools),
        "tool_calls": calls,
        "calls_in_batched_turns": calls - (len(with_tools) - len(batched)),
    }


# ------------------------------------------------------------- attribution


def commit_index(pin=None):
    """(utc_time, subject) for each commit, oldest first (CA-09)."""
    fmt = subprocess.run(
        ["git", "-C", REPO, "log", "--format=%cI|%s", "--reverse"],
        capture_output=True,
        text=True,
        check=True,
    ).stdout.splitlines()
    rows = []
    for line in fmt:
        iso, _, subject = line.partition("|")
        # CA-09: convert, never subtract a fixed offset — this repo has two.
        utc = (
            datetime.datetime.fromisoformat(iso)
            .astimezone(datetime.timezone.utc)
            .isoformat()
            .replace("+00:00", "Z")
        )
        rows.append((utc, subject))
    if pin:
        rows = [r for r in rows if r[0] <= pin]
    return rows


def resolve_pin(ref):
    """A commit-ish pin becomes the UTC instant of that commit."""
    if not ref:
        return None
    if ref.endswith("Z"):
        return ref
    iso = subprocess.run(
        ["git", "-C", REPO, "log", "-1", "--format=%cI", ref],
        capture_output=True,
        text=True,
        check=True,
    ).stdout.strip()
    return (
        datetime.datetime.fromisoformat(iso)
        .astimezone(datetime.timezone.utc)
        .isoformat()
        .replace("+00:00", "Z")
    )


def attribute(responses, commits):
    """Assign each response a bucket per CA-08/CA-10/CA-11.

    Intervals are ending-at-commit: (prev_commit, this_commit]. Subagent
    responses inherit the interval of their timestamp like any other.
    """
    label_for = []
    prev = ""
    for ts, subject in commits:
        label_for.append((prev, ts, task_label(subject) or UNATTRIBUTED))
        prev = ts
    last = commits[-1][0] if commits else ""

    for r in responses:
        ts = r["timestamp"]
        if last and ts > last:
            r["task"] = OPEN_REMAINDER  # CA-11
            continue
        r["task"] = UNATTRIBUTED
        for lo, hi, label in label_for:
            if lo < ts <= hi:
                r["task"] = label
                break
    return responses


# ----------------------------------------------------------------- report


def collect(slug, pin_ref=None, since_ref=None):
    """Report over (since_ref, pin_ref]. Both ends optional.

    `since` exists for CB-WP-0004 T05: comparing a pass against the pass
    that measured it needs two disjoint windows over the same transcripts,
    not two whole-corpus totals. Attribution and reconciliation are
    unchanged — the window only selects which responses are counted.
    """
    prices = load_prices()
    pin = resolve_pin(pin_ref)
    since = resolve_pin(since_ref)
    paths = transcript_paths(slug)
    if not paths:
        raise Abort(f"no transcripts found for {slug}")

    responses = []
    for p in paths:
        responses.extend(read_responses(p, pin))
    if since:
        responses = [r for r in responses if r["timestamp"] > since]
    if not responses:
        # Positive control: a run that measured nothing must not report $0.00
        # as though it were an answer.
        raise Abort(f"no responses in {len(paths)} transcript(s) — refusing to report")

    stale = check_price_sheet_age(prices)
    if stale:
        raise Abort(stale)

    for r in responses:
        r["cost"] = price_of(prices, r["model"], r["toks"], r["timestamp"])

    attribute(responses, commit_index(pin))

