feat(subagent-model): pick a cheaper subagent model only at 90 % same-result confidence and log spawns with their quality

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

README.md

@@ -19,6 +19,7 @@ flowchart LR
     edit_hook([edit in a git worktree])
     reminders([reminder hooks])
     code_task([code task])
+    spawn([subagent spawn])
   end
   subgraph commit [Commit]
     commit_next[commit-next]
@@ -34,6 +35,7 @@ flowchart LR
     coordinate[coordinate]
     funnel[funnel]
     cleanup[cleanup]
+    subagent_model[subagent-model]
   end
   subgraph ci [CI]
     observe[observe]
@@ -81,6 +83,7 @@ flowchart LR
   reminders -.-> autotune
   code_task -.-> no_defaults
   code_task -.-> scope_guard
+  spawn -.-> subagent_model
 
   commit_next --> merge_fix
   commit_next --> commit_no_verify
@@ -96,6 +99,8 @@ flowchart LR
   cleanup --> democracy
   funnel --> democracy
   funnel --> dialectic
+  funnel --> subagent_model
+  dialectic --> subagent_model
   democracy --> merge_fix
 
   observe --> triage
@@ -155,7 +160,7 @@ Copy the skills into a specific project (`<repo>/.agents/skills` and `<repo>/.cl
 make project TARGET=/path/to/repo
 ```
 
-Both commands also patch the target's `.claude/settings.local.json` — the machine-local, untracked file, so a project's reviewed `.claude/settings.json` is never rewritten by an install: the caveman and ponytail plugins are enabled so their modes auto-activate on session start, and two reminder hooks are registered — `autotune` points you at `/autotune` at most once an hour, `dream` points you at `/dream` at most once every twelve hours per repository (stamped in that repository's git dir). A PreToolUse gate from the `democracy` skill blocks edits to files in a git worktree until the session holds a claim (`skills/democracy/scripts/council.py`), and its resource watch (`skills/democracy/scripts/watch.py`) measures disk, Docker and caches, cleans up low-risk data and blocks new heavy jobs before the disk runs out; it stays inactive until `~/.config/agents-council/config.json` exists. Existing settings are preserved.
+Both commands also patch the target's `.claude/settings.local.json` — the machine-local, untracked file, so a project's reviewed `.claude/settings.json` is never rewritten by an install: the caveman and ponytail plugins are enabled so their modes auto-activate on session start, and two reminder hooks are registered — `autotune` points you at `/autotune` at most once an hour, `dream` points you at `/dream` at most once every twelve hours per repository (stamped in that repository's git dir). A PreToolUse gate from the `democracy` skill blocks edits to files in a git worktree until the session holds a claim (`skills/democracy/scripts/council.py`), and its resource watch (`skills/democracy/scripts/watch.py`) measures disk, Docker and caches, cleans up low-risk data and blocks new heavy jobs before the disk runs out; it stays inactive until `~/.config/agents-council/config.json` exists. Two hooks of the `subagent-model` skill (`skills/subagent-model/scripts/log.py`) log every Agent spawn with its task class, model and effort, and store the quality feedback a sandboxed session prints, in `~/.local/state/agents-council/subagent-model.jsonl`. Existing settings are preserved.
 
 Restart your agent afterwards so it loads the new skills.
 

scripts/enable-plugins.js

@@ -1,7 +1,7 @@
 #!/usr/bin/env node
 // Enable the caveman and ponytail plugins in a Claude Code settings.json so
 // their SessionStart hooks auto-activate both modes, register the autotune
-// and dream reminder hooks, the democracy claim gate and the resource watch. Merges non-destructively: existing marketplaces,
+// and dream reminder hooks, the democracy claim gate, the resource watch and the subagent-model log. Merges non-destructively: existing marketplaces,
 // plugins and hooks are preserved, unparseable files are left untouched.