    by_task = collections.defaultdict(float)
    # T02: per-task token detail, so a task close can report *measured*
    # tokens. Every update_task_status in this project before T02 carried
    # hand-typed estimates — in a repo whose finding is that estimated
    # token counts are worthless.
    task_detail = collections.defaultdict(
        lambda: {"cost": 0.0, "responses": 0, "models": collections.Counter(),
                 "toks": collections.Counter()})
    by_component = collections.Counter()
    by_component_cost = collections.defaultdict(float)
    by_model = collections.defaultdict(float)
    unpriced = []
    cache = prices["cache"]
    for r in responses:
        if r["cost"] is None:
            unpriced.append(r)
            continue
        by_task[r["task"]] += r["cost"]
        d = task_detail[r["task"]]
        d["cost"] += r["cost"]
        d["responses"] += 1
        d["models"][r["model"]] += 1
        d["toks"].update(r["toks"])
        by_model[r["model"]] += r["cost"]
        rin, rout = rates_at(prices, r["model"], r["timestamp"])
        unit = rin / 1e6
        rates = {
            "input": unit,
            "output": rout / 1e6,
            "cache_read": unit * cache["read"],
            "write_5m": unit * cache["write_5m"],
            "write_1h": unit * cache["write_1h"],
        }
        for k, n in r["toks"].items():
            by_component[k] += n
            by_component_cost[k] += n * rates[k]

    total = sum(by_task.values())
    # CA-14: reconciliation is asserted, not assumed.
    residual = total - sum(by_component_cost.values())
    if abs(residual) > 0.005:
        raise Abort(
            f"reconciliation failed: task total ${total:,.4f} vs component total "
            f"${sum(by_component_cost.values()):,.4f} (residual ${residual:,.4f})"
        )

    # Tool mix: a turn's whole cost is charged to each category it touched,
    # so columns may overlap and must not be summed as if disjoint.
    mix_turns, mix_cost = collections.Counter(), collections.defaultdict(float)
    for r in responses:
        for c in r.get("categories") or []:
            mix_turns[c] += 1
            mix_cost[c] += r["cost"] or 0.0
    mech = [r for r in responses
            if set(r.get("categories") or []) & MECHANICAL]

    sub = sum(r["cost"] or 0 for r in responses if r["subagent"])
    return {
        "session_shape": session_shape(responses),
        "tool_mix": {
            "turns": dict(mix_turns),
            "cost": dict(mix_cost),
            "mechanical_turns": len(mech),
            "mechanical_cost": sum(r["cost"] or 0 for r in mech),
        },
        "slug": slug,
        "pin": pin,
        "since": since,
        "responses": len(responses),
        "transcripts": len(paths),
        "total": total,
        "subagent_total": sub,
        "main_total": total - sub,
        "by_task": dict(by_task),
        "by_task_detail": {
            k: {
                "cost": v["cost"],
                "responses": v["responses"],
                # The model that ran the most responses for this task. The
                # hub schema has one model field per event; a task split
                # across models cannot be represented faithfully, so the
                # full count travels in the note.
                "model": v["models"].most_common(1)[0][0],
                "models": dict(v["models"]),
                "toks": dict(v["toks"]),
                # CA-03/CA-04: the hub has no cache fields. tokens_in is
                # the honest sum of everything billed on the input side.
                "tokens_in": (v["toks"].get("input", 0)
                              + v["toks"].get("cache_read", 0)
                              + v["toks"].get("write_5m", 0)
                              + v["toks"].get("write_1h", 0)),
                "tokens_out": v["toks"].get("output", 0),
            }
            for k, v in task_detail.items()
        },
        "by_model": dict(by_model),
        "tokens": dict(by_component),
        "cost_by_component": dict(by_component_cost),
        "unpriced": [
            {"model": r["model"], "tokens": r["toks"]} for r in unpriced
        ],
        "reconciled": True,
        "residual": residual,
    }


def render(rep, by_task=False, composition=False):
    print(f"M-D2-CST cost report — {rep['slug']}")
    print(f"  pin           {rep['pin'] or '(none — live file, not reproducible)'}")
    if rep.get("since"):
        print(f"  since         {rep['since']}  (window is exclusive of this instant)")
    print(f"  transcripts   {rep['transcripts']}   responses {rep['responses']}")
    print(f"  main          ${rep['main_total']:>10,.2f}")
    print(f"  subagent tree ${rep['subagent_total']:>10,.2f}")
    print(f"  TOTAL         ${rep['total']:>10,.2f}")

    if composition:
        print("\n  composition")
        tot = rep["total"] or 1
        for k in COMPONENTS:
            c = rep["cost_by_component"].get(k, 0.0)
            print(
                f"    {k:<12}{rep['tokens'].get(k,0):>14,} tok  "
                f"${c:>9,.2f}  {100*c/tot:>5.1f}%"
            )

    if by_task:
        print("\n  by task")
        rows = sorted(rep["by_task"].items(), key=lambda kv: -kv[1])
        tot = rep["total"] or 1
        for task, c in rows:
            print(f"    {task:<20}${c:>9,.2f}  {100*c/tot:>5.1f}%")
        # CA-10: the limit is reported with the number, every time.
        un = rep["by_task"].get(UNATTRIBUTED, 0.0)
        print(
            f"\n    NOTE: {100*un/tot:.0f}% of cost is UNATTRIBUTED — commits whose "
            f"subject carries no T## tag.\n"
            f"    A per-task table is a view over {100*(1-un/tot):.0f}% of spend."
        )

    mix = rep["tool_mix"]
    if mix["turns"]:
        print("\n  tool mix — a turn's cost is charged to every category it")
        print("  touched, so columns overlap and must not be summed")
        for k in sorted(mix["turns"], key=lambda x: -mix["cost"][x]):
            tag = "mech" if k in MECHANICAL else "    "
            print(f"    {tag} {k:<28}{mix['turns'][k]:>5} turns  "
                  f"${mix['cost'][k]:>8,.2f}")
        print(f"    MECHANICAL (deduplicated)      "
              f"{mix['mechanical_turns']:>5} turns  ${mix['mechanical_cost']:>8,.2f}"
              f"  = {100*mix['mechanical_cost']/(rep['total'] or 1):.0f}% of pass")

    sh = rep["session_shape"]
    print("\n  session shape (specs/SessionShape.md)")
    print(f"    SH-1 mean context   {sh['SH-1_mean_context']:>12,.0f} tok  "
          f"[{'ok ' if sh['SH-1_mean_context']<=200_000 else 'FAIL'} target 200,000]")
    print(f"    SH-2 p90 context    {sh['SH-2_p90_context']:>12,.0f} tok  "
          f"[{'ok ' if sh['SH-2_p90_context']<=300_000 else 'FAIL'} target 300,000]")
    print(f"    SH-3 batching rate  {100*sh['SH-3_batching_rate']:>11.1f}%   "
          f"[{'ok ' if sh['SH-3_batching_rate']>=0.20 else 'FAIL'} target 20.0%]")
    print(f"         {sh['tool_calls']} tool calls in {sh['responses_with_tools']} responses; "
          f"{sh['calls_in_batched_turns']} in batched turns")

    if rep["unpriced"]:
        print(f"\n  UNPRICED ({len(rep['unpriced'])} responses, model not in sheet):")
        for u in rep["unpriced"]:
            print(f"    {u['model']}  {u['tokens']}")

    print(f"\n  reconciled: ok (residual ${rep['residual']:.6f})")


# -------------------------------------------------------------- self-test


def self_test():
    """AC-5..AC-8. Each asserts a failure mode is actually detected."""
    prices = load_prices()
    checks = []

    def check(name, ok, detail=""):
        checks.append((name, ok, detail))

    # AC-8: per-TTL cache pricing. 5m must not be priced at the 1h rate.
    toks = {"input": 0, "output": 0, "cache_read": 0, "write_5m": 100_000, "write_1h": 0}
    got = price_of(prices, "claude-fable-5", toks)
    want = 100_000 * (10.0 / 1e6) * 1.25
    wrong = 100_000 * (10.0 / 1e6) * 2.0
    check("AC-8 5m cache priced at write_5m", abs(got - want) < 1e-9 and got != wrong,
          f"${got:.4f} (1h would be ${wrong:.4f})")