 "use strict";
 
@@ -60,21 +60,23 @@ if (!JSON.stringify(data.hooks.PreToolUse).includes("democracy/scripts/council.p
   });
 }
 
-const watch = path.join(path.dirname(settingsPath), "skills", "democracy", "scripts", "watch.py");
-const WATCH_HOOKS = [
-  ["UserPromptSubmit", null, "watch"],
-  ["PostToolUse", "Bash", "watch"],
-  ["PreToolUse", "Bash", "block"],
+const SCRIPT_HOOKS = [
+  ["democracy/scripts/watch.py", "UserPromptSubmit", null, "watch"],
+  ["democracy/scripts/watch.py", "PostToolUse", "Bash", "watch"],
+  ["democracy/scripts/watch.py", "PreToolUse", "Bash", "block"],
+  ["subagent-model/scripts/log.py", "PreToolUse", "Agent|Task", "spawn"],
+  ["subagent-model/scripts/log.py", "PostToolUse", "Bash", "persist"],
 ];
-for (const [event, matcher, sub] of WATCH_HOOKS) {
+for (const [script, event, matcher, sub] of SCRIPT_HOOKS) {
   data.hooks[event] = data.hooks[event] || [];
   const registered = data.hooks[event].some((entry) =>
     (entry.hooks || []).some((hook) =>
-      String(hook.command).includes("democracy/scripts/watch.py") && String(hook.command).endsWith(` ${sub}`)));
+      String(hook.command).includes(script) && String(hook.command).endsWith(` ${sub}`)));
   if (registered) continue;
-  const entry = { hooks: [{ type: "command", command: `python3 ${JSON.stringify(watch)} ${sub}` }] };
+  const file = path.join(path.dirname(settingsPath), "skills", ...script.split("/"));
+  const entry = { hooks: [{ type: "command", command: `python3 ${JSON.stringify(file)} ${sub}` }] };
   data.hooks[event].push(matcher ? { matcher, ...entry } : entry);
 }
 
 fs.writeFileSync(settingsPath, JSON.stringify(data, null, 2) + "\n");
-console.log(`skills: enabled caveman + ponytail plugins, the ${REMINDERS.join(" + ")} reminders, the democracy claim gate and resource watch in ${settingsPath}`);
+console.log(`skills: enabled caveman + ponytail plugins, the ${REMINDERS.join(" + ")} reminders, the democracy claim gate, the resource watch and the subagent-model log in ${settingsPath}`);

skills/dialectic/SKILL.md

@@ -30,7 +30,8 @@ asking; ask only when an operator-only fact blocks the conclusion.
    make or CLI helpers the project exposes); CI logs and artifacts; tests; docs.
    Cite `file:line` and command output — never a bare assertion.
 2. **Antithesis.** Spawn MULTIPLE independent skeptics (parallel subagents or a
-   workflow) whose only job is to REFUTE the thesis. Each re-inspects the
+   workflow, on the default model as the `subagent-model` skill requires)
+   whose only job is to REFUTE the thesis. Each re-inspects the
    sources itself and defaults to "refuted" on any concrete hole. Give diverse
    skeptics diverse lenses: correctness, an alternate mechanism, does-it-
    reproduce, does-the-fix-break-something-else.

skills/funnel/SKILL.md

@@ -20,7 +20,8 @@ in a sidetree until it does, or leaves as a proposal.
 
 ## The stages
 
-1. **Brief.** One concern per agent. Name the goal, the exact base commit, the
+1. **Brief.** One concern per agent, on the model the `subagent-model` skill
+   picks. Name the goal, the exact base commit, the
    scope that may change, the evidence expected back, a time cap (default
    60 minutes) at which it reports its interim state, and what the agent must
    not do: no pushes, no gates or deploys

skills/subagent-model/SKILL.md

@@ -0,0 +1,72 @@
+---
+name: subagent-model
+description: >
+  Pick `model` and `effort` per subagent from a fixed table: cheaper only on a
+  clear match at 90 % that the result equals the default's; log spawns and
+  quality. Apply AUTOMATICALLY before every Agent tool call.
+---
+
+A subagent runs on the main model and effort unless the spawn names others.
+Set `model` and `effort` on an Agent call only when the task clearly matches a
+row below and you are at least 90 % sure the cheaper setting delivers the same
+result as the default: no lost findings, no rework. Cost counts per finished
+task, retries included. Otherwise set neither, and the default stays.
+
+## Table
+
+| Task class | Subagent's task | `model` | `effort` | Evidence |
+| --- | --- | --- | --- | --- |
+| `lookup` | Read-only: find files, symbols or call sites, grep, list, read and quote, pull a log line or field, summarise a given text without judging it | `haiku` | `low` | Haiku 5.5 costs 1/40 of Opus 5.5 per token and is the speed tier; Anthropic names Haiku at low effort for simple, speed-critical and reading-heavy worker tasks; Explore and the cavecrew investigator already run on it |
+| `mechanical-edit` | An edit the brief fully specifies (exact old and new text, a rename, deleting listed lines) in one or two files | `haiku` | `low` | nothing is left to decide, and the parent checks the diff |
+| `judgement` | Everything else: root cause, skeptic or dialectic antithesis, review, security or legal checks, design, new or multi-file code, writing tests, performance work | omit | omit | no evidence at 90 % that a cheaper setting gives the same result |
+
+Prices per million tokens in and out (claude-api reference, cached
+2026-10-06): Haiku 5.5 $0.10 / $0.50, Sonnet 5.5 $2 / $10, Opus 5.5 $4 / $20,
+Fable 5.1 $10 / $50.
+
+## Spawn
+
+1. Pick the class. A task that mixes classes, or whose class you hesitate
+   over, is `judgement`.
+2. Start the brief with the line `task-class: <class>`; the spawn hook logs
+   it together with `model`, `effort` and the Agent `description`.
+3. Set `model` and `effort` from the row; for `judgement` set neither.
+
+- An agent type whose definition sets its own model (Explore, Plan, the
+  cavecrew agents) keeps it; never pass `model` or `effort` for it.
+- Never pass `fable` or `opus`: neither is cheaper than the default.
+- A Haiku agent that reports the task needs judgement, or returns an
+  incomplete result, is not retried on Haiku: the default continues it.
+- Never change the default itself (settings, `CLAUDE_CODE_SUBAGENT_MODEL`,
+  agent definitions); this skill only chooses per call.
+
+## Feedback
+
+Once you have judged the subagent's result, rate it under the same Agent
+`description`:
+
+```bash
+${CLAUDE_SKILL_DIR}/scripts/log.py feedback --description "<description>" --completed yes|no --tests pass|fail|none --redo yes|no
+```
+
+`--redo yes` when the default had to redo or finish the work. When the
+operator later asks a follow-up question about that result or corrects it:
+
+```bash
+${CLAUDE_SKILL_DIR}/scripts/log.py followup --description "<description>" --text "<question or correction>"
+```
+
+All sessions share `~/.local/state/agents-council/subagent-model.jsonl`.
+Inside a sandbox that cannot write it, the script prints the line as
+`pending:` and the PostToolUse hook stores it.
+
+## Evaluation
+
+`log.py report` shows per task class, model and effort the runs, the rated
+runs and the share with the same result: completed, not redone, no failing
+test, no follow-up. A row is added, or a cheaper setting extended to a class,
+only when the report shows at least 90 % over at least 20 rated runs, and only
+after the operator confirms it (for example through `/autotune`). A row whose
+rate falls below 90 % over 20 rated runs goes back to the default. `sonnet`
+enters the table only this way, and a trial run on a setting the table does
+not hold needs the operator's order.

skills/subagent-model/scripts/log.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+"""Log subagent spawns with their quality feedback, and evaluate the table.
+
+Usage:
+    log.py spawn      hook (PreToolUse on Agent): append the spawn line
+    log.py feedback --description D --completed yes|no --tests pass|fail|none --redo yes|no
+                      rate the latest spawn with this Agent description
+    log.py followup --description D --text T
+                      record an operator follow-up question or correction on it
+    log.py persist    hook (PostToolUse on Bash): append the one line a sandboxed
+                      feedback or followup printed as `pending: <json>`, only
+                      when the command was that bare call and the line is
+                      well-formed and from the hook's own session
+    log.py report     runs, rated runs and same-result rate per task class,
+                      model and effort; a row qualifies at >= 90 % over >= 20
+
+Environment:
+    SUBAGENT_MODEL_LOG: the log shared by every session;
+        ~/.local/state/agents-council/subagent-model.jsonl unless set.
+    CLAUDE_CODE_SESSION_ID: the session that rates its spawns.