    # AC-5: dedup invariant is enforced.
    import tempfile
    with tempfile.NamedTemporaryFile("w", suffix=".jsonl", delete=False) as fh:
        u1 = {"input_tokens": 1, "output_tokens": 2, "cache_read_input_tokens": 3,
              "cache_creation": {"ephemeral_5m_input_tokens": 0,
                                 "ephemeral_1h_input_tokens": 0}}
        u2 = dict(u1)
        # Diverging cache_read is a real violation (input-side, charged once).
        u2["cache_read_input_tokens"] = 999
        for u in (u1, u2):
            fh.write(json.dumps({"type": "assistant", "requestId": "r1",
                                 "timestamp": "2026-01-01T00:00:00Z",
                                 "message": {"model": "claude-opus-5", "usage": u}}) + "\n")
        bad = fh.name
    try:
        read_responses(bad)
        check("AC-5 dedup violation aborts", False, "no Abort raised")
    except Abort:
        check("AC-5 dedup violation aborts", True)
    finally:
        os.unlink(bad)

    # AC-9: streamed partial output_tokens must resolve to the final total,
    # not the first line. Regression pin on a real defect: first-wins scored
    # a measured subagent response at 5 output tokens instead of 195.
    with tempfile.NamedTemporaryFile("w", suffix=".jsonl", delete=False) as fh:
        for out_tok in (5, 5, 195):
            u = {"input_tokens": 2, "output_tokens": out_tok,
                 "cache_read_input_tokens": 0,
                 "cache_creation": {"ephemeral_5m_input_tokens": 19008,
                                    "ephemeral_1h_input_tokens": 0}}
            fh.write(json.dumps({"type": "assistant", "requestId": "r2",
                                 "timestamp": "2026-01-01T00:00:00Z",
                                 "message": {"model": "claude-fable-5",
                                             "usage": u}}) + "\n")
        partial = fh.name
    try:
        rows = read_responses(partial)
        got = rows[0]["toks"]["output"] if rows else None
        check("AC-9 streamed partial output resolves to final", got == 195,
              f"got {got}, first-wins would give 5")
    finally:
        os.unlink(partial)

    # CA-08 label collision (found by T02's positive control): three
    # workplans each had a T01, and a bare-label bucket summed all three
    # into one figure — $12.10 for a task that cost $2.33. Qualified
    # subjects must produce distinct buckets.
    check("CA-08 qualified task ids do not collide across workplans",
          task_label("CB-WP-0004 T01: fix env") == "CB-WP-0004-T01"
          and task_label("CB-WP-0002 T01: survey") == "CB-WP-0002-T01"
          and task_label("CB-WP-0004-T01: hyphenated") == "CB-WP-0004-T01")
    check("CA-08 unqualified subjects stay bare, not retro-assigned",
          task_label("T01: cost-accounting survey") == "T01"
          and task_label("chore: no task here") is None)

    # CA-16: a dated promo rate must apply before its expiry and lapse after.
    pr = prices
    before = rates_at(pr, "claude-sonnet-5", "2026-07-31T00:00:00Z")
    after = rates_at(pr, "claude-sonnet-5", "2026-09-01T00:00:00Z")
    check("CA-16 promo rate applies before expiry and lapses after",
          before == (2.0, 10.0) and after == (3.0, 15.0),
          f"{before} -> {after}")

    # CA-17: staleness must actually trip, or the rule is decorative again.
    import datetime as _dt
    fresh = check_price_sheet_age(pr, _dt.date(2026, 8, 1))
    stale = check_price_sheet_age(pr, _dt.date(2026, 11, 10))
    check("CA-17 staleness detected past max_age_days",
          fresh is None and stale is not None, "fresh ok, 102d trips")

    # CB-02: thresholds must be ordered, or the budget silently never fires.
    ap_defaults = {"soft": 10.00, "hard": 22.00}
    check("CB-02 budget thresholds ordered and positive",
          0 < ap_defaults["soft"] < ap_defaults["hard"],
          f"soft ${ap_defaults['soft']:.2f} < hard ${ap_defaults['hard']:.2f}")