+"""
+
+from __future__ import annotations
+
+import argparse
+import datetime
+import json
+import os
+import re
+import shlex
+import sys
+from pathlib import Path
+
+LOG = Path(
+    os.environ.get(
+        "SUBAGENT_MODEL_LOG",
+        str(Path.home() / ".local" / "state" / "agents-council" / "subagent-model.jsonl"),
+    )
+)
+TASK_CLASS = re.compile(r"^task-class:\s*(\S+)", re.MULTILINE)
+PENDING = "pending: "
+SCRIPT = "subagent-model/scripts/log.py"
+CONTROL = re.compile(r"^[();<>|&]+$")
+KEYS = {
+    "feedback": {"kind", "time", "session", "description", "completed", "tests", "redo"},
+    "followup": {"kind", "time", "session", "description", "text"},
+}
+QUALIFY_RUNS = 20
+QUALIFY_RATE = 0.9
+
+
+def now() -> str:
+    return datetime.datetime.now(datetime.UTC).isoformat(timespec="seconds")
+
+
+def append(line: dict) -> None:
+    LOG.parent.mkdir(parents=True, exist_ok=True)
+    with LOG.open("a", encoding="utf-8") as handle:
+        handle.write(json.dumps(line, sort_keys=True) + "\n")
+
+
+def record(line: dict) -> int:
+    try:
+        append(line)
+    except OSError:
+        print(PENDING + json.dumps(line, sort_keys=True))
+    return 0
+
+
+def cmd_spawn() -> int:
+    payload = json.load(sys.stdin)
+    tool = payload.get("tool_input") or {}
+    match = TASK_CLASS.search(tool.get("prompt", ""))
+    append({
+        "kind": "spawn",
+        "time": now(),
+        "session": payload.get("session_id", ""),
+        "description": tool.get("description", ""),
+        "subagent_type": tool.get("subagent_type") or "general-purpose",
+        "task_class": match.group(1) if match else "unclassified",
+        "model": tool.get("model") or "default",
+        "effort": tool.get("effort") or "default",
+    })
+    return 0
+
+
+def rated(kind: str, args: argparse.Namespace, **fields: object) -> dict:
+    session = os.environ.get("CLAUDE_CODE_SESSION_ID")
+    if not session:
+        sys.exit("log.py: CLAUDE_CODE_SESSION_ID is not set")
+    return {"kind": kind, "time": now(), "session": session, "description": args.description, **fields}
+
+
+def invoked_kind(command: str) -> str | None:
+    if "$(" in command or "`" in command:
+        return None
+    lexer = shlex.shlex(command, posix=True, punctuation_chars=True)
+    lexer.whitespace_split = True
+    try:
+        tokens = list(lexer)
+    except ValueError:
+        return None
+    if any(CONTROL.match(token) for token in tokens):
+        return None
+    if tokens[:1] in (["python3"], ["python"]):
+        tokens = tokens[1:]
+    if len(tokens) < 2 or not tokens[0].endswith(SCRIPT) or tokens[1] not in KEYS:
+        return None
+    return tokens[1]
+
+
+def well_formed(line: object, kind: str, session: str) -> bool:
+    if not isinstance(line, dict) or set(line) != KEYS[kind] or line["kind"] != kind or line["session"] != session:
+        return False
+    if not all(isinstance(line[key], str) for key in ("time", "description")):
+        return False
+    if kind == "feedback":
+        return isinstance(line["completed"], bool) and isinstance(line["redo"], bool) and line["tests"] in ("pass", "fail", "none")
+    return isinstance(line["text"], str)
+
+
+def cmd_persist() -> int:
+    payload = json.load(sys.stdin)
+    kind = invoked_kind((payload.get("tool_input") or {}).get("command", ""))
+    if not kind:
+        return 0
+    response = payload.get("tool_response")
+    stdout = response.get("stdout", "") if isinstance(response, dict) else str(response or "")
+    pending = [text[len(PENDING):] for text in stdout.splitlines() if text.startswith(PENDING)]
+    if len(pending) != 1:
+        return 0
+    try:
+        line = json.loads(pending[0])
+    except json.JSONDecodeError:
+        return 0
+    if well_formed(line, kind, payload.get("session_id", "")):
+        append(line)
+    return 0
+
+
+def runs() -> list[dict]:
+    current: dict[tuple[str, str], dict] = {}
+    found: list[dict] = []
+    for text in LOG.read_text(encoding="utf-8").splitlines() if LOG.exists() else []:
+        line = json.loads(text)
+        key = (line["session"], line["description"])
+        if line["kind"] == "spawn":
+            current[key] = {**line, "feedback": None, "followups": 0}
+            found.append(current[key])
+        elif key in current and line["kind"] == "feedback":
+            current[key]["feedback"] = line
+        elif key in current and line["kind"] == "followup":
+            current[key]["followups"] += 1
+    return found
+
+
+def same_result(run: dict) -> bool:
+    feedback = run["feedback"]