    # AC-6: zero responses must not report $0.00 as an answer.
    with tempfile.NamedTemporaryFile("w", suffix=".jsonl", delete=False) as fh:
        fh.write(json.dumps({"type": "user", "message": {}}) + "\n")
        empty = fh.name
    try:
        got = read_responses(empty)
        check("AC-6 empty transcript yields no responses", got == [], f"{len(got)} rows")
    finally:
        os.unlink(empty)

    # AC-7: the subagent tree is discovered by the path globs.
    slug = "-home-worsch-clay-borg"
    paths = transcript_paths(slug)
    subs = [p for p in paths if "/subagents/" in p]
    check("AC-7 subagent tree enumerated", len(subs) > 0,
          f"{len(subs)} subagent transcript(s) of {len(paths)} total")

    print("cb-cost self-test (positive control)")
    ok = True
    for name, passed, detail in checks:
        print(f"  [{'ok ' if passed else 'FAIL'}] {name}" + (f"  — {detail}" if detail else ""))
        ok &= passed
    return 0 if ok else 1


def budget(slug, soft, hard):
    """Live cost budget (specs/CostAccounting.md §7).

    The per-task figure needs the commit that closes the task, so it can
    only ever be retrospective. What IS observable mid-task is spend since
    the LAST commit — the open remainder — because the transcript is an
    append-live file. That is the number a budget can actually fire on.
    """
    try:
        rep = collect(slug, None)
    except Abort as e:
        print(f"ABORT — {e}", file=sys.stderr)
        return 1
    open_spend = rep["by_task"].get(OPEN_REMAINDER, 0.0)
    head = subprocess.run(
        ["git", "-C", REPO, "log", "-1", "--format=%h %s"],
        capture_output=True, text=True, check=True).stdout.strip()

    print("cost budget — spend since the last commit")
    print(f"  last commit   {head}")
    print(f"  open spend    ${open_spend:,.2f}")
    print(f"  soft / hard   ${soft:,.2f} / ${hard:,.2f}")
    if open_spend > hard:
        print(f"\n  HARD BREACH — ${open_spend:,.2f} > ${hard:,.2f}. Commit what "
              f"works, or stop and decompose. Uncommitted work is also "
              f"unattributable.", file=sys.stderr)
        return 1
    if open_spend > soft:
        print(f"\n  soft breach — ${open_spend:,.2f} > ${soft:,.2f}. State progress "
              f"as a percentage and decide: continue, or commit and decompose.")
        return 0
    print("\n  within budget")
    return 0


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--slug", default="-home-worsch-clay-borg")
    ap.add_argument("--pin", help="commit-ish or ISO Z instant (CA-07)")
    ap.add_argument("--since", help="window start, exclusive: commit-ish or ISO Z")
    ap.add_argument("--by-task", action="store_true")
    ap.add_argument("--composition", action="store_true")
    ap.add_argument("--session-shape", action="store_true",
                    help="SH-1..SH-3 (always shown in the default report)")
    ap.add_argument("--budget", action="store_true",
                    help="CB-01/CB-02: spend since the last commit, live")
    ap.add_argument("--soft", type=float, default=10.00)
    ap.add_argument("--hard", type=float, default=22.00)
    ap.add_argument("--self-test", action="store_true")
    ap.add_argument("--json", action="store_true")
    args = ap.parse_args()

    if args.self_test:
        return self_test()

    if args.budget:
        return budget(args.slug, args.soft, args.hard)

    try:
        rep = collect(args.slug, args.pin, args.since)
    except Abort as e:
        print(f"ABORT — {e}", file=sys.stderr)
        return 1

    if args.json:
        print(json.dumps(rep, indent=2))
    else:
        render(rep, by_task=args.by_task, composition=args.composition)
    return 0


if __name__ == "__main__":
    sys.exit(main())