+    return feedback["completed"] and not feedback["redo"] and feedback["tests"] != "fail" and not run["followups"]
+
+
+def cmd_report() -> int:
+    rows: dict[tuple[str, str, str], list[dict]] = {}
+    for run in runs():
+        rows.setdefault((run["task_class"], run["model"], run["effort"]), []).append(run)
+    print("task_class\tmodel\teffort\truns\trated\tsame\trate\tqualifies")
+    for (task_class, model, effort), group in sorted(rows.items()):
+        rated_runs = [r for r in group if r["feedback"]]
+        same = sum(map(same_result, rated_runs))
+        rate = same / len(rated_runs) if rated_runs else 0.0
+        qualifies = len(rated_runs) >= QUALIFY_RUNS and rate >= QUALIFY_RATE
+        print(f"{task_class}\t{model}\t{effort}\t{len(group)}\t{len(rated_runs)}\t{same}\t{rate:.0%}\t{'yes' if qualifies else 'no'}")
+    return 0
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    sub = parser.add_subparsers(dest="command", required=True)
+    sub.add_parser("spawn")
+    feedback = sub.add_parser("feedback")
+    feedback.add_argument("--description", required=True)
+    feedback.add_argument("--completed", required=True, choices=("yes", "no"))
+    feedback.add_argument("--tests", required=True, choices=("pass", "fail", "none"))
+    feedback.add_argument("--redo", required=True, choices=("yes", "no"))
+    followup = sub.add_parser("followup")
+    followup.add_argument("--description", required=True)
+    followup.add_argument("--text", required=True)
+    sub.add_parser("persist")
+    sub.add_parser("report")
+    args = parser.parse_args()
+    if args.command == "spawn":
+        return cmd_spawn()
+    if args.command == "feedback":
+        return record(rated(
+            "feedback", args, completed=args.completed == "yes", tests=args.tests, redo=args.redo == "yes"
+        ))
+    if args.command == "followup":
+        return record(rated("followup", args, text=args.text))
+    if args.command == "persist":
+        return cmd_persist()
+    return cmd_report()
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tests/test_dream.py

@@ -132,6 +132,8 @@ class TestSettingsRegistration(unittest.TestCase):
         self.assertEqual(entries.count("democracy/scripts/watch.py"), 1)
         post = json.dumps(data["hooks"]["PostToolUse"])
         self.assertEqual(post.count("democracy/scripts/watch.py"), 1)
+        self.assertEqual(gate.count("subagent-model/scripts/log.py"), 1)
+        self.assertEqual(post.count("subagent-model/scripts/log.py"), 1)
 
 
 if __name__ == "__main__":

tests/test_subagent_model.py

@@ -0,0 +1,107 @@
+"""Exercise the subagent-model log: spawn hook, feedback, sandbox persist, report."""
+
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+LOG_PY = REPO_ROOT / "skills" / "subagent-model" / "scripts" / "log.py"
+
+
+class TestSubagentModelLog(unittest.TestCase):
+    def setUp(self):
+        self.tmp = tempfile.TemporaryDirectory()
+        self.addCleanup(self.tmp.cleanup)
+        self.base = Path(self.tmp.name)
+        self.log = self.base / "state" / "subagent-model.jsonl"
+
+    def _run(self, *args: str, stdin: str = "", log: Path | None = None) -> subprocess.CompletedProcess:
+        env = {"SUBAGENT_MODEL_LOG": str(log or self.log), "CLAUDE_CODE_SESSION_ID": "s1"}
+        return subprocess.run(
+            [sys.executable, str(LOG_PY), *args], capture_output=True, text=True, env=env, input=stdin, check=False
+        )
+
+    def _spawn(self, description: str, prompt: str, **tool: str) -> None:
+        payload = {"session_id": "s1", "tool_input": {"description": description, "prompt": prompt, **tool}}
+        self.assertEqual(self._run("spawn", stdin=json.dumps(payload)).returncode, 0)
+
+    def _feedback(self, description: str, completed: str = "yes", redo: str = "no", tests: str = "none") -> None:
+        result = self._run(
+            "feedback", "--description", description, "--completed", completed, "--tests", tests, "--redo", redo
+        )
+        self.assertEqual(result.returncode, 0, result.stderr)
+
+    def _lines(self) -> list[dict]:
+        return [json.loads(text) for text in self.log.read_text(encoding="utf-8").splitlines()]
+
+    def _report(self) -> dict[str, list[str]]:
+        rows = self._run("report").stdout.splitlines()[1:]
+        return {row.split("\t")[0]: row.split("\t") for row in rows}
+
+    def test_spawn_logs_class_model_and_effort_or_the_defaults(self):
+        self._spawn("find callers", "task-class: lookup\nFind every caller.", model="haiku", effort="low")
+        self._spawn("fix it", "Find the root cause.")
+        first, second = self._lines()
+        self.assertEqual((first["task_class"], first["model"], first["effort"]), ("lookup", "haiku", "low"))
+        self.assertEqual((second["task_class"], second["model"], second["effort"]), ("unclassified", "default", "default"))
+
+    def _pending_feedback(self) -> str:
+        blocked = self.base / "file"
+        blocked.write_text("", encoding="utf-8")
+        result = self._run(
+            "feedback", "--description", "d", "--completed", "yes", "--tests", "none", "--redo", "no",
+            log=blocked / "log.jsonl",
+        )
+        self.assertTrue(result.stdout.startswith("pending: "), result.stdout)
+        return result.stdout
+
+    def _persist(self, command: str, stdout: str, session: str = "s1") -> None:
+        payload = {"session_id": session, "tool_input": {"command": command}, "tool_response": {"stdout": stdout}}
+        self.assertEqual(self._run("persist", stdin=json.dumps(payload)).returncode, 0)
+
+    def test_sandboxed_feedback_is_printed_and_stored_by_the_persist_hook(self):
+        stdout = self._pending_feedback()
+        self._persist("python3 /x/skills/subagent-model/scripts/log.py feedback --description 'd'", stdout)
+        self.assertEqual(self._lines()[0]["kind"], "feedback")
+
+    def test_persist_rejects_pending_lines_it_did_not_get_from_a_bare_log_call(self):
+        stdout = self._pending_feedback()
+        line = json.loads(stdout[len("pending: "):])
+        call = "/x/skills/subagent-model/scripts/log.py feedback --description d"
+        for command, output, session in (
+            ("cat ci.log", stdout, "s1"),
+            (f"{call}; cat ci.log", stdout, "s1"),
+            (f"{call} | tee /tmp/x", stdout, "s1"),
+            (f"echo $({call})", stdout, "s1"),
+            (call, stdout, "other-session"),
+            (call, stdout + stdout, "s1"),
+            (call, "pending: " + json.dumps({**line, "extra": 1}), "s1"),
+            (call, "pending: " + json.dumps({**line, "completed": "yes"}), "s1"),
+            ("/x/skills/subagent-model/scripts/log.py followup --description d", stdout, "s1"),
+        ):
+            self._persist(command, output, session)
+        self.assertFalse(self.log.exists())
+
+    def test_report_qualifies_a_row_only_at_ninety_percent_over_twenty_rated_runs(self):
+        for i in range(20):
+            self._spawn(f"q{i}", "task-class: lookup", model="haiku", effort="low")
+            self._feedback(f"q{i}", redo="yes" if i < 2 else "no")
+        for i in range(19):
+            self._spawn(f"r{i}", "task-class: mechanical-edit", model="haiku", effort="low")
+            self._feedback(f"r{i}")
+        self._run("followup", "--description", "q5", "--text", "that list missed a caller")
+        report = self._report()
+        self.assertEqual(report["lookup"][3:], ["20", "20", "17", "85%", "no"])
+        self.assertEqual(report["mechanical-edit"][3:], ["19", "19", "19", "100%", "no"])
+        self._spawn("r19", "task-class: mechanical-edit", model="haiku", effort="low")
+        self._feedback("r19")
+        self.assertEqual(self._report()["mechanical-edit"][-1], "yes")
+
+
+if __name__ == "__main__":
+    unittest.main